WO2021233018A1 - Method and apparatus for measuring muscle fatigue degree after exercise, and electronic device - Google Patents

Method and apparatus for measuring muscle fatigue degree after exercise, and electronic device Download PDF

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Publication number
WO2021233018A1
WO2021233018A1 PCT/CN2021/086963 CN2021086963W WO2021233018A1 WO 2021233018 A1 WO2021233018 A1 WO 2021233018A1 CN 2021086963 W CN2021086963 W CN 2021086963W WO 2021233018 A1 WO2021233018 A1 WO 2021233018A1
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WO
WIPO (PCT)
Prior art keywords
exercise
user
signal
difference
muscle fatigue
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PCT/CN2021/086963
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French (fr)
Chinese (zh)
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赵帅
杨斌
任慧超
李玥
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华为技术有限公司
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Publication of WO2021233018A1 publication Critical patent/WO2021233018A1/en

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/021—Measuring pressure in heart or blood vessels
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A61B5/02438—Detecting, measuring or recording pulse rate or heart rate with portable devices, e.g. worn by the patient
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • A61B5/0816—Measuring devices for examining respiratory frequency
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1118—Determining activity level
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/389—Electromyography [EMG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
    • A61B5/4519—Muscles
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6802—Sensor mounted on worn items
    • A61B5/681—Wristwatch-type devices
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01G—WEIGHING
    • G01G19/00—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups
    • G01G19/44—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for weighing persons
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B1/00—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission
    • H04B1/38—Transceivers, i.e. devices in which transmitter and receiver form a structural unit and in which at least one part is used for functions of transmitting and receiving
    • H04B1/40—Circuits
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/57—Mechanical or electrical details of cameras or camera modules specially adapted for being embedded in other devices

Definitions

  • This application relates to the technical field of intelligent detection equipment, and in particular to methods and devices for detecting muscle fatigue after exercise, and electronic equipment.
  • Muscle fatigue after exercise is an important indicator for evaluating exercise intensity and body recovery function after exercise.
  • the current main measurement methods are blood lactic acid method and surface electromyography method.
  • the blood lactic acid method uses blood lactic acid to detect muscle fatigue with high accuracy, but it needs to collect blood, which cannot be accepted by most groups;
  • the surface electromyography method uses surface electromyogram (Emg, Electroyogram) for evaluation, which can be measured by muscle mass.
  • Emg Electroyogram
  • Muscle fatigue but electrodes need to be attached to different muscle masses, which is cumbersome to operate and is not suitable for ordinary users to measure by themselves.
  • the embodiments of the present application provide a method, device, electronic equipment, weight measuring equipment, computer storage medium and computer program product for detecting muscle fatigue after exercise, which can detect the muscle fatigue of the user after exercise, and facilitate the user to check after exercise. Muscle fatigue is self-measured and user acceptance is high.
  • an embodiment of the present application provides a method for detecting muscle fatigue after exercise, which is applied to a device for detecting muscle fatigue after exercise, and the method includes:
  • body state parameters include body stability, heart rate, breathing frequency, and breathing intensity;
  • the muscle fatigue degree of the user after exercise is calculated according to the body state parameter of the user before exercise and the body state parameter of the user after exercise.
  • the acquiring the physical state parameters of the user before exercise includes:
  • the acquiring the physical state parameters of the user after exercise includes:
  • the weight measurement device is generated when the weight measurement is performed.
  • the acquiring the physical state parameters of the user before exercise includes:
  • the acquiring the physical state parameters of the user after exercise includes:
  • the method further includes:
  • the heart recovery index and the lung recovery index of the user are calculated according to the body state parameters of the user before exercise, the body state parameters of the user after exercise, and the exercise parameters of the user.
  • the calculation of the muscle fatigue of the user after exercise according to the physical state parameters of the user before exercise and the physical state parameters of the user after exercise includes:
  • the muscle fatigue degree of the user after exercise is calculated according to the physical state parameters of the user before exercise, the physical state parameters of the user after exercise, and the exercise parameters of the user.
  • the exercise parameter includes one or more of exercise type, exercise intensity, exercise duration, post-exercise duration, and exercise parameter credibility.
  • the determining the physical state parameters of the user before exercise according to the first pressure signal includes:
  • the first pressure signal is sequentially subjected to high-pass amplification processing and low-pass filtering processing to obtain a first signal, and the first signal is subjected to high-pass filtering processing to obtain a pre-exercise cardiac shock signal, according to the pre-exercise cardiac shock
  • the signal determines the heart rate before exercise, the waveform profile of the cardiac shock signal before exercise is determined according to the waveform change of the cardiac shock signal before exercise, and the exercise is calculated according to the peak feature points of the waveform contour of the cardiac shock signal before exercise
  • the pre-exercise respiration frequency and the pre-exercise respiration intensity calculated according to the peak-to-peak value of the waveform profile of the pre-exercise cardiac shock signal;
  • the determining the body state parameter of the user after exercise according to the second pressure signal includes:
  • the second pressure signal is sequentially subjected to high-pass amplification processing and low-pass filtering processing to obtain a third signal
  • the third signal is subjected to high-pass filtering processing to obtain a post-exercise cardiac shock signal, according to the post-exercise cardiac shock
  • the signal determines the heart rate after exercise, the waveform contour of the post-exercise cardiac shock signal is determined according to the waveform change of the post-exercise cardiac shock signal, and the exercise is calculated according to the peak feature points of the waveform contour of the post-exercise cardiac shock signal
  • the post-exercise respiration frequency and the post-exercise respiration intensity calculated according to the peak-to-peak value of the waveform profile of the post-exercise cardiac shock signal;
  • the calculation of the muscle fatigue of the user after exercise according to the physical state parameters of the user before exercise and the physical state parameters of the user after exercise includes:
  • a weighted average algorithm is used to calculate the user's muscle fatigue after exercise.
  • the user’s heart after exercise is calculated according to the user’s body state parameters before exercise, the user’s body state parameters after exercise, and the user’s exercise parameters.
  • Recovery index and lung recovery index including:
  • a weighted average algorithm is used to calculate the user's heart after exercise Recovery index and lung recovery index.
  • a neural network is used to calculate the user's muscle fatigue and heart rate. Recovery index and lung recovery index.
  • the method further includes:
  • an embodiment of the present application provides a device for detecting muscle fatigue after exercise, and the device includes:
  • the first acquisition module is used to acquire the body state parameters of the user before exercise and the body state parameters after exercise, wherein the body state parameters include body stability, heart rate, breathing rate, and breathing intensity; and
  • the first calculation module is configured to calculate the muscle fatigue of the user after exercise according to the physical state parameters of the user before exercise and the physical state parameters of the user after exercise.
  • the embodiment of the present invention obtains the body state parameters of the user before exercise and the body state parameters after exercise, and then calculates the user's body state parameters based on the body state parameters of the user before exercise and the body state parameters of the user after exercise.
  • the muscle fatigue after exercise does not need to collect the user's blood, the operation is simple, and the user acceptability is high.
  • the muscle fatigue detection can be realized by relying on the weight measurement equipment, without the need for special testing equipment.
  • the first acquisition module may include:
  • the first acquisition unit is configured to receive the first pressure signal generated by the weight measurement device, and determine the physical state parameter of the user before exercise according to the first pressure signal, wherein the first pressure signal is used by the weight measurement device Generated when the user uses the weight measurement device to perform weight measurement before the user exercises;
  • the second acquisition unit is configured to receive a second pressure signal generated by the weight measurement device, and determine the body state parameter of the user after exercise according to the second pressure signal, wherein the second pressure signal is generated by the weight measurement device It is generated when the user uses the weight measurement device to perform weight measurement after the user exercises.
  • the first acquisition module may include:
  • the first determining unit is configured to measure the weight of the user before the user exercises, generate a first pressure signal according to the pressure applied by the user, and determine according to the first pressure signal that the user is before the exercise Physical state parameters;
  • the second determining unit is configured to measure the weight of the user after the user exercises, generate a second pressure signal according to the pressure applied by the user, and determine that the user is after exercise according to the second pressure signal Physical state parameters.
  • the device may further include:
  • the second acquisition module is used to acquire the motion parameters corresponding to the user motion.
  • the second calculation module is configured to calculate the heart recovery index and lung recovery index of the user according to the physical state parameters of the user before exercise, the physical state parameters of the user after exercise, and the exercise parameters of the user.
  • the first calculation module may include:
  • the first calculation unit is configured to calculate the muscle fatigue of the user after exercise according to the physical state parameters of the user before exercise, the physical state parameters of the user after exercise, and the exercise parameters of the user.
  • the exercise parameter includes one or any more of exercise type, exercise intensity, exercise duration, post-exercise duration, and exercise parameter credibility.
  • the first acquiring module or the first determining module may include:
  • the first processing unit is configured to sequentially perform high-pass amplification processing and low-pass filtering processing on the first pressure signal to obtain a first signal, perform high-pass filtering processing on the first signal to obtain a pre-exercise cardiac shock signal, according to
  • the cardiac shock signal before exercise determines the heart rate before exercise, the waveform profile of the cardiac shock signal before exercise is determined according to the waveform change of the cardiac shock signal before exercise, and the waveform of the cardiac shock signal before exercise is determined. Calculating the pre-exercise respiration frequency with the peak feature points of the contour and calculating the pre-exercise respiration intensity according to the peak-to-peak value of the waveform contour of the pre-exercise cardiac shock signal; and
  • the second processing unit is configured to perform low-pass filter processing on the first signal to obtain a second signal, calculate the dominant frequency, peak-to-peak value, and standard deviation of the second signal, according to the dominant frequency of the second signal , Peak-to-peak value and standard deviation to calculate the body stability before exercise.
  • the second acquiring module or the second determining module may include:
  • the third processing unit is configured to sequentially perform high-pass amplification processing and low-pass filter processing on the second pressure signal to obtain a third signal, perform high-pass filter processing on the third signal to obtain a post-exercise cardiac shock signal, according to
  • the post-exercise cardiac shock signal determines the post-exercise heart rate
  • the waveform profile of the post-exercise cardiac shock signal is determined according to the waveform change of the post-exercise cardiac shock signal
  • the waveform profile of the post-exercise cardiac shock signal is determined according to the waveform of the post-exercise cardiac shock signal
  • the peak feature points of the contour calculate the post-exercise respiration frequency
  • the post-exercise respiration intensity is calculated according to the peak-to-peak value of the waveform contour of the post-exercise cardiac shock signal
  • the fourth processing unit is configured to perform low-pass filtering processing on the third signal to obtain a fourth signal, calculate the main frequency, peak-to-peak value and standard deviation of the fourth signal, and calculate the main frequency of the fourth signal according to the main frequency of the fourth signal. , Peak-to-peak value and standard deviation to calculate the body stability after exercise.
  • the first calculation module may include:
  • the second calculation unit is used to calculate the body stability difference between the body stability of the user before exercise and the body stability of the user after exercise, and to calculate the heart rate of the user before exercise and the user exercise
  • the third calculation unit is configured to calculate the muscle fatigue degree of the user after exercise by using a weighted average algorithm according to the difference in body stability, the difference in heart rate, the difference in respiratory frequency, and the difference in respiratory intensity.
  • the second calculation unit may further include:
  • the first calculation subunit is configured to adopt a weighted average algorithm according to the difference in body stability, the difference in heart rate, the difference in respiratory frequency, and the difference in respiratory intensity, in combination with the exercise parameters of the user Calculate the heart recovery index and lung recovery index of the user after exercise.
  • the first calculation unit may include:
  • the second calculation subunit is used to calculate the difference in body stability between the body stability of the user before exercise and the body stability of the user after exercise, and to calculate the difference between the user’s heart rate before exercise and the user’s
  • the heart rate difference between the heart rates after exercise, and the respiration rate difference between the respiration rate of the user before exercise and the respiration rate of the user after exercise, and the calculation of the respiration intensity of the user before exercise and the total The breathing intensity difference between the breathing intensity of the user after exercise;
  • the third calculation subunit is used to calculate the difference in body stability, the difference in heart rate, the difference in respiration rate, and the difference in respiration intensity, in combination with the exercise parameters of the user, using a neural network to calculate Describes the user’s muscle fatigue, heart recovery index and lung recovery index.
  • the device may further include:
  • the first display module is used to display any one or more of the calculated muscle fatigue, heart recovery index, and lung recovery index of the user, and according to the user’s muscle fatigue, heart recovery index, and lung recovery index.
  • One or any more of the recovery index generates and displays one or any more of exercise volume evaluation, physical function evaluation, exercise suggestion, and physical recovery suggestion.
  • an embodiment of the present application provides an electronic device that includes a memory, a processor, a touch sensor, and a display screen.
  • the memory stores a computer program
  • the processor is connected to the memory, and the The processor executes a computer program to implement the above-mentioned method for detecting muscle fatigue after exercise.
  • an embodiment of the present application provides a weight measurement device, the weight measurement device includes a memory, a processor, a touch sensor, and a display screen, the memory stores a computer program, and the processor is connected to the memory The processor executes the computer program to realize the above-mentioned method for detecting muscle fatigue after exercise.
  • an embodiment of the present application provides a computer-readable storage medium, including computer instructions, which, when the computer instructions run on an electronic device, cause the electronic device to perform the first aspect or any one of the first aspect.
  • the instruction of the method in the implementation mode is not limited to:
  • the embodiments of the present application provide a computer program product, which when the computer program product runs on a computer, causes the computer to execute the method in the first aspect or any possible implementation of the first aspect instruction.
  • the embodiment of the present invention obtains the body state parameters of the user before exercise and the body state parameters after exercise, and then calculates the user’s post-exercise based on the user’s body state parameters before exercise and the user’s body state parameters after exercise.
  • the muscle fatigue is not required to collect the user’s blood, the operation is simple, and the user’s acceptance is high.
  • the muscle fatigue detection can be achieved by relying on the weight measurement device without special detection equipment.
  • the detection method provided by the embodiment of the present invention is based on pressure Detection without changing the form of hardware products.
  • FIG. 1 is a schematic structural diagram of an electronic device provided by an embodiment of the application.
  • FIG. 2 is a block diagram of the software structure of an electronic device according to an embodiment of the present invention.
  • FIG. 3 is a flowchart of a method for detecting muscle fatigue after exercise provided by an embodiment of the present invention
  • FIG. 4 is an exemplary application framework diagram of a method for detecting muscle fatigue after exercise provided by an embodiment of the present invention
  • FIG. 5 is an exemplary detection flow chart of a user's body state parameter before exercise according to an embodiment of the present invention
  • FIG. 6 is an exemplary flow chart for detecting muscle fatigue, heart recovery index, and lung recovery index after exercise of a user according to an embodiment of the present invention
  • FIG. 7 is an exemplary schematic diagram of measuring a user's body state parameters before/after exercise according to an embodiment of the present invention.
  • FIG. 8 is a schematic diagram of calculating muscle fatigue, heart recovery index, and lung recovery index through neural network according to an embodiment of the present invention
  • FIG. 9 is a state selection interface before and after exercise of an exemplary device for detecting muscle fatigue after exercise provided by an embodiment of the present invention.
  • Fig. 10 is an exemplary setting interface for automatic acquisition of sports parameters provided by an embodiment of the present invention.
  • Fig. 11 is an exemplary manual input selection interface for sports parameters provided by an embodiment of the present invention.
  • Fig. 12 is an exemplary motion parameter input interface provided by an embodiment of the present invention.
  • Fig. 13 is yet another exemplary motion parameter input interface provided by the embodiment of the present invention.
  • FIG. 14 is an exemplary measurement result display interface provided by an embodiment of the present invention.
  • FIG. 15 is a schematic diagram of a device for detecting muscle fatigue after exercise provided by an embodiment of the application.
  • At least one refers to one or more, and “multiple” refers to two or more.
  • “And/or” describes the association relationship of the associated objects, indicating that there can be three relationships, for example, A and/or B, which can mean: A alone exists, A and B exist at the same time, and B exists alone, where A, B can be singular or plural.
  • the character “/” generally indicates that the associated objects before and after are in an “or” relationship.
  • the following at least one item (a)” or similar expressions refers to any combination of these items, including any combination of a single item (a) or a plurality of items (a).
  • at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple .
  • the method for detecting muscle fatigue of a user after exercise can be applied to the electronic device 100 shown in FIG. 1, and the electronic device 100 shown in FIG.
  • Portable electronic devices with player functions such as mobile phones, tablet computers, wearable devices with wireless communication functions (such as smart watches), etc.
  • the above-mentioned portable electronic devices are, for example, laptop computers with touch panels.
  • the electronic device 100 includes a straight-face display screen, a curved display screen, or a foldable display screen.
  • the electronic device 100 collects the touch points in a preset area when the user holds the electronic device 100, and uploads the touch points to the cloud server 200.
  • the cloud server 200 determines the user's holding posture according to the touch points, and feeds back the holding posture to the electronic device.
  • Equipment 100 In other embodiments, the electronic terminal 100 can also determine the user's holding posture based on the touch point, which is not limited herein.
  • the electronic device 100 is taken as an example to describe the embodiments in detail.
  • the electronic device 100 may include a processor 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, and an antenna 2.
  • Mobile communication module 150 wireless communication module 160, audio module 170, speaker 170A, receiver 170B, microphone 170C, earphone jack 170D, sensor module 180, buttons 190, motor 191, indicator 192, camera 193, display screen 194, and Subscriber identification module (subscriber identification module, SIM) card interface 195, etc.
  • SIM Subscriber identification module
  • the sensor module 180 may include a pressure sensor 180A, a gyroscope sensor 180B, an air pressure sensor 180C, a magnetic sensor 180D, an acceleration sensor 180E, a distance sensor 180F, a proximity sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, and ambient light Sensor 180L, bone conduction sensor 180M, etc.
  • the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the electronic device 100.
  • the electronic device 100 may include more or fewer components than those shown in the figure, or combine certain components, or split certain components, or arrange different components.
  • the illustrated components can be implemented in hardware, software, or a combination of software and hardware.
  • the processor 110 may include one or more processing units.
  • the processor 110 may include an application processor (AP), a modem processor, a graphics processing unit (GPU), and an image signal processor. (image signal processor, ISP), controller, video codec, digital signal processor (digital signal processor, DSP), baseband processor, and/or neural-network processing unit (NPU), etc.
  • AP application processor
  • modem processor modem processor
  • GPU graphics processing unit
  • image signal processor image signal processor
  • ISP image signal processor
  • controller video codec
  • digital signal processor digital signal processor
  • DSP digital signal processor
  • NPU neural-network processing unit
  • the different processing units may be independent devices or integrated in one or more processors.
  • the controller can generate operation control signals according to the instruction operation code and timing signals to complete the control of fetching instructions and executing instructions.
  • a memory may also be provided in the processor 110 for storing computer programs and data.
  • the memory in the processor 110 is a cache memory.
  • the memory can store instructions or data that have just been used or recycled by the processor 110. If the processor 110 needs to use the instruction or data again, it can be directly called from the memory. Repeated accesses are avoided, the waiting time of the processor 110 is reduced, and the efficiency of the system is improved.
  • the processor 110 may include one or more interfaces.
  • the interface can include an integrated circuit (inter-integrated circuit, I2C) interface, an integrated circuit built-in audio (inter-integrated circuit sound, I2S) interface, a pulse code modulation (pulse code modulation, PCM) interface, and a universal asynchronous transmitter (universal asyncHRonous) interface.
  • I2C integrated circuit
  • I2S integrated circuit built-in audio
  • PCM pulse code modulation
  • PCM pulse code modulation
  • UART universal asynchronous transmitter
  • receiver/transmitter UART
  • MIPI mobile industry processor interface
  • GPIO general-purpose input/output
  • SIM subscriber identity module
  • USB Universal Serial Bus
  • the I2C interface is a bidirectional synchronous serial bus, including a serial data line (SDA) and a serial clock line (SCL).
  • the processor 110 may include multiple sets of I2C buses.
  • the processor 110 may be coupled to the touch sensor 180K, charger, flash, camera 193, etc., respectively through different I2C bus interfaces.
  • the processor 110 may couple the touch sensor 180K through an I2C interface, so that the processor 110 and the touch sensor 180K communicate through the I2C bus interface to implement the touch function of the electronic device 100.
  • the I2S interface can be used for audio communication.
  • the processor 110 may include multiple sets of I2S buses.
  • the processor 110 may be coupled with the audio module 170 through an I2S bus to implement communication between the processor 110 and the audio module 170.
  • the audio module 170 may transmit audio signals to the wireless communication module 160 through an I2S interface, so as to realize the function of answering calls through a Bluetooth headset.
  • the PCM interface can also be used for audio communication to sample, quantize and encode analog signals.
  • the audio module 170 and the wireless communication module 160 may be coupled through a PCM bus interface.
  • the audio module 170 may also transmit audio signals to the wireless communication module 160 through the PCM interface, so as to realize the function of answering calls through the Bluetooth headset. Both the I2S interface and the PCM interface can be used for audio communication.
  • the UART interface is a universal serial data bus used for asynchronous communication.
  • the bus can be a two-way communication bus. It converts the data to be transmitted between serial communication and parallel communication.
  • the UART interface is generally used to connect the processor 110 and the wireless communication module 160.
  • the processor 110 communicates with the Bluetooth module in the wireless communication module 160 through the UART interface to realize the Bluetooth function.
  • the audio module 170 may transmit audio signals to the wireless communication module 160 through a UART interface, so as to realize the function of playing music through a Bluetooth headset.
  • the MIPI interface can be used to connect the processor 110 with the display screen 194, the camera 193 and other peripheral devices.
  • the MIPI interface includes a camera serial interface (camera serial interface, CSI), a display serial interface (display serial interface, DSI), and so on.
  • the processor 110 and the camera 193 communicate through a CSI interface to implement the shooting function of the electronic device 100.
  • the processor 110 and the display screen 194 communicate through a DSI interface to realize the display function of the electronic device 100.
  • the GPIO interface can be configured through software.
  • the GPIO interface can be configured as a control signal or as a data signal.
  • the GPIO interface can be used to connect the processor 110 with the camera 193, the display screen 194, the wireless communication module 160, the audio module 170, the sensor module 180, and so on.
  • the GPIO interface can also be configured as an I2C interface, I2S interface, UART interface, MIPI interface, etc.
  • the USB interface 130 is an interface that complies with the USB standard specification, and specifically may be a Mini USB interface, a Micro USB interface, a USB Type C interface, and so on.
  • the USB interface 130 can be used to connect a charger to charge the electronic device 100, and can also be used to transfer data between the electronic device 100 and peripheral devices. It can also be used to connect earphones and play audio through earphones. This interface can also be used to connect other electronic devices, such as AR devices.
  • the interface connection relationship between the modules illustrated in the embodiment of the present application is merely a schematic description, and does not constitute a structural limitation of the electronic device 100.
  • the electronic device 100 may also adopt different interface connection modes in the foregoing embodiments, or a combination of multiple interface connection modes.
  • the charging management module 140 is used to receive charging input from the charger.
  • the charger can be a wireless charger or a wired charger.
  • the charging management module 140 may receive the charging input of the wired charger through the USB interface 130.
  • the charging management module 140 may receive the wireless charging input through the wireless charging coil of the electronic device 100. While the charging management module 140 charges the battery 142, it can also supply power to the electronic device 100 through the power management module 141.
  • the power management module 141 is used to connect the battery 142, the charging management module 140 and the processor 110.
  • the power management module 141 receives input from the battery 142 and/or the charge management module 140, and supplies power to the processor 110, the internal memory 121, the display screen 194, the camera 193, and the wireless communication module 160.
  • the power management module 141 can also be used to monitor parameters such as battery capacity, battery cycle times, and battery health status (leakage, impedance).
  • the power management module 141 may also be provided in the processor 110.
  • the power management module 141 and the charging management module 140 may also be provided in the same device.
  • the wireless communication function of the electronic device 100 can be implemented by the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, the modem processor, and the baseband processor.
  • the antenna 1 and the antenna 2 are used to transmit and receive electromagnetic wave signals.
  • Each antenna in the electronic device 100 can be used to cover a single or multiple communication frequency bands. Different antennas can also be reused to improve antenna utilization.
  • Antenna 1 can be multiplexed as a diversity antenna of a wireless local area network.
  • the antenna can be used in combination with a tuning switch.
  • the mobile communication module 150 can provide a wireless communication solution including 2G/3G/4G/5G and the like applied to the electronic device 100.
  • the mobile communication module 150 may include at least one filter, a switch, a power amplifier, a low noise amplifier (LNA), and the like.
  • the mobile communication module 150 can receive electromagnetic waves by the antenna 1, and perform processing such as filtering, amplifying and transmitting the received electromagnetic waves to the modem processor for demodulation.
  • the mobile communication module 150 can also amplify the signal modulated by the modem processor, and convert it into electromagnetic waves for radiation via the antenna 1.
  • at least part of the functional modules of the mobile communication module 150 may be provided in the processor 110.
  • at least part of the functional modules of the mobile communication module 150 and at least part of the modules of the processor 110 may be provided in the same device.
  • the modem processor may include a modulator and a demodulator.
  • the modulator is used to modulate the low frequency baseband signal to be sent into a medium and high frequency signal.
  • the demodulator is used to demodulate the received electromagnetic wave signal into a low-frequency baseband signal.
  • the demodulator then transmits the demodulated low-frequency baseband signal to the baseband processor for processing.
  • the application processor outputs a sound signal through an audio device (not limited to the speaker 170A, the receiver 170B, etc.), or displays an image or video through the display screen 194.
  • the modem processor may be an independent device.
  • the modem processor may be independent of the processor 110 and be provided in the same device as the mobile communication module 150 or other functional modules.
  • the wireless communication module 160 can provide applications on the electronic device 100 including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), bluetooth (BT), and global navigation satellites.
  • WLAN wireless local area networks
  • BT wireless fidelity
  • GNSS global navigation satellites
  • frequency modulation frequency modulation, FM
  • NFC near field communication technology
  • infrared infrared
  • the wireless communication module 160 may be one or more devices integrating at least one communication processing module.
  • the wireless communication module 160 receives electromagnetic waves via the antenna 2, frequency modulates and filters the electromagnetic wave signals, and sends the processed signals to the processor 110.
  • the wireless communication module 160 may also receive a signal to be sent from the processor 110, perform frequency modulation, amplify, and convert it into electromagnetic waves to radiate through the antenna 2.
  • the antenna 1 of the electronic device 100 is coupled with the mobile communication module 150, and the antenna 2 is coupled with the wireless communication module 160, so that the electronic device 100 can communicate with the network and other devices through wireless communication technology.
  • the wireless communication technology may include global system for mobile communications (GSM), general packet radio service (GPRS), code division multiple access (CDMA), broadband Code division multiple access (wideBAnd code division multiple access, WCDMA), time-division code division multiple access (TD-SCDMA), long term evolution (LTE), BT, GNSS, WLAN, NFC , FM, and/or IR technology, etc.
  • the GNSS may include global satellite positioning system (gloBAl positioning system, GPS), global navigation satellite system (gloBAl navigation satellite system, GLONASS), Beidou navigation satellite system (BDS), quasi-zenith satellite system (quasi -zenith satellite system, QZSS) and/or satellite BAsed augmentation systems (SBAS).
  • GloBAl positioning system GPS
  • GLONASS global navigation satellite system
  • BDS Beidou navigation satellite system
  • QZSS quasi-zenith satellite system
  • SBAS satellite BAsed augmentation systems
  • the electronic device 100 implements a display function through a GPU, a display screen 194, an application processor, and the like.
  • the GPU is an image processing microprocessor, which is connected to the display screen 194 and the application processor.
  • the GPU is used to perform mathematical and geometric calculations and is used for graphics rendering.
  • the processor 110 may include one or more GPUs that execute program instructions to generate or change display information.
  • the display screen 194 is used to display images, videos, and the like.
  • the display screen 194 includes a display panel.
  • the display panel can use liquid crystal display (LCD), organic light-emitting diode (OLED), active matrix organic light-emitting diode or active-matrix organic light-emitting diode (active-matrix organic light-emitting diode).
  • LCD liquid crystal display
  • OLED organic light-emitting diode
  • active-matrix organic light-emitting diode active-matrix organic light-emitting diode
  • AMOLED flexible light-emitting diode (FLED), Miniled, MicroLed, Micro-oLed, quantum dot light-emitting diode (QLED), etc.
  • the electronic device 100 may include one or N display screens 194, and N is a positive integer greater than one.
  • the display screen 194 is a curved display screen or a foldable display screen.
  • the electronic device 100 can implement a shooting function through an ISP, a camera 193, a video codec, a GPU, a display screen 194, and an application processor.
  • the ISP is used to process the data fed back from the camera 193. For example, when taking a picture, the shutter is opened, the light is transmitted to the photosensitive element of the camera through the lens, the light signal is converted into an electrical signal, and the photosensitive element of the camera transmits the electrical signal to the ISP for processing and is converted into an image visible to the naked eye.
  • ISP can also optimize the image noise, brightness, and skin color. ISP can also optimize the exposure, color temperature and other parameters of the shooting scene.
  • the ISP may be provided in the camera 193.
  • the camera 193 is used to capture still images or videos.
  • the object generates an optical image through the lens and is projected to the photosensitive element.
  • the photosensitive element may be a charge coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor.
  • CMOS complementary metal-oxide-semiconductor
  • the photosensitive element converts the optical signal into an electrical signal, and then transfers the electrical signal to the ISP to convert it into a digital image signal.
  • ISP outputs digital image signals to DSP for processing.
  • DSP converts digital image signals into standard RGB, YUV and other formats of image signals.
  • the electronic device 100 may include one or N cameras 193, and N is a positive integer greater than one.
  • Digital signal processors are used to process digital signals. In addition to digital image signals, they can also process other digital signals. For example, when the electronic device 100 selects the frequency point, the digital signal processor is used to perform Fourier transform on the energy of the frequency point.
  • Video codecs are used to compress or decompress digital video.
  • the electronic device 100 may support one or more video codecs. In this way, the electronic device 100 can play or record videos in multiple encoding formats, such as: moving picture experts group (MPEG) 1, MPEG2, MPEG3, MPEG4, and so on.
  • MPEG moving picture experts group
  • MPEG2 MPEG2, MPEG3, MPEG4, and so on.
  • NPU is a neural-network (NN) computing processor.
  • NN neural-network
  • applications such as intelligent cognition of the electronic device 100 can be realized, such as image recognition, face recognition, voice recognition, text understanding, and so on.
  • the external memory interface 120 may be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the electronic device 100.
  • the external memory card communicates with the processor 110 through the external memory interface 120 to realize the data storage function. For example, save music, video and other files in an external memory card.
  • the internal memory 121 may be used to store computer executable program code, where the executable program code includes instructions.
  • the internal memory 121 may include a storage program area and a storage data area.
  • the storage program area can store an operating system, an application program (such as a sound playback function, an image playback function, etc.) required by at least one function, and the like.
  • the data storage area can store data (such as audio data, phone book, etc.) created during the use of the electronic device 100.
  • the internal memory 121 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, a universal flash storage (UFS), and the like.
  • the processor 110 executes various functional applications and data processing of the electronic device 100 by running instructions stored in the internal memory 121 and/or instructions stored in a memory provided in the processor.
  • the electronic device 100 can implement audio functions through the audio module 170, the speaker 170A, the receiver 170B, the microphone 170C, the earphone interface 170D, and the application processor. For example, music playback, recording, etc.
  • the audio module 170 is used to convert digital audio information into an analog audio signal for output, and is also used to convert an analog audio input into a digital audio signal.
  • the audio module 170 can also be used to encode and decode audio signals.
  • the audio module 170 may be provided in the processor 110, or part of the functional modules of the audio module 170 may be provided in the processor 110.
  • the speaker 170A also called “speaker” is used to convert audio electrical signals into sound signals.
  • the electronic device 100 can listen to music through the speaker 170A, or listen to a hands-free call.
  • the receiver 170B also called “earpiece” is used to convert audio electrical signals into sound signals.
  • the electronic device 100 answers a call or voice message, it can receive the voice by bringing the receiver 170B close to the human ear.
  • the microphone 170C also called “microphone”, “microphone”, is used to convert sound signals into electrical signals.
  • the user can make a sound by approaching the microphone 170C through the human mouth, and input the sound signal into the microphone 170C.
  • the electronic device 100 may be provided with at least one microphone 170C. In other embodiments, the electronic device 100 may be provided with two microphones 170C, which can implement noise reduction functions in addition to collecting sound signals. In other embodiments, the electronic device 100 may also be provided with three, four or more microphones 170C to collect sound signals, reduce noise, identify sound sources, and realize directional recording functions.
  • the earphone interface 170D is used to connect wired earphones.
  • the earphone interface 170D may be a USB interface 130, or a 3.5mm open mobile terminal platform (OMTP) standard interface, and a cellular telecommunications industry association of the USA (CTIA) standard interface.
  • OMTP open mobile terminal platform
  • CTIA cellular telecommunications industry association of the USA
  • the pressure sensor 180A is used to sense the pressure signal and can convert the pressure signal into an electrical signal.
  • the pressure sensor 180A may be provided on the display screen 194.
  • the capacitive pressure sensor may include at least two parallel plates with conductive materials.
  • the electronic device 100 determines the intensity of the pressure according to the change in capacitance.
  • the electronic device 100 detects the intensity of the touch operation according to the pressure sensor 180A.
  • the electronic device 100 may also calculate the touched position according to the detection signal of the pressure sensor 180A.
  • touch operations that act on the same touch position but have different touch operation strengths may correspond to different operation instructions. For example: when a touch operation whose intensity of the touch operation is less than the first pressure threshold is applied to the short message application icon, an instruction to view the short message is executed. When a touch operation with a touch operation intensity greater than or equal to the first pressure threshold acts on the short message application icon, an instruction to create a new short message is executed.
  • the gyro sensor 180B may be used to determine the movement posture of the electronic device 100.
  • the angular velocity of the electronic device 100 around three axes ie, x, y, and z axes
  • the gyro sensor 180B can be used for image stabilization.
  • the gyro sensor 180B detects the shake angle of the electronic device 100, calculates the distance that the lens module needs to compensate according to the angle, and allows the lens to counteract the shake of the electronic device 100 through reverse movement to achieve anti-shake.
  • the gyro sensor 180B can also be used for navigation and somatosensory game scenes.
  • the air pressure sensor 180C is used to measure air pressure.
  • the electronic device 100 calculates the altitude based on the air pressure value measured by the air pressure sensor 180C to assist positioning and navigation.
  • the magnetic sensor 180D includes a Hall sensor.
  • the electronic device 100 may use the magnetic sensor 180D to detect the opening and closing of the flip holster.
  • the electronic device 100 can detect the opening and closing of the flip according to the magnetic sensor 180D.
  • features such as automatic unlocking of the flip cover are set.
  • the acceleration sensor 180E can detect the magnitude of the acceleration of the electronic device 100 in various directions (generally three axes). When the electronic device 100 is stationary, the magnitude and direction of gravity can be detected. It can also be used to identify the posture of the electronic device 100, and be used in applications such as horizontal and vertical screen switching, pedometers, and the like.
  • the electronic device 100 can measure the distance by infrared or laser. In some embodiments, when shooting a scene, the electronic device 100 may use the distance sensor 180F to measure the distance to achieve fast focusing.
  • the proximity light sensor 180G may include, for example, a light emitting diode (LED) and a light detector such as a photodiode.
  • the light emitting diode may be an infrared light emitting diode.
  • the electronic device 100 emits infrared light to the outside through the light emitting diode.
  • the electronic device 100 uses a photodiode to detect infrared reflected light from nearby objects. When sufficient reflected light is detected, it can be determined that there is an object near the electronic device 100. When insufficient reflected light is detected, the electronic device 100 can determine that there is no object near the electronic device 100.
  • the electronic device 100 can use the proximity light sensor 180G to detect that the user holds the electronic device 100 close to the ear to talk, so as to automatically turn off the screen to save power.
  • the proximity light sensor 180G can also be used in leather case mode, and the pocket mode will automatically unlock and lock the screen.
  • the ambient light sensor 180L is used to sense the brightness of the ambient light.
  • the electronic device 100 can adaptively adjust the brightness of the display screen 194 according to the perceived brightness of the ambient light.
  • the ambient light sensor 180L can also be used to automatically adjust the white balance when taking pictures.
  • the ambient light sensor 180L can also cooperate with the proximity light sensor 180G to detect whether the electronic device 100 is in the pocket to prevent accidental touch.
  • the fingerprint sensor 180H is used to collect fingerprints.
  • the electronic device 100 can use the collected fingerprint characteristics to implement fingerprint unlocking, access application locks, fingerprint photographs, fingerprint answering calls, and so on.
  • the temperature sensor 180J is used to detect temperature.
  • the electronic device 100 uses the temperature detected by the temperature sensor 180J to execute a temperature processing strategy. For example, when the temperature reported by the temperature sensor 180J exceeds a threshold value, the electronic device 100 executes to reduce the performance of the processor located near the temperature sensor 180J, so as to reduce power consumption and implement thermal protection.
  • the electronic device 100 when the temperature is lower than another threshold, the electronic device 100 heats the battery 142 to avoid abnormal shutdown of the electronic device 100 due to low temperature.
  • the electronic device 100 boosts the output voltage of the battery 142 to avoid abnormal shutdown caused by low temperature.
  • Touch sensor 180K also called “touch panel”.
  • the touch sensor 180K may be disposed on the display screen 194, and the touch screen is composed of the touch sensor 180K and the display screen 194, which is also called a “touch screen”.
  • the touch sensor 180K is used to detect touch operations acting on or near it.
  • the touch sensor can pass the detected touch operation to the application processor to determine the type of touch event.
  • the visual output related to the touch operation can be provided through the display screen 194.
  • the touch sensor 180K may also be disposed on the surface of the electronic device 100, which is different from the position of the display screen 194.
  • the bone conduction sensor 180M can acquire vibration signals.
  • the bone conduction sensor 180M can obtain the vibration signal of the vibrating bone mass of the human voice.
  • the bone conduction sensor 180M can also contact the human pulse and receive the blood pressure pulse signal.
  • the bone conduction sensor 180M may also be provided in the earphone, combined with the bone conduction earphone.
  • the audio module 170 can parse the voice signal based on the vibration signal of the vibrating bone block of the voice obtained by the bone conduction sensor 180M, and realize the voice function.
  • the application processor can analyze the heart rate information based on the blood pressure beating signal obtained by the bone conduction sensor 180M, and realize the heart rate detection function.
  • the button 190 includes a power-on button, a volume button, and so on.
  • the button 190 may be a mechanical button. It can also be a touch button.
  • the electronic device 100 may receive key input, and generate key signal input related to user settings and function control of the electronic device 100.
  • the motor 191 can generate vibration prompts.
  • the motor 191 can be used for incoming call vibration notification, and can also be used for touch vibration feedback.
  • touch operations applied to different applications can correspond to different vibration feedback effects.
  • Acting on touch operations in different areas of the display screen 194, the motor 191 can also correspond to different vibration feedback effects.
  • Different application scenarios for example: time reminding, receiving information, alarm clock, games, etc.
  • the touch vibration feedback effect can also support customization.
  • the indicator 192 may be an indicator light, which may be used to indicate the charging status, power change, or to indicate messages, missed calls, notifications, and so on.
  • the SIM card interface 195 is used to connect to the SIM card.
  • the SIM card can be inserted into the SIM card interface 195 or pulled out from the SIM card interface 195 to achieve contact and separation with the electronic device 100.
  • the electronic device 100 may support 1 or N SIM card interfaces, and N is a positive integer greater than 1.
  • the SIM card interface 195 can support Nano SIM cards, Micro SIM cards, SIM cards, etc.
  • the same SIM card interface 195 can insert multiple cards at the same time. The types of the multiple cards can be the same or different.
  • the SIM card interface 195 can also be compatible with different types of SIM cards.
  • the SIM card interface 195 may also be compatible with external memory cards.
  • the electronic device 100 interacts with the network through the SIM card to implement functions such as call and data communication.
  • the electronic device 100 adopts an eSIM, that is, an embedded SIM card.
  • the eSIM card can be embedded in the electronic device 100 and cannot be separated from the electronic device 100.
  • the software system of the electronic device 100 may adopt a layered architecture, an event-driven architecture, a microkernel architecture, a microservice architecture, or a cloud architecture.
  • the embodiment of the present invention takes an Android system with a layered architecture as an example to illustrate the software structure of the electronic device 100.
  • FIG. 2 is a block diagram of the software structure of the electronic device 100 according to an embodiment of the present invention.
  • the layered architecture divides the software into several layers, and each layer has a clear role and division of labor. Communication between layers through software interface.
  • the Android system is divided into four layers, from top to bottom, the application layer, the application framework layer, the Android runtime and system library, and the kernel layer.
  • the application layer can include a series of application packages.
  • the application package may include applications such as camera, gallery, calendar, call, map, navigation, WLAN, Bluetooth, music, video, short message, etc.
  • the application framework layer provides an application programming interface (application programming interface, API) and a programming framework for applications in the application layer.
  • the application framework layer includes some predefined functions.
  • the application framework layer can include a window manager, a content provider, a view system, a phone manager, a resource manager, and a notification manager.
  • the window manager is used to manage window programs.
  • the window manager can obtain the size of the display screen, determine whether there is a status bar, lock the screen, take a screenshot, etc.
  • the content provider is used to store and retrieve data and make these data accessible to applications.
  • the data may include videos, images, audios, phone calls made and received, browsing history and bookmarks, phone book, etc.
  • the view system includes visual controls, such as controls that display text, controls that display pictures, and so on.
  • the view system can be used to build applications.
  • the display interface can be composed of one or more views.
  • a display interface that includes a short message notification icon may include a view that displays text and a view that displays pictures.
  • the phone manager is used to provide the communication function of the electronic device 100. For example, the management of the call status (including connecting, hanging up, etc.).
  • the resource manager provides various resources for the application, such as localized strings, icons, pictures, layout files, video files, and so on.
  • the notification manager enables the application to display notification information in the status bar, which can be used to convey notification-type messages, and it can automatically disappear after a short stay without user interaction.
  • the notification manager is used to notify download completion, message reminders, and so on.
  • the notification manager can also be a notification that appears in the status bar at the top of the system in the form of a chart or a scroll bar text, such as a notification of an application running in the background, or a notification that appears on the screen in the form of a dialog window. For example, text messages are prompted in the status bar, prompt sounds, electronic devices vibrate, and indicator lights flash.
  • Android Runtime includes core libraries and virtual machines. Android runtime is responsible for the scheduling and management of the Android system.
  • the core library consists of two parts: one part is the function functions that the java language needs to call, and the other part is the core library of Android.
  • the application layer and application framework layer run in a virtual machine.
  • the virtual machine executes the java files of the application layer and the application framework layer as binary files.
  • the virtual machine is used to perform functions such as object life cycle management, stack management, thread management, security and exception management, and garbage collection.
  • the system library can include multiple functional modules. For example: surface manager (surface manager), media library (Media LiBraries), 3D graphics processing library (for example: OpenGL ES), 2D graphics engine (for example: SGL), etc.
  • surface manager surface manager
  • media library Media LiBraries
  • 3D graphics processing library for example: OpenGL ES
  • 2D graphics engine for example: SGL
  • the surface manager is used to manage the display subsystem and provides a combination of 2D and 3D layers for multiple applications.
  • the media library supports playback and recording of a variety of commonly used audio and video formats, as well as still image files.
  • the media library can support multiple audio and video encoding formats, such as: MPEG4, H.264, MP3, AAC, AMR, JPG, PNG, etc.
  • the 3D graphics processing library is used to implement 3D graphics drawing, image rendering, synthesis, and layer processing.
  • the 2D graphics engine is a drawing engine for 2D drawing.
  • the kernel layer is the layer between hardware and software.
  • the kernel layer contains at least display driver, camera driver, audio driver, and sensor driver.
  • the corresponding hardware interrupt is sent to the kernel layer.
  • the kernel layer processes touch operations into original input events (including touch coordinates, time stamps of touch operations, etc.).
  • the original input events are stored in the kernel layer.
  • the application framework layer obtains the original input event from the kernel layer and identifies the control corresponding to the input event. Taking the touch operation as a touch click operation, and the control corresponding to the click operation is the control of the camera application icon as an example, the camera application calls the interface of the application framework layer to start the camera application, and then starts the camera driver by calling the kernel layer.
  • the camera 193 captures still images or videos.
  • Exercise muscle fatigue refers to the inability of the body's physiological processes to continue its function at a certain level or the inability to maintain a predetermined exercise intensity.
  • the detection methods of muscle fatigue after exercise mainly include: blood lactate detection method and surface electromyography (EMG) detection method.
  • the blood lactic acid detection method requires blood collection and cannot be accepted by most user groups, while the surface electromyography measurement method requires multiple electrodes to be attached, which is cumbersome to operate and is not suitable for users to measure by themselves.
  • the present invention provides a A method for detecting muscle fatigue after exercise is convenient for users to measure by themselves and has high user acceptance, and does not require special testing equipment.
  • FIG. 3 is a flowchart of a method for detecting muscle fatigue after exercise provided by an embodiment of the present invention
  • a method for detecting muscle fatigue after exercise can be applied to a device for detecting muscle fatigue after exercise.
  • the detecting device may be the above-mentioned electronic device 100 (such as a mobile phone) or a weight measuring device (such as a weight scale or Body fat scale or other equipment with weight measurement function), of course, can also be other electronic equipment, as shown in Figure 3, the method includes:
  • Step S11 Obtain the body state parameters of the user before exercise and the body state parameters after exercise.
  • Step S12 Calculate the user's muscle fatigue after exercise according to the user's body state parameters before exercise and the user's body state parameters after exercise.
  • the embodiment of the present invention obtains the user's body state parameters before exercise and post-exercise body state parameters, and then calculates the user's muscle fatigue after exercise according to the user's body state parameters before exercise and the user's body state parameters after exercise. , No need to collect the user's blood, simple operation, high user acceptance, and at the same time, the muscle fatigue detection can be achieved by relying on the weight measurement equipment, without the need for special testing equipment.
  • step S11 and step S12 may be executed by a terminal device (for example, the electronic device 100), or may be executed by a weight measurement device (for example, a weight scale).
  • a terminal device for example, the electronic device 100
  • a weight measurement device for example, a weight scale
  • the terminal device for example, the electronic device 100
  • the terminal device is in communication connection with the weight measurement device
  • the weight measurement device measures the pressure signal applied by the user before and after the exercise based on the stress test
  • the pressure signal is sent to the terminal device
  • the terminal device separately determines the user's body state parameters before and after the exercise according to the pressure signal, thereby calculating the muscle fatigue and displaying it to the user.
  • the weight measurement device can also process the pressure signal measured by itself to obtain the user's body state parameters before and after exercise, and then calculate the user's body state parameters before and after exercise.
  • the body state parameters are sent to the terminal device, and the terminal device calculates the degree of muscle fatigue based on the body state parameters before and after exercise.
  • the embodiment of the present invention refers to the pre-exercise pressure signal as the "first pressure signal” and the post-exercise pressure signal as the "second pressure signal”. ".
  • the user's state is divided into pre-exercise, in-exercise and post-exercise.
  • pre-exercise in-exercise
  • post-exercise if the user performs weight measurement before exercise, if there are historical body state parameters before exercise (at rest), then The body state parameters obtained in this measurement and the body state parameters before exercise (at rest) can be weighted and averaged to obtain the new body state parameters before exercise (at rest). Of course, in some embodiments, the weighted average calculation may not be performed.
  • the latest pre-exercise (resting state) body state parameter is directly used as the pre-exercise (resting state).
  • the state of the user can be determined as pre-exercise or post-exercise according to the state selected by the user during weight measurement (before exercise/after exercise).
  • a weight measurement device such as a weight scale
  • Each step of the fatigue detection method is executed by the weight measurement equipment;
  • the detection method provided by the embodiment of the present invention is based on pressure detection, and there is no need to change the form of the hardware product.
  • acquiring the user's physical state parameters before exercise includes:
  • Obtain the physical state parameters of the user after exercise including:
  • the second pressure signal generated by the weight measurement device is received, and the body state parameter of the user after exercise is determined according to the second pressure signal, wherein the second pressure signal is generated by the weight measurement device when the user uses the weight measurement device to perform weight measurement after exercise.
  • acquiring the user's physical state parameters before exercise includes:
  • Obtain the physical state parameters of the user after exercise including:
  • the weight of the user is measured, the first pressure signal is generated according to the pressure applied by the user, and the physical state parameters of the user after the exercise are determined according to the first pressure signal.
  • the body state parameters of the user before exercise and the body state parameters after exercise generated by the weight measurement device are received, wherein the body state parameters of the user before exercise are determined by the weight measurement device.
  • the measured first pressure signal is generated, and the body state parameters of the user after exercise are generated by the weight measurement device according to the measured second pressure signal.
  • the physical state parameters of the user before exercise include, but are not limited to: one or any more of body stability BA1, heart rate HR1, respiratory rate BR1, and respiratory intensity BI1.
  • the physical state parameters of the user after exercise include, but are not limited to, one or any more of body stability BA2, heart rate HR2, breathing rate BR2, and breathing intensity BI2.
  • receiving the first pressure signal generated by the weight measurement device further includes:
  • the user's heart recovery index RH and lung recovery index RL are calculated according to the user's body state parameters before exercise, the user's body state parameters after exercise, and the user's exercise parameters.
  • calculating the user's muscle fatigue after exercise according to the user's body state parameters before exercise and the user's body state parameters after exercise may include:
  • the user's muscle fatigue degree FM after exercise is calculated according to the user's body state parameters before exercise, the user's body state parameters after exercise, and the user's exercise parameters.
  • the embodiment of the present invention can calculate the user's muscle fatigue FM in combination with the user's exercise parameters. Of course, it is also possible to calculate the user's muscle fatigue FM without combining the user's exercise parameters.
  • the embodiment of the present invention can also be combined with the user's exercise parameters. Calculate the user's heart recovery index RH and/or lung recovery index RL with the user's body state parameters before exercise and the user's body state parameters after exercise.
  • the exercise parameter includes one or any more of exercise type s, exercise intensity m, exercise duration t, post-exercise time T, and exercise parameter credibility w.
  • the method for acquiring the exercise parameter may include: the exercise health App (Application, application) of the device that executes the method for detecting muscle fatigue after exercise in FIG. 3 or the exercise worn by the user Obtained from smart devices such as watches and sports bracelets.
  • the exercise health App Application, application
  • the sports and health app of the corresponding device can learn the related exercise parameters of the user's exercise, such as exercise type s, exercise intensity m, exercise duration t, One or more of the post-exercise time T, etc.
  • the device can automatically obtain the user’s exercise type, exercise intensity, exercise duration, post-exercise duration, etc. according to the user’s actions,
  • the user can also be input by the user into a sports health app or a sports watch or a sports bracelet, and then the device that executes the method for detecting muscle fatigue after exercise in Figure 3 above obtains the relevant exercise parameters from the related sports health app.
  • the specific implementation process of the sports health app that can learn the user's related sports parameters.
  • the prior art has been widely used, and the embodiments of the present invention will not repeat them one by one.
  • the relevant exercise parameters can also be obtained through manual input by the user, or some exercise parameters are obtained from the sports health app, and some exercise parameters are obtained through manual input by the user.
  • all exercise parameters or Part of the motion parameters may also be unnecessary.
  • all the motion parameters or some of the motion parameters may be empty, which is not specifically limited in the present invention.
  • the motion parameter may also include the reliability w of the motion parameter.
  • the determination method of the motion parameter credibility w can be based on actual conditions. Flexible settings are required.
  • the user’s muscle fatigue FM, heart recovery index RH, and lung recovery index after exercise are calculated according to the user’s body state parameters before exercise, the user’s body state parameter calculations after exercise, and/or the user’s exercise parameters.
  • RL, methods for calculating the user's muscle fatigue FM, heart recovery index RH, and lung recovery index RL after exercise include but are not limited to methods such as weighted average algorithm and neural network.
  • the present invention may further include: displaying the calculated user’s muscle fatigue FM, heart Any one or more of the recovery index RH and the lung recovery index RL, and any one or more of the user’s muscle fatigue, heart recovery index, and lung recovery index generate exercise volume assessment and/or physical function assessment and / Or exercise suggestion and/or body recovery suggestion and display the above exercise volume assessment and/or physical function assessment and/or exercise suggestion and/or body recovery suggestion to the user.
  • the above specific implementation process will be described in detail below.
  • the embodiment of the present invention displays the detection result to the user, and provides exercise volume assessment and/or physical function assessment and/or exercise suggestion and/or physical recovery suggestion, which facilitates the user to understand the current physical state through human-computer interaction. At the same time, it provides users with guidance on exercise and physical recovery, which can prevent users from performing exercise training with unreasonable intensity and time and guide users to quickly restore physical functions.
  • FIG. 4 is an exemplary application framework diagram of a method for detecting muscle fatigue after exercise provided by an embodiment of the present invention
  • the user's calculation of muscle fatigue, heart recovery index, and lung recovery index may specifically include:
  • Step (1) Before the user exercises (at rest), the weight measurement device (such as a weight scale, or a device with a weight detection function such as a body fat scale) detects the user’s pressure on the weight scale and generates a pressure signal (ie The above-mentioned first pressure signal), the weight measuring device or the terminal device calculates the body stability BA1, the heart rate HR1, the breathing rate BR1, and the breathing intensity BI1 according to the first pressure signal as the body state parameters of the user in the resting state;
  • the weight measurement device such as a weight scale, or a device with a weight detection function such as a body fat scale
  • Step (2) After the user exercises, the weight measurement device (for example, a weight scale, or a device with a weight detection function such as a body fat scale) detects the pressure of the user on the weight scale, and generates the above-mentioned pressure signal (that is, the above-mentioned second Pressure signal), the weight measuring device or terminal device calculates the body stability BA2, the heart rate HR2, the breathing rate BR2, and the breathing intensity BI2 according to the second pressure signal as the physical state parameters of the user after exercise;
  • the weight measurement device for example, a weight scale, or a device with a weight detection function such as a body fat scale
  • the acquisition method is preferably It can be automatically obtained from sports and health apps or corresponding watches, bracelets and other devices. It can also be entered manually by the user, or it can be empty.
  • step (3) can also be performed simultaneously with step (2), or step (3) is performed first, and then step (2) is performed.
  • Step (4) Calculate muscle fatigue FM, heart recovery index RH, and lung recovery index RL according to the user’s resting state and post-exercise physical state parameters, as well as exercise parameters.
  • the calculation methods include but are not limited to weighted average algorithm or Neural network, etc.
  • Step (5) Display the calculated muscle fatigue, heart recovery index, lung recovery index, body stability, heart rate, breathing rate, and breathing intensity on the interface, and give an assessment of exercise volume and physical function, as well as exercise suggestions and Recovery suggestions, etc.
  • FIG. 5 is an exemplary detection flow chart of a user's body state parameter before exercise according to an embodiment of the present invention
  • the process of detecting body state parameters of the user before exercise (at rest) can be implemented by a weight measurement device or a terminal device, and the process includes:
  • Step S21 Collect the first pressure signal, which can be collected by the body weight measurement device
  • Step S22 sequentially perform high-pass amplification and low-pass filtering processing on the first pressure signal
  • Step S23 Perform low-pass filter processing on the first pressure signal after the high-pass amplification and low-pass filter processing, to obtain the body stability BA0 before exercise/resting state, and perform the high-pass amplification and low-pass filter processing on the The first pressure signal is subjected to high-pass processing to obtain the user's pre-exercise/rest heart rate HR0, respiratory rate BR0, and respiratory intensity BI0;
  • Step S24 Determine whether the body state parameters in the historical resting state are stored (including the body stability BA3 in the historical resting state, the heart rate HR3 in the historical resting state, the respiratory rate BR3 in the historical resting state, and the historical resting state). Any one or more of the breathing intensity BI3 in the rest state), if there is, the body state parameter obtained in step S23 and the corresponding body state parameter in the historical resting state are subjected to a weighted average operation to obtain a new The body state parameters of the user before exercise/resting state. If not, the body state parameters obtained in step S23 are used as the body state parameters of the user before exercise (resting state).
  • Step S24 Store and display the body state parameters before exercise (at rest): body stability BA1, heart rate HR1, respiration rate BR1, respiration intensity BI1.
  • FIG. 6 is an exemplary flow chart for detecting muscle fatigue, heart recovery index, and lung recovery index after exercise of a user according to an embodiment of the present invention
  • the process of detecting muscle fatigue FM, heart recovery index RH, and lung recovery index RL after the user exercises can be implemented by a weight measurement device or a terminal device, and the process includes:
  • Step S31 Obtain the user's exercise parameters, where the exercise parameters include one or more of exercise type s, exercise intensity m, exercise duration t, post-exercise duration T, and exercise parameter credibility w; as shown in FIG. 6 Before acquiring the user's motion parameters, the user's motion parameter acquisition method can be set.
  • the manner of obtaining the user's motion parameters may include:
  • exercise parameters can include exercise type s1, exercise intensity m1, exercise duration t1, post-exercise time T1, exercise parameter credibility w1);
  • the user can input the exercise parameter (the exercise parameter may include exercise type s2, exercise intensity m2, exercise duration t2, post-exercise time T2, exercise parameter credibility w2);
  • some sports parameters are obtained from sports health apps or corresponding watches, bracelets and other devices, and some sports parameters are input by the user;
  • the exercise parameters may be unnecessary.
  • the exercise parameters cannot be obtained from the sports health app or corresponding watches, bracelets and other devices, and the user does not input the exercise parameters, the exercise parameters can be empty.
  • sports parameters cannot be obtained from sports and health apps or corresponding watches, bracelets and other devices, pass The user enters the motion parameter, and the user does not enter the motion parameter, the motion parameter is empty.
  • the user's input of the motion parameter may specifically be an input interface for the user to input the motion parameter displayed to the user, and the user performs input and/or selection operations on the input interface to realize the input of the motion parameter.
  • Step S32 Collect the second pressure signal, which can be collected by the body weight measurement device
  • Step S33 sequentially perform high-pass amplification processing and low-pass filtering processing on the first pressure signal
  • Step S34 Perform low-pass filter processing on the second pressure signal after the high-pass amplification processing and low-pass filter processing, to obtain the body stability BA2 before exercise (at rest), and after the high-pass amplification and low-pass filter processing Perform high-pass processing on the second pressure signal of the user to obtain the user's pre-exercise/rest heart rate HR2, respiratory rate BR2, and respiratory intensity BI2;
  • Step S35 Calculate the user's muscle fatigue FM, heart recovery index RH, and lung recovery index RL in combination with the body state parameters and exercise parameters of the user before exercise (at rest).
  • Step S36 Display the calculated muscle fatigue FM, heart recovery index RH, and lung recovery index RL.
  • the calculated muscle fatigue FM, heart recovery index RH, and lung recovery index RL provide exercise volume evaluation, physical function evaluation, and exercise Suggestions and suggestions for body recovery.
  • steps S31 to S36 are an exemplary implementation process, and some steps may be unnecessary or alternative.
  • the muscle fatigue degree FM, the heart recovery index RH and the heart recovery index RH obtained from the calculation in step S36 The lung recovery index RL gives exercise volume assessment, physical function assessment, exercise advice, and physical recovery advice. It may be unnecessary, or it can only give exercise volume assessment, physical function assessment, exercise advice, and body recovery advice. For example, only Exercise volume assessment and physical function assessment.
  • FIG. 7 is an exemplary schematic diagram of measuring a user's body state parameters before/after exercise according to an embodiment of the present invention.
  • the determination of the user's body state parameters before exercise according to the first pressure signal includes:
  • Step S41 the first pressure signal (signal 0) is sequentially subjected to high-pass amplification processing and low-pass filtering processing to obtain the first signal (that is, signal 2 shown in FIG. 7);
  • step S41 may further include: performing low-pass filtering processing on the first pressure signal (signal 0) to obtain weight information (that is, signal 1 shown in FIG. 7).
  • the pre-exercise respiration frequency BR1 and the pre-exercise respiration intensity BI1 are calculated based on the peak-to-peak value of the waveform profile of the pre-exercise cardiac shock signal (signal 3).
  • the main frequency, peak-to-peak value and standard deviation of the second signal (signal 4) are used to calculate the pre-exercise body stability BA1 according to the main frequency, peak-to-peak value and standard deviation of the second signal (signal 4).
  • the calculation method of the body flat temperature BA1 includes but is not limited to multiple Linear regression.
  • step S42(a) and step S42(b) can be performed simultaneously or in a sequence.
  • the embodiment of the present invention does not limit the sequence.
  • the user’s body state parameters after exercise are determined according to the second pressure signal, including:
  • Step S51 perform high-pass amplification and low-pass filtering on the second pressure signal (signal 0) in order to obtain a third signal (that is, signal 2 shown in FIG. 7);
  • step S51 may further include: performing low-pass filtering processing on the second pressure signal (signal 0) to obtain weight information (that is, signal 1 shown in FIG. 7).
  • the post-exercise respiration frequency BR2 and the post-exercise respiration intensity BI2 are calculated based on the peak-to-peak value of the waveform profile of the post-exercise cardiac shock signal (signal 3).
  • the main frequency, peak-to-peak value and standard deviation of the fourth signal (signal 4) are used to calculate the post-exercise body stability BA2 according to the main frequency, peak-to-peak value and standard deviation of the fourth signal (signal 4).
  • the calculation method of body flat temperature BA2 includes but is not limited to multiple Linear regression.
  • step S52(a) and step S52(b) can be performed simultaneously or in a sequence.
  • the embodiment of the present invention does not limit the sequence.
  • FIG. 7 measures the pressure of the human body against it by a weight measuring device, generates a pressure signal, and processes the pressure signal through different high-pass, low-pass, and amplifying processes to analyze the weight, heart rate, respiratory frequency, and respiratory intensity.
  • Body stability and other signals, without additional sensors, only pressure signals can be used to obtain a variety of human physiological signals (that is, body state parameters).
  • the weight measurement device or the terminal device may use the following methods to calculate the user's muscle fatigue, heart recovery index, and lung recovery index.
  • Method 1 Through the weighted average algorithm.
  • using a weighting algorithm to calculate the user's muscle fatigue FM may include:
  • Step S61 Calculate the physiological parameters before and after exercise by using the body stability before exercise BA1, the heart rate HR1, the respiratory frequency BR1, the respiratory intensity BI1, and the post exercise body stability BA2, the heart rate HR2, the respiratory frequency BR2, and the respiratory intensity BI2.
  • Difference body stability difference BA, heart rate difference HR, respiratory rate difference BR, and respiratory intensity difference BI).
  • the body stability difference BA BA2-BA1
  • the heart rate difference HR HR2-HR1
  • the respiratory rate difference BR BR2-BR1
  • the respiratory intensity difference BI BI2-BI1.
  • Step S62 Using a weighted average algorithm, calculate the muscle fatigue FM based on the body stability difference BA, the heart rate difference HR, the respiratory rate difference BR, and the respiratory intensity difference BI.
  • FM a1*BA+a2*HR+a3*BR+a4*BI
  • a1, a2, a3, and a4 are coefficients, which can be flexibly set according to actual needs.
  • using a weighting algorithm to calculate the user's heart recovery index RH may include:
  • Step S71 Calculate the physiological parameters before and after exercise by using body stability BA1, heart rate HR1, breathing rate BR1, breathing intensity BI1, and body stability after exercise BA2, heart rate HR2, breathing frequency BR2, and breathing intensity BI2 Difference (body stability difference BA, heart rate difference HR, respiratory rate difference BR, and respiratory intensity difference BI).
  • Step S72 Using weighted average algorithm, according to body stability difference BA, heart rate difference HR, respiratory rate difference BR and respiratory intensity difference BI, combined with exercise type s1, exercise intensity m1, exercise duration t1, and post-exercise time T1, exercise parameter credibility w1 to calculate the heart recovery index RH.
  • RH b1*BA+b2*HR+b3*BR+b4*BI+b5*s1+b6*m1+b7*t1+b8*T1+b9*w1;
  • b1, b2, b3, b4, b5, b6, b7, b8, and b9 are coefficients, which can be flexibly set according to actual needs.
  • calculating the user's lung recovery index RL by using a weighting algorithm may include:
  • Step S81 Use the body stability before exercise BA1, heart rate HR1, breathing rate BR1, breathing intensity BI1, and the body stability after exercise BA2, heart rate HR2, breathing frequency BR2, and breathing intensity BI2 to calculate the physiological parameters before and after exercise. Difference (body stability difference BA, heart rate difference HR, respiratory rate difference BR, and respiratory intensity difference BI).
  • Step S82 Using weighted average algorithm, according to body stability difference BA, heart rate difference HR, respiratory rate difference BR and respiratory intensity difference BI, combined with exercise type s, exercise intensity m, exercise duration t, and post-exercise time T, exercise parameter credibility w, calculate the heart recovery index RL.
  • RL c1*BA+c2*HR+c3*BR+c4*BI+c5*s+c6*m+c7*t+c8*T+c9*w;
  • c1, c2, c3, c4, c5, c6, c7, c8, and c9 are coefficients, which can be flexibly set according to actual needs.
  • the embodiment of the present invention calculates the difference in physiological parameters such as body stability, heart rate, breathing frequency, and breathing intensity before and after exercise to characterize changes in physiological performance before and after exercise.
  • physiological parameters such as body stability, heart rate, breathing frequency, and breathing intensity
  • a weighted average algorithm is used to calculate the user’s post-exercise.
  • the muscular fatigue, heart recovery ability and lung recovery ability are highly accurate and easy to calculate.
  • Method 2 Through neural network.
  • the trained neural network is obtained, and then the muscle fatigue FM, the heart recovery index RH and the heart recovery index RL are calculated according to the trained neural network.
  • the training set when training the neural network model may include the input training set and the input training set: where the input training set may include: the physiological parameter difference before and after the user exercise (body stability difference BA, heart rate difference HR , Breathing rate difference BR, breathing intensity difference BI) and exercise parameters (exercise type s, exercise intensity m, exercise duration t, post-exercise time T, exercise parameter credibility w); the output training set can include: muscle fatigue Degree FM, heart recovery capacity RH and lung recovery capacity RL, among which muscle fatigue FM can be measured by blood lactic acid method or electromyography method. The heart recovery capacity RH and the lung recovery capacity RL can be measured by an exercise testing machine.
  • the physiological parameter difference before and after the user exercise body stability difference BA, heart rate difference HR , Breathing rate difference BR, breathing intensity difference BI
  • exercise parameters exercise type s, exercise intensity m, exercise duration t, post-exercise time T, exercise parameter credibility w
  • the output training set can include: muscle fatigue Degree FM, heart recovery capacity R
  • the calculation parameters of the neural network can be obtained.
  • the parameters in the neural network are used to calculate muscle fatigue FM, heart recovery capacity RH, and lung recovery capacity RL.
  • FIG. 8 is a schematic diagram of calculating muscle fatigue FM, heart recovery index RH, and lung recovery index RL through neural network according to an embodiment of the present invention
  • the input information of the neural network after training may include: the physiological parameter difference before and after the user exercise (body stability difference BA, heart rate difference HR, respiratory rate difference BR, respiratory intensity difference BI) And exercise parameters (exercise type s, exercise intensity m, exercise duration t, post-exercise time T, exercise parameter credibility w).
  • the trained neural network can calculate the muscle fatigue FM, the heart recovery index RH and the lung recovery index RL through the above input information and output them.
  • the embodiment of the present invention uses a neural network to calculate muscle fatigue FM, heart recovery index RH, and lung recovery index RL.
  • the network can effectively perform multi-parameter fusion to obtain deeper physiological parameters.
  • FIG. 9 is a state selection interface before and after exercise of an exemplary device for detecting muscle fatigue after exercise provided by an embodiment of the present invention.
  • the user can select the state of the user through the state selection interface, whether it is the resting state before exercise or the state after exercise. If the user is in the resting state before exercise, they can select [An static state] in Figure 9, if the user is For the state after exercise, you can select [After exercise] in Figure 9.
  • Fig. 10 is an exemplary setting interface for automatic acquisition of sports parameters provided by an embodiment of the present invention.
  • the setting interface for automatic acquisition of exercise parameters is used to show the user whether to automatically acquire exercise parameters from the Sports Health App.
  • the exercise parameters include exercise type s, exercise intensity m, exercise duration t, post-exercise time T, and exercise parameter credibility w. One or any number of.
  • the device for detecting muscle fatigue after exercise will be set to obtain exercise parameters from the Sports Health App.
  • the manual input selection interface as shown in Figure 11 can be shown to the user. If the user selects [No] in Figure 10 or [Yes] in Figure 11, Then the user can be shown the exercise parameter input interface shown in Figure 12, through which the user can manually input or set the exercise parameters, etc., specifically allowing the user to select or input the exercise type s, exercise intensity m and/or exercise duration t, Post-exercise time (exercise end time) T.
  • a drop-down menu can be used to allow the user to select the type of exercise and the time after exercise (exercise end time) T, such as running, swimming, squatting, basketball, etc.
  • the selected exercise type allows the user to select or input the exercise intensity/duration of the exercise type selected by the user. For example, if the type of exercise selected by the user is running/swimming, expand the menu for the user to select or set the length of running time. If the exercise type selected by the user is squat, the menu for the user to select or set the number of squats is expanded.
  • Figure 12 and Figure 13 provide schematic diagrams of two exercise parameter input interfaces.
  • FIG. 14 is an exemplary measurement result display interface provided by an embodiment of the present invention.
  • the measurement result display interface shows the user: the user’s weight, heart rate HR2, heart recovery index RH, breathing rate BR2, breathing intensity BI2, lung recovery index RH, body stability BA2, muscle fatigue FM, and Physical recovery and exercise recommendations.
  • FIG. 14 is only an exemplary embodiment. In other embodiments, it may only display the user's weight, heart rate HR2, heart recovery index RH, respiratory rate BR2, respiratory intensity BI2, lung recovery index RH, and body stability.
  • the degree of BA2, the degree of muscle fatigue FM, and some of the information in the body recovery and exercise recommendations or other information may also be shown to the user.
  • the embodiment of the present invention displays the detection results to the user, facilitates the user to understand the current physical state through human-computer interaction, and provides the user with guidance on exercise and physical recovery, which can prevent the user from exercising with unreasonable intensity and time. Train and guide users to quickly restore physical functions.
  • the embodiment of the application also discloses a device for detecting muscle fatigue after exercise.
  • the device for detecting muscle fatigue after exercise may be the above body weight measurement device or the above terminal device. It should be understood that the device 400 can Perform each step in the method for detecting muscle fatigue after exercise, in order to avoid repetition, it will not be described in detail here. As shown in FIG. 14, the device 400 includes: a first acquisition module 410 and a first calculation module 420.
  • the first acquiring module 410 is used to acquire the body state parameters of the user before exercise and the body state parameters after exercise, where the body state parameters include body stability, heart rate, breathing rate, and breathing intensity; and
  • the first calculation module 420 is configured to calculate the user's muscle fatigue after exercise according to the user's body state parameters before exercise and the user's body state parameters after exercise.
  • the embodiment of the present invention obtains the user's body state parameters before exercise and post-exercise body state parameters, and then calculates the user's muscle fatigue after exercise according to the user's body state parameters before exercise and the user's body state parameters after exercise. , No need to collect the user's blood, simple operation, high user acceptance, and at the same time, the muscle fatigue detection can be achieved by relying on the weight measurement equipment, without the need for special testing equipment.
  • the first obtaining module 410 may include:
  • the first acquisition unit is configured to receive the first pressure signal generated by the weight measurement device, and determine the user's body state parameter before exercise according to the first pressure signal, wherein the first pressure signal is used by the weight measurement device to measure the weight of the user before the exercise Generated when the device is taking weight measurements;
  • the second acquisition unit is configured to receive a second pressure signal generated by the weight measurement device, and determine the body state parameter of the user after exercise according to the second pressure signal, wherein the second pressure signal is used by the weight measurement device after the user exercises the weight measurement device Generated when taking a weight measurement.
  • the first obtaining module 410 may include:
  • the first determining unit is configured to measure the weight of the user before the user exercises, generate a first pressure signal according to the pressure applied by the user, and determine the physical state parameters of the user before the exercise according to the first pressure signal;
  • the second determining unit is configured to measure the weight of the user after the user exercises, generate a second pressure signal according to the pressure applied by the user, and determine the body state parameter of the user after the exercise according to the second pressure signal.
  • the apparatus 400 may further include:
  • the second acquiring module is used to acquire the motion parameters corresponding to the user's motion.
  • the second calculation module is used to calculate the user's heart recovery index and lung recovery index according to the user's body state parameters before exercise, the user's body state parameters after exercise, and the user's exercise parameters.
  • the first calculation module 410 may include:
  • the first calculation unit is used to calculate the user's muscle fatigue after exercise according to the user's body state parameters before exercise, the user's body state parameters after exercise, and the user's exercise parameters.
  • the exercise parameter includes one or more of exercise type, exercise intensity, exercise duration, post-exercise duration, and exercise parameter credibility.
  • the first acquiring module or the first determining module may include:
  • the first processing unit is used to sequentially perform high-pass amplification and low-pass filtering on the first pressure signal to obtain the first signal, perform high-pass filtering on the first signal to obtain the pre-exercise cardiac shock signal, and according to the pre-exercise cardiac
  • the shock signal determines the heart rate before exercise
  • the waveform profile of the pre-exercise cardiac shock signal is determined according to the waveform change of the pre-exercise cardiac shock signal
  • the pre-exercise respiration frequency is calculated according to the peak feature points of the waveform profile of the pre-exercise cardiac shock signal and Calculate the pre-exercise respiration intensity based on the peak-to-peak value of the waveform profile of the pre-exercise cardiac shock signal;
  • the second processing unit is used to perform low-pass filter processing on the first signal to obtain the second signal, calculate the dominant frequency, peak-to-peak value and standard deviation of the second signal, according to the dominant frequency, peak-to-peak value and standard deviation of the second signal Calculate the body stability before exercise.
  • the second acquiring module or the second determining module may include:
  • the third processing unit is used to sequentially perform high-pass amplification processing and low-pass filter processing on the second pressure signal to obtain a third signal, perform high-pass filter processing on the third signal to obtain a post-exercise cardiac shock signal, according to the post-exercise heart
  • the shock signal determines the heart rate after exercise
  • the waveform profile of the cardiac shock signal after exercise is determined according to the waveform change of the cardiac shock signal after exercise
  • the respiration frequency after exercise is calculated according to the peak feature points of the waveform contour of the cardiac shock signal after exercise. Calculate the post-exercise respiration intensity based on the peak-to-peak value of the waveform profile of the post-exercise cardiac shock signal.
  • the fourth processing unit is used to perform low-pass filter processing on the third signal to obtain the fourth signal, calculate the dominant frequency, peak-to-peak value and standard deviation of the fourth signal, according to the dominant frequency, peak-to-peak value and standard deviation of the fourth signal Calculate the body stability after exercise.
  • the first calculation module 420 may include:
  • the second calculation unit is used to calculate the body stability difference between the user's body stability before exercise and the user's body stability after exercise, and to calculate the heart rate difference between the user's heart rate before exercise and the user's heart rate after exercise Calculate the difference between the user's breathing rate before exercise and the user's breathing rate after exercise, and calculate the difference between the user's breathing intensity before exercise and the user's breathing intensity after exercise;
  • the third calculation unit is used to calculate the user's muscle fatigue after exercise by using a weighted average algorithm based on the difference in body stability, the difference in heart rate, the difference in respiratory frequency, and the difference in respiratory intensity.
  • the second calculation unit may include:
  • the first calculation subunit is used to calculate the heart recovery index and lungs of the user after exercise by using the weighted average algorithm according to the difference in body stability, heart rate, respiratory rate, and respiratory intensity, and combined with the user's exercise parameters. Recovery index.
  • the first calculation unit may include:
  • the second calculation subunit is used to calculate the body stability difference between the user's body stability before exercise and the user's body stability after exercise, and to calculate the heart rate between the user's heart rate before exercise and the user's heart rate after exercise Difference, and calculating the difference between the user's respiration rate before exercise and the user’s respiration rate after exercise, and calculating the difference between the user’s respiration intensity before exercise and the user’s respiration intensity after exercise;
  • the third calculation subunit is used to calculate the user’s muscle fatigue, heart recovery index, and lungs based on the difference in body stability, heart rate, respiratory rate, and respiratory intensity, combined with the user’s exercise parameters, and the neural network. Recovery index.
  • the apparatus 400 may further include:
  • the first display module is used to display any one or more of the calculated user’s muscle fatigue, heart recovery index, and lung recovery index, and according to the user’s muscle fatigue, heart recovery index, and lung recovery index.
  • the embodiment of the present invention displays the detection results to the user, facilitates the user to understand the current physical state through human-computer interaction, and provides the user with guidance on exercise and physical recovery, which can prevent the user from exercising with unreasonable intensity and time. Train and guide users to quickly restore physical functions.
  • the electronic device 100 includes a memory 121 and a processor 110.
  • the memory 121 stores a computer program
  • the processor 110 is connected to the memory 121, and the processor 110 executes the computer program to Realize the above-mentioned method for detecting muscle fatigue after exercise.
  • This application also provides a body weight measurement device, which includes a memory and a processor, and a computer program is stored in the memory.
  • the processor is connected to the memory, and the processor executes the computer program to realize the detection of muscle fatigue after exercise as described above. method.
  • This application also provides a computer storage medium, including computer instructions.
  • the computer instructions run on the electronic device 100 or the weight measurement device, the electronic device 100 or the weight measurement device executes the above-mentioned method for detecting muscle fatigue after exercise. The various steps in.
  • the present application also provides a computer program product.
  • the computer program product runs on a computer
  • the computer program product runs on the computer
  • the computer executes the steps in the above-mentioned post-exercise muscle fatigue detection method.

Abstract

A method and apparatus for measuring a muscle fatigue degree after exercise, and an electronic device. The measuring method comprises: obtaining body state parameters of a user before exercise and the body state parameters after exercise (S11), wherein the body state parameters comprise body stability, a heart rate, a respiratory rate, and a respiration intensity; and calculating, according to the body state parameters of the user before exercise and the body state parameters of the user after exercise, a muscle fatigue degree of the user after exercise (S12). The measuring method facilitates self-measurement of users, and has high user acceptance.

Description

运动后肌肉疲劳度的检测方法及装置、电子设备Method and device for detecting muscle fatigue after exercise, and electronic equipment
本申请要求于2020年5月20日提交中国专利局、申请号为202010430716.0、申请名称为“运动后肌肉疲劳度的检测方法及装置、电子设备”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。This application claims the priority of a Chinese patent application filed with the Chinese Patent Office on May 20, 2020, the application number is 202010430716.0, and the application name is "Methods and devices for detecting muscle fatigue after exercise, and electronic equipment". The entire content of the application is approved. The reference is incorporated in this application.
技术领域Technical field
本申请涉及智能检测设备技术领域，具体涉及运动后肌肉疲劳度的检测方法及装置、电子设备。This application relates to the technical field of intelligent detection equipment, and in particular to methods and devices for detecting muscle fatigue after exercise, and electronic equipment.
背景技术Background technique
运动后肌肉疲劳是评估运动强度和人体运动后身体恢复机能的重要指标，当前主要的测量方法有血液乳酸法和表面肌电法。其中，血液乳酸法利用血液乳酸检测肌肉疲劳度，准确性高，但是需要采集血液，不能被大部分群体接受；表面肌电法利用表面肌电信号(Emg，Electroyogram)评定，可以分肌肉块测量肌肉疲劳，但是需要在不同的肌肉块贴放电极，操作繁琐，不适合普通用户自行测量。为了检测运动后的肌肉疲劳程度，从而为用户评估运动强度，亟需提供一种检测方便且与用户接受度的肌肉疲劳度检测方案。Muscle fatigue after exercise is an important indicator for evaluating exercise intensity and body recovery function after exercise. The current main measurement methods are blood lactic acid method and surface electromyography method. Among them, the blood lactic acid method uses blood lactic acid to detect muscle fatigue with high accuracy, but it needs to collect blood, which cannot be accepted by most groups; the surface electromyography method uses surface electromyogram (Emg, Electroyogram) for evaluation, which can be measured by muscle mass. Muscle fatigue, but electrodes need to be attached to different muscle masses, which is cumbersome to operate and is not suitable for ordinary users to measure by themselves. In order to detect the degree of muscle fatigue after exercise, so as to evaluate the exercise intensity for users, it is urgent to provide a muscle fatigue detection solution that is convenient for detection and acceptable to users.
发明内容Summary of the invention
本申请实施例提供一种运动后肌肉疲劳度的检测方法、装置、电子设备、体重测量设备、计算机存储介质及计算机程序产品，能够对用户运动后肌肉疲劳度进行检测，能够方便用户对运动后肌肉疲劳度自行测量且用户接收度高。The embodiments of the present application provide a method, device, electronic equipment, weight measuring equipment, computer storage medium and computer program product for detecting muscle fatigue after exercise, which can detect the muscle fatigue of the user after exercise, and facilitate the user to check after exercise. Muscle fatigue is self-measured and user acceptance is high.
第一方面，本申请实施例提供了一种运动后肌肉疲劳度的检测方法，应用于运动后肌肉疲劳度的检测装置，所述方法包括：In the first aspect, an embodiment of the present application provides a method for detecting muscle fatigue after exercise, which is applied to a device for detecting muscle fatigue after exercise, and the method includes:
获取用户运动前的身体状态参数以及运动后的身体状态参数，其中，所述身体状态参数包括身体平稳度、心率、呼吸频率及呼吸强度；Acquiring the body state parameters of the user before exercise and the body state parameters after exercise, where the body state parameters include body stability, heart rate, breathing frequency, and breathing intensity;
根据所述用户运动前的身体状态参数以及所述用户在运动后的身体状态参数计算所述用户运动后的肌肉疲劳度。The muscle fatigue degree of the user after exercise is calculated according to the body state parameter of the user before exercise and the body state parameter of the user after exercise.
结合第一方面，在一种可行的实现方式中，所述获取所述用户运动前的身体状态参数，包括：With reference to the first aspect, in a feasible implementation manner, the acquiring the physical state parameters of the user before exercise includes:
接收体重测量设备生成的第一压力信号，根据所述第一压力信号确定所述用户在运动前的身体状态参数，其中，所述第一压力信号由体重测量设备在所述用户运动前使用所述体重测量设备进行体重测量时产生；Receive a first pressure signal generated by a weight measurement device, and determine the physical state parameter of the user before exercise according to the first pressure signal, wherein the first pressure signal is used by the weight measurement device before the user exercises Said weight measurement device is generated when the weight measurement is performed;
所述获取所述用户运动后的身体状态参数，包括：The acquiring the physical state parameters of the user after exercise includes:
接收所述体重测量设备生成的第二压力信号，根据所述第二压力信号确定所述用户运动后的身体状态参数，其中，所述第二压力信号由体重测量设备在所述用户运动后使用所述体重测量设备进行体重测量时产生。Receive a second pressure signal generated by the weight measurement device, and determine the body state parameter of the user after exercise according to the second pressure signal, wherein the second pressure signal is used by the weight measurement device after the user exercises The weight measurement device is generated when the weight measurement is performed.
结合第一方面，在一种可行的实现方式中，所述获取所述用户运动前的身体状态参数，包括：With reference to the first aspect, in a feasible implementation manner, the acquiring the physical state parameters of the user before exercise includes:
在所述用户运动前，对所述用户进行体重测量，根据所述用户所施加的压力生成第一压力信 号，根据所述第一压力信号确定所述用户在运动前的身体状态参数；Before the user exercises, measure the weight of the user, generate a first pressure signal according to the pressure applied by the user, and determine the physical state parameters of the user before the exercise according to the first pressure signal;
所述获取所述用户运动后的身体状态参数，包括：The acquiring the physical state parameters of the user after exercise includes:
在所述用户运动后，对所述用户进行体重测量，根据所述用户所施加的压力生成第二压力信号，根据所述第二压力信号确定所述用户在运动后的身体状态参数。After the user exercises, measure the weight of the user, generate a second pressure signal according to the pressure applied by the user, and determine the body state parameter of the user after the exercise according to the second pressure signal.
结合第一方面，在一种可行的实现方式中，所述方法还包括：With reference to the first aspect, in a feasible implementation manner, the method further includes:
获取所述用户运动对应的运动参数；Acquiring the motion parameter corresponding to the user motion;
根据所述用户运动前的身体状态参数、所述用户运动后的身体状态参数以及所述用户的运动参数计算所述用户的心脏恢复指数及肺恢复指数。The heart recovery index and the lung recovery index of the user are calculated according to the body state parameters of the user before exercise, the body state parameters of the user after exercise, and the exercise parameters of the user.
结合第一方面，在一种可行的实现方式中，所述根据所述用户运动前的身体状态参数以及所述用户在运动后的身体状态参数计算所述用户运动后的肌肉疲劳度，包括：With reference to the first aspect, in a feasible implementation manner, the calculation of the muscle fatigue of the user after exercise according to the physical state parameters of the user before exercise and the physical state parameters of the user after exercise includes:
根据所述用户运动前的身体状态参数、所述用户在运动后的身体状态参数以及所述用户的运动参数计算所述用户运动后的肌肉疲劳度。The muscle fatigue degree of the user after exercise is calculated according to the physical state parameters of the user before exercise, the physical state parameters of the user after exercise, and the exercise parameters of the user.
结合第一方面，在一种可行的实现方式中，所述运动参数包括运动类型、运动强度、运动时长、运动后时长及运动参数可信度中的一种或者任意多种。With reference to the first aspect, in a feasible implementation manner, the exercise parameter includes one or more of exercise type, exercise intensity, exercise duration, post-exercise duration, and exercise parameter credibility.
结合第一方面，在一种可行的实现方式中，所述根据所述第一压力信号确定所述用户在运动前的身体状态参数，包括：With reference to the first aspect, in a feasible implementation manner, the determining the physical state parameters of the user before exercise according to the first pressure signal includes:
对所述第一压力信号依次进行高通放大处理和低通滤波处理后得到第一信号，将所述第一信号进行高通滤波处理后得到运动前的心冲击信号，根据所述运动前的心冲击信号确定运动前的心率，根据所述运动前的心冲击信号的波形变化确定所述运动前的心冲击信号的波形轮廓，根据所述运动前的心冲击信号的波形轮廓的峰值特征点计算运动前的呼吸频率以及根据所述运动前的心冲击信号的波形轮廓的峰峰值计算运动前的呼吸强度；The first pressure signal is sequentially subjected to high-pass amplification processing and low-pass filtering processing to obtain a first signal, and the first signal is subjected to high-pass filtering processing to obtain a pre-exercise cardiac shock signal, according to the pre-exercise cardiac shock The signal determines the heart rate before exercise, the waveform profile of the cardiac shock signal before exercise is determined according to the waveform change of the cardiac shock signal before exercise, and the exercise is calculated according to the peak feature points of the waveform contour of the cardiac shock signal before exercise The pre-exercise respiration frequency and the pre-exercise respiration intensity calculated according to the peak-to-peak value of the waveform profile of the pre-exercise cardiac shock signal;
对所述第一信号进行低通滤波处理，得到第二信号，计算出所述第二信号的主频、峰峰值及标准差，根据所述第二信号的主频、峰峰值及标准差计算运动前的身体平稳度。Perform low-pass filtering on the first signal to obtain a second signal, calculate the dominant frequency, peak-to-peak value and standard deviation of the second signal, and calculate based on the dominant frequency, peak-to-peak value and standard deviation of the second signal Body stability before exercise.
结合第一方面，在一种可行的实现方式中，所述根据所述第二压力信号确定所述用户运动后的身体状态参数，包括：With reference to the first aspect, in a feasible implementation manner, the determining the body state parameter of the user after exercise according to the second pressure signal includes:
对所述第二压力信号依次进行高通放大处理和低通滤波处理后得到第三信号，将所述第三信号进行高通滤波处理后得到运动后的心冲击信号，根据所述运动后的心冲击信号确定运动后的心率，根据所述运动后的心冲击信号的波形变化确定所述运动后的心冲击信号的波形轮廓，根据所述运动后的心冲击信号的波形轮廓的峰值特征点计算运动后的呼吸频率以及根据所述运动后的心冲击信号的波形轮廓的峰峰值计算运动后的呼吸强度；The second pressure signal is sequentially subjected to high-pass amplification processing and low-pass filtering processing to obtain a third signal, and the third signal is subjected to high-pass filtering processing to obtain a post-exercise cardiac shock signal, according to the post-exercise cardiac shock The signal determines the heart rate after exercise, the waveform contour of the post-exercise cardiac shock signal is determined according to the waveform change of the post-exercise cardiac shock signal, and the exercise is calculated according to the peak feature points of the waveform contour of the post-exercise cardiac shock signal The post-exercise respiration frequency and the post-exercise respiration intensity calculated according to the peak-to-peak value of the waveform profile of the post-exercise cardiac shock signal;
对所述第三信号进行低通滤波处理，得到第四信号，计算出所述第四信号的主频、峰峰值及标准差，根据所述第四信号的主频、峰峰值及标准差计算运动后的身体平稳度。Perform low-pass filtering on the third signal to obtain a fourth signal, calculate the dominant frequency, peak-to-peak value and standard deviation of the fourth signal, and calculate based on the dominant frequency, peak-to-peak value and standard deviation of the fourth signal Body stability after exercise.
结合第一方面，在一种可行的实现方式中，所述根据所述用户运动前的身体状态参数以及所述用户在运动后的身体状态参数计算所述用户运动后的肌肉疲劳度，包括：With reference to the first aspect, in a feasible implementation manner, the calculation of the muscle fatigue of the user after exercise according to the physical state parameters of the user before exercise and the physical state parameters of the user after exercise includes:
计算所述用户运动前的身体平稳度与所述用户运动后的身体平稳度之间的身体平稳度差值，以及计算所述用户运动前的心率与所述用户运动后的心率之间的心率差值，以及计算所述用户运动前的呼吸频率与所述用户运动后的呼吸频率之间的呼吸频率差值，以及计算所述用户运动前的呼吸强度与所述用户运动后的呼吸强度之间的呼吸强度差值；Calculate the body stability difference between the body stability of the user before exercise and the body stability of the user after exercise, and calculate the heart rate between the heart rate of the user before exercise and the heart rate of the user after exercise Difference, and calculating the difference between the breathing frequency of the user before exercise and the breathing frequency of the user after exercise, and calculating the difference between the breathing intensity of the user before exercise and the breathing intensity of the user after exercise Difference in respiratory intensity between;
根据所述身体平稳度差值、所述心率差值、所述呼吸频率差值及呼吸强度差值，采用加权平 均算法计算所述用户运动后的肌肉疲劳度。According to the difference in body stability, the difference in heart rate, the difference in respiratory rate, and the difference in respiratory intensity, a weighted average algorithm is used to calculate the user's muscle fatigue after exercise.
结合第一方面，在一种可行的实现方式中，根据所述用户运动前的身体状态参数、所述用户在运动后的身体状态参数以及所述用户的运动参数计算所述用户运动后的心脏恢复指数及肺恢复指数，包括：With reference to the first aspect, in a feasible implementation manner, the user’s heart after exercise is calculated according to the user’s body state parameters before exercise, the user’s body state parameters after exercise, and the user’s exercise parameters. Recovery index and lung recovery index, including:
根据所述身体平稳度差值、所述心率差值、所述呼吸频率差值及所述呼吸强度差值，并结合所述用户的运动参数，采用加权平均算法计算所述用户运动后的心脏恢复指数及肺恢复指数。According to the body stability difference, the heart rate difference, the respiratory frequency difference, and the respiratory intensity difference, combined with the user's exercise parameters, a weighted average algorithm is used to calculate the user's heart after exercise Recovery index and lung recovery index.
结合第一方面，在一种可行的实现方式中，所述根据所述用户运动前的身体状态参数、所述用户在运动后的身体状态参数计算以及所述用户的运动参数计算所述用户的肌肉疲劳度、心脏恢复指数及肺恢复指数，包括：With reference to the first aspect, in a feasible implementation manner, the calculation of the user’s physical state parameters according to the user’s physical state parameters before exercise, the user’s physical state parameter calculations after exercise, and the user’s exercise parameters Muscle fatigue, heart recovery index and lung recovery index, including:
计算所述用户运动前的身体平稳度与所述用户运动后的身体平稳度之间的身体平稳度差值，以及计算所述用户运动前的心率与所述用户运动后的心率之间的心率差值，以及计算所述用户运动前的呼吸频率与所述用户运动后的呼吸频率之间的呼吸频率差值，以及计算所述用户运动前的呼吸强度与所述用户运动后的呼吸强度之间的呼吸强度差值；Calculate the body stability difference between the body stability of the user before exercise and the body stability of the user after exercise, and calculate the heart rate between the heart rate of the user before exercise and the heart rate of the user after exercise Difference, and calculating the difference between the breathing frequency of the user before exercise and the breathing frequency of the user after exercise, and calculating the difference between the breathing intensity of the user before exercise and the breathing intensity of the user after exercise Difference in respiratory intensity between;
根据所述身体平稳度差值、所述心率差值、所述呼吸频率差值及所述呼吸强度差值，结合所述用户的运动参数，使用神经网络计算所述用户的肌肉疲劳度、心脏恢复指数及肺恢复指数。According to the body stability difference, the heart rate difference, the breathing rate difference, and the breathing intensity difference, combined with the user's exercise parameters, a neural network is used to calculate the user's muscle fatigue and heart rate. Recovery index and lung recovery index.
结合第一方面，在一种可行的实现方式中，所述方法还包括：With reference to the first aspect, in a feasible implementation manner, the method further includes:
显示计算得到的所述用户的肌肉疲劳度、心脏恢复指数及肺恢复指数中的任意一个或者任意多个，以及根据所述用户的肌肉疲劳度、心脏恢复指数及肺恢复指数中的一个或者任意多个生成并显示运动量评估、身体机能评估、运动建议、身体恢复建议中的一个或者任意多个。Display any one or any of the calculated muscle fatigue, heart recovery index, and lung recovery index of the user, and one or any of the user’s muscle fatigue, heart recovery index, and lung recovery index Multiple generations and displays one or more of exercise volume evaluation, physical function evaluation, exercise suggestion, and physical recovery suggestion.
第二方面，本申请实施例提供了一种运动后肌肉疲劳度的检测装置，所述装置包括：In a second aspect, an embodiment of the present application provides a device for detecting muscle fatigue after exercise, and the device includes:
第一获取模块，用于获取用户运动前的身体状态参数以及运动后的身体状态参数，其中，所述身体状态参数包括身体平稳度、心率、呼吸频率及呼吸强度；以及The first acquisition module is used to acquire the body state parameters of the user before exercise and the body state parameters after exercise, wherein the body state parameters include body stability, heart rate, breathing rate, and breathing intensity; and
第一计算模块，用于根据所述用户运动前的身体状态参数以及所述用户在运动后的身体状态参数计算所述用户运动后的肌肉疲劳度。The first calculation module is configured to calculate the muscle fatigue of the user after exercise according to the physical state parameters of the user before exercise and the physical state parameters of the user after exercise.
可以理解，本发明实施例通过获取用户运动前的身体状态参数以及运动后的身体状态参数，然后根据所述用户运动前的身体状态参数以及所述用户在运动后的身体状态参数计算所述用户运动后的肌肉疲劳度，无需采集用户的血液且操作简单，用户接受度高，同时，依靠体重测量设备就能够实现肌肉疲劳度检测，无需专门的检测设备。It can be understood that the embodiment of the present invention obtains the body state parameters of the user before exercise and the body state parameters after exercise, and then calculates the user's body state parameters based on the body state parameters of the user before exercise and the body state parameters of the user after exercise. The muscle fatigue after exercise does not need to collect the user's blood, the operation is simple, and the user acceptability is high. At the same time, the muscle fatigue detection can be realized by relying on the weight measurement equipment, without the need for special testing equipment.
结合第二方面，在一种可行的实现方式中，第一获取模块可以包括：With reference to the second aspect, in a feasible implementation manner, the first acquisition module may include:
第一获取单元，用于接收体重测量设备生成的第一压力信号，根据所述第一压力信号确定所述用户在运动前的身体状态参数，其中，所述第一压力信号由体重测量设备在所述用户运动前使用所述体重测量设备进行体重测量时产生；以及The first acquisition unit is configured to receive the first pressure signal generated by the weight measurement device, and determine the physical state parameter of the user before exercise according to the first pressure signal, wherein the first pressure signal is used by the weight measurement device Generated when the user uses the weight measurement device to perform weight measurement before the user exercises; and
第二获取单元，用于接收所述体重测量设备生成的第二压力信号，根据所述第二压力信号确定所述用户运动后的身体状态参数，其中，所述第二压力信号由体重测量设备在所述用户运动后使用所述体重测量设备进行体重测量时产生。The second acquisition unit is configured to receive a second pressure signal generated by the weight measurement device, and determine the body state parameter of the user after exercise according to the second pressure signal, wherein the second pressure signal is generated by the weight measurement device It is generated when the user uses the weight measurement device to perform weight measurement after the user exercises.
结合第二方面，在一种可行的实现方式中，第一获取模块可以包括：With reference to the second aspect, in a feasible implementation manner, the first acquisition module may include:
第一确定单元，用于在所述用户运动前，对所述用户进行体重测量，根据所述用户所施加的压力生成第一压力信号，根据所述第一压力信号确定所述用户在运动前的身体状态参数；以及The first determining unit is configured to measure the weight of the user before the user exercises, generate a first pressure signal according to the pressure applied by the user, and determine according to the first pressure signal that the user is before the exercise Physical state parameters; and
第二确定单元，用于在所述用户运动后，对所述用户进行体重测量，根据所述用户所施加的 压力生成第二压力信号，根据所述第二压力信号确定所述用户在运动后的身体状态参数。The second determining unit is configured to measure the weight of the user after the user exercises, generate a second pressure signal according to the pressure applied by the user, and determine that the user is after exercise according to the second pressure signal Physical state parameters.
结合第二方面，在一种可行的实现方式中，所述装置还可以包括：With reference to the second aspect, in a feasible implementation manner, the device may further include:
第二获取模块，用于获取所述用户运动对应的运动参数；以及The second acquisition module is used to acquire the motion parameters corresponding to the user motion; and
第二计算模块，用于根据所述用户运动前的身体状态参数、所述用户运动后的身体状态参数以及所述用户的运动参数计算所述用户的心脏恢复指数及肺恢复指数。The second calculation module is configured to calculate the heart recovery index and lung recovery index of the user according to the physical state parameters of the user before exercise, the physical state parameters of the user after exercise, and the exercise parameters of the user.
结合第二方面，在一种可行的实现方式中，所述第一计算模块可以包括：With reference to the second aspect, in a feasible implementation manner, the first calculation module may include:
第一计算单元，用于根据所述用户运动前的身体状态参数、所述用户在运动后的身体状态参数以及所述用户的运动参数计算所述用户运动后的肌肉疲劳度。The first calculation unit is configured to calculate the muscle fatigue of the user after exercise according to the physical state parameters of the user before exercise, the physical state parameters of the user after exercise, and the exercise parameters of the user.
结合第二方面，在一种可行的实现方式中，所述运动参数包括运动类型、运动强度、运动时长、运动后时长及运动参数可信度中的一种或者任意多种。With reference to the second aspect, in a feasible implementation manner, the exercise parameter includes one or any more of exercise type, exercise intensity, exercise duration, post-exercise duration, and exercise parameter credibility.
结合第二方面，在一种可行的实现方式中，所述第一获取模块或者所述第一确定模块可以包括：With reference to the second aspect, in a feasible implementation manner, the first acquiring module or the first determining module may include:
第一处理单元，用于对所述第一压力信号依次进行高通放大处理和低通滤波处理后得到第一信号，将所述第一信号进行高通滤波处理后得到运动前的心冲击信号，根据所述运动前的心冲击信号确定运动前的心率，根据所述运动前的心冲击信号的波形变化确定所述运动前的心冲击信号的波形轮廓，根据所述运动前的心冲击信号的波形轮廓的峰值特征点计算运动前的呼吸频率以及根据所述运动前的心冲击信号的波形轮廓的峰峰值计算运动前的呼吸强度；以及The first processing unit is configured to sequentially perform high-pass amplification processing and low-pass filtering processing on the first pressure signal to obtain a first signal, perform high-pass filtering processing on the first signal to obtain a pre-exercise cardiac shock signal, according to The cardiac shock signal before exercise determines the heart rate before exercise, the waveform profile of the cardiac shock signal before exercise is determined according to the waveform change of the cardiac shock signal before exercise, and the waveform of the cardiac shock signal before exercise is determined. Calculating the pre-exercise respiration frequency with the peak feature points of the contour and calculating the pre-exercise respiration intensity according to the peak-to-peak value of the waveform contour of the pre-exercise cardiac shock signal; and
第二处理单元，用于对所述第一信号进行低通滤波处理，得到第二信号，计算出所述第二信号的主频、峰峰值及标准差，根据所述第二信号的主频、峰峰值及标准差计算运动前的身体平稳度。The second processing unit is configured to perform low-pass filter processing on the first signal to obtain a second signal, calculate the dominant frequency, peak-to-peak value, and standard deviation of the second signal, according to the dominant frequency of the second signal , Peak-to-peak value and standard deviation to calculate the body stability before exercise.
结合第二方面，在一种可行的实现方式中，所述第二获取模块或者所述第二确定模块可以包括：With reference to the second aspect, in a feasible implementation manner, the second acquiring module or the second determining module may include:
第三处理单元，用于对所述第二压力信号依次进行高通放大处理和低通滤波处理后得到第三信号，将所述第三信号进行高通滤波处理后得到运动后的心冲击信号，根据所述运动后的心冲击信号确定运动后的心率，根据所述运动后的心冲击信号的波形变化确定所述运动后的心冲击信号的波形轮廓，根据所述运动后的心冲击信号的波形轮廓的峰值特征点计算运动后的呼吸频率以及根据所述运动后的心冲击信号的波形轮廓的峰峰值计算运动后的呼吸强度；以及The third processing unit is configured to sequentially perform high-pass amplification processing and low-pass filter processing on the second pressure signal to obtain a third signal, perform high-pass filter processing on the third signal to obtain a post-exercise cardiac shock signal, according to The post-exercise cardiac shock signal determines the post-exercise heart rate, the waveform profile of the post-exercise cardiac shock signal is determined according to the waveform change of the post-exercise cardiac shock signal, and the waveform profile of the post-exercise cardiac shock signal is determined according to the waveform of the post-exercise cardiac shock signal The peak feature points of the contour calculate the post-exercise respiration frequency, and the post-exercise respiration intensity is calculated according to the peak-to-peak value of the waveform contour of the post-exercise cardiac shock signal; and
第四处理单元，用于对所述第三信号进行低通滤波处理，得到第四信号，计算出所述第四信号的主频、峰峰值及标准差，根据所述第四信号的主频、峰峰值及标准差计算运动后的身体平稳度。The fourth processing unit is configured to perform low-pass filtering processing on the third signal to obtain a fourth signal, calculate the main frequency, peak-to-peak value and standard deviation of the fourth signal, and calculate the main frequency of the fourth signal according to the main frequency of the fourth signal. , Peak-to-peak value and standard deviation to calculate the body stability after exercise.
结合第二方面，在一种可行的实现方式中，所述第一计算模块可以包括：With reference to the second aspect, in a feasible implementation manner, the first calculation module may include:
第二计算单元，用于计算所述用户运动前的身体平稳度与所述用户运动后的身体平稳度之间的身体平稳度差值，以及计算所述用户运动前的心率与所述用户运动后的心率之间的心率差值，以及计算所述用户运动前的呼吸频率与所述用户运动后的呼吸频率之间的呼吸频率差值，以及计算所述用户运动前的呼吸强度与所述用户运动后的呼吸强度之间的呼吸强度差值；以及The second calculation unit is used to calculate the body stability difference between the body stability of the user before exercise and the body stability of the user after exercise, and to calculate the heart rate of the user before exercise and the user exercise The heart rate difference between the heart rates after the exercise, and the respiration rate difference between the respiration rate of the user before exercise and the respiration rate of the user after exercise, and the calculation of the respiration intensity of the user before exercise and the The difference in breathing intensity between the breathing intensity of the user after exercise; and
第三计算单元，用于根据所述身体平稳度差值、所述心率差值、所述呼吸频率差值及呼吸强度差值，采用加权平均算法计算所述用户运动后的肌肉疲劳度。The third calculation unit is configured to calculate the muscle fatigue degree of the user after exercise by using a weighted average algorithm according to the difference in body stability, the difference in heart rate, the difference in respiratory frequency, and the difference in respiratory intensity.
在一种可行的实现方式中，所述第二计算单元还可以包括：In a feasible implementation manner, the second calculation unit may further include:
第一计算子单元，用于根据所述身体平稳度差值、所述心率差值、所述呼吸频率差值及所述 呼吸强度差值，并结合所述用户的运动参数，采用加权平均算法计算所述用户运动后的心脏恢复指数及肺恢复指数。The first calculation subunit is configured to adopt a weighted average algorithm according to the difference in body stability, the difference in heart rate, the difference in respiratory frequency, and the difference in respiratory intensity, in combination with the exercise parameters of the user Calculate the heart recovery index and lung recovery index of the user after exercise.
结合第二方面，在一种可行的实现方式中，所述第一计算单元可以包括：With reference to the second aspect, in a feasible implementation manner, the first calculation unit may include:
第二计算子单元，用于计算所述用户运动前的身体平稳度与所述用户运动后的身体平稳度之间的身体平稳度差值，以及计算所述用户运动前的心率与所述用户运动后的心率之间的心率差值，以及计算所述用户运动前的呼吸频率与所述用户运动后的呼吸频率之间的呼吸频率差值，以及计算所述用户运动前的呼吸强度与所述用户运动后的呼吸强度之间的呼吸强度差值；以及The second calculation subunit is used to calculate the difference in body stability between the body stability of the user before exercise and the body stability of the user after exercise, and to calculate the difference between the user’s heart rate before exercise and the user’s The heart rate difference between the heart rates after exercise, and the respiration rate difference between the respiration rate of the user before exercise and the respiration rate of the user after exercise, and the calculation of the respiration intensity of the user before exercise and the total The breathing intensity difference between the breathing intensity of the user after exercise; and
第三计算子单元，用于根据所述身体平稳度差值、所述心率差值、所述呼吸频率差值及所述呼吸强度差值，结合所述用户的运动参数，使用神经网络计算所述用户的肌肉疲劳度、心脏恢复指数及肺恢复指数。The third calculation subunit is used to calculate the difference in body stability, the difference in heart rate, the difference in respiration rate, and the difference in respiration intensity, in combination with the exercise parameters of the user, using a neural network to calculate Describes the user’s muscle fatigue, heart recovery index and lung recovery index.
结合第二方面，在一种可行的实现方式中，所述装置还可以包括：With reference to the second aspect, in a feasible implementation manner, the device may further include:
第一显示模块，用于显示计算得到的所述用户的肌肉疲劳度、心脏恢复指数及肺恢复指数中的任意一个或者任意多个，以及根据所述用户的肌肉疲劳度、心脏恢复指数及肺恢复指数中的一个或者任意多个生成并显示运动量评估、身体机能评估、运动建议、身体恢复建议中的一个或者任意多个。The first display module is used to display any one or more of the calculated muscle fatigue, heart recovery index, and lung recovery index of the user, and according to the user’s muscle fatigue, heart recovery index, and lung recovery index. One or any more of the recovery index generates and displays one or any more of exercise volume evaluation, physical function evaluation, exercise suggestion, and physical recovery suggestion.
第三方面，本申请实施例提供一种电子设备，所述电子设备包括存储器、处理器、触摸传感器及显示屏，所述存储器中存储有计算机程序，所述处理器与所述存储器连接，所述处理器执行计算机程序以实现上述的运动后肌肉疲劳度的检测方法。In a third aspect, an embodiment of the present application provides an electronic device that includes a memory, a processor, a touch sensor, and a display screen. The memory stores a computer program, the processor is connected to the memory, and the The processor executes a computer program to implement the above-mentioned method for detecting muscle fatigue after exercise.
第四方面，本申请实施例提供一种体重测量设备，所述体重测量设备包括存储器、处理器、触摸传感器及显示屏，所述存储器中存储有计算机程序，所述处理器与所述存储器连接，所述处理器执行计算机程序以实现上述的运动后肌肉疲劳度的检测方法。In a fourth aspect, an embodiment of the present application provides a weight measurement device, the weight measurement device includes a memory, a processor, a touch sensor, and a display screen, the memory stores a computer program, and the processor is connected to the memory The processor executes the computer program to realize the above-mentioned method for detecting muscle fatigue after exercise.
第五方面，本申请实施例提供一种计算机可读存储介质，包括计算机指令，当所述计算机指令在电子设备上运行时，使得所述电子设备执行第一方面或者第一方面的任一可能的实现方式中的方法的指令。In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, including computer instructions, which, when the computer instructions run on an electronic device, cause the electronic device to perform the first aspect or any one of the first aspect. The instruction of the method in the implementation mode.
第六方面，本申请实施例提供一种计算机程序产品，当所述计算机程序产品在计算机上运行时，使得所述计算机执行第一方面或者第一方面的任一可能的实现方式中的方法的指令。In a sixth aspect, the embodiments of the present application provide a computer program product, which when the computer program product runs on a computer, causes the computer to execute the method in the first aspect or any possible implementation of the first aspect instruction.
可以理解，本可以理解，本发明实施例通过获取用户运动前的身体状态参数以及运动后的身体状态参数，然后根据用户运动前的身体状态参数以及用户在运动后的身体状态参数计算用户运动后的肌肉疲劳度，无需采集用户的血液且操作简单，用户接受度高，同时，依靠体重测量设备就能够实现肌肉疲劳度检测，无需专门的检测设备，同时本发明实施例提供的检测方法基于压力检测，无需更改硬件产品形态。It can be understood that the embodiment of the present invention obtains the body state parameters of the user before exercise and the body state parameters after exercise, and then calculates the user’s post-exercise based on the user’s body state parameters before exercise and the user’s body state parameters after exercise. The muscle fatigue is not required to collect the user’s blood, the operation is simple, and the user’s acceptance is high. At the same time, the muscle fatigue detection can be achieved by relying on the weight measurement device without special detection equipment. At the same time, the detection method provided by the embodiment of the present invention is based on pressure Detection without changing the form of hardware products.
附图说明Description of the drawings
图1为本申请实施例提供的电子设备的结构示意图；FIG. 1 is a schematic structural diagram of an electronic device provided by an embodiment of the application;
图2为本发明实施例的电子设备的软件结构框图；2 is a block diagram of the software structure of an electronic device according to an embodiment of the present invention;
图3为本发明实施例提供的一种运动后肌肉疲劳度的检测方法的流程图；3 is a flowchart of a method for detecting muscle fatigue after exercise provided by an embodiment of the present invention;
图4为本发明实施例提供的运动后肌肉疲劳度的检测方法的一种示例性的应用框架图；4 is an exemplary application framework diagram of a method for detecting muscle fatigue after exercise provided by an embodiment of the present invention;
图5为本发明实施例提供的一种示例性的用户运动前身体状态参数的检测流程图；FIG. 5 is an exemplary detection flow chart of a user's body state parameter before exercise according to an embodiment of the present invention;
图6为本发明实施例提供的一种示例性的用户运动后检测肌肉疲劳度、心脏恢复指数和肺恢复指数的流程图；FIG. 6 is an exemplary flow chart for detecting muscle fatigue, heart recovery index, and lung recovery index after exercise of a user according to an embodiment of the present invention;
图7为本发明实施例提供的一种示例性的测量用户运动前/运动后的身体状态参数的示意图；FIG. 7 is an exemplary schematic diagram of measuring a user's body state parameters before/after exercise according to an embodiment of the present invention;
图8为本发明实施例提供的一种通过神经网络计算肌肉疲劳度、心脏恢复指数和肺恢复指数的示意图；8 is a schematic diagram of calculating muscle fatigue, heart recovery index, and lung recovery index through neural network according to an embodiment of the present invention;
图9为本发明实施例提供的一种示例性的运动后肌肉疲劳度的检测装置运动前和运动后的状态选择界面；9 is a state selection interface before and after exercise of an exemplary device for detecting muscle fatigue after exercise provided by an embodiment of the present invention;
图10本发明实施例提供的一种示例性的运动参数自动获取的设置界面；Fig. 10 is an exemplary setting interface for automatic acquisition of sports parameters provided by an embodiment of the present invention;
图11本发明实施例提供的一种示例性的运动参数手动输入选择界面；Fig. 11 is an exemplary manual input selection interface for sports parameters provided by an embodiment of the present invention;
图12本发明实施例提供的一种示例性的运动参数输入界面；Fig. 12 is an exemplary motion parameter input interface provided by an embodiment of the present invention;
图13本发明实施例提供的又一种示例性的运动参数输入界面；Fig. 13 is yet another exemplary motion parameter input interface provided by the embodiment of the present invention;
图14为本发明实施例提供的一种示例性的测量结果显示界面；FIG. 14 is an exemplary measurement result display interface provided by an embodiment of the present invention;
图15为本申请实施例提供的运动后肌肉疲劳度的检测装置的示意图。FIG. 15 is a schematic diagram of a device for detecting muscle fatigue after exercise provided by an embodiment of the application.
具体实施方式Detailed ways
为了更好的理解本发明的技术方案，下面结合附图对本申请实施例进行详细描述。In order to better understand the technical solutions of the present invention, the embodiments of the present application will be described in detail below with reference to the accompanying drawings.
应当明确，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其它实施例，都属于本发明保护的范围。It should be clear that the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.
本申请中，“至少一个”是指一个或者多个，“多个”是指两个或两个以上。“和/或”，描述关联对象的关联关系，表示可以存在三种关系，例如，A和/或B，可以表示：单独存在A，同时存在A和B，单独存在B的情况，其中A，B可以是单数或者复数。字符“/”一般表示前后关联对象是一种“或”的关系。“以下至少一项(个)”或其类似表达，是指的这些项中的任意组合，包括单项(个)或复数项(个)的任意组合。例如，a，b，或c中的至少一项(个)，可以表示：a，b，c，a-b，a-c，b-c，或a-b-c，其中a，b，c可以是单个，也可以是多个。In this application, "at least one" refers to one or more, and "multiple" refers to two or more. "And/or" describes the association relationship of the associated objects, indicating that there can be three relationships, for example, A and/or B, which can mean: A alone exists, A and B exist at the same time, and B exists alone, where A, B can be singular or plural. The character "/" generally indicates that the associated objects before and after are in an "or" relationship. "The following at least one item (a)" or similar expressions refers to any combination of these items, including any combination of a single item (a) or a plurality of items (a). For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple .
本申请实施例提供的用户运动后肌肉疲劳度的检测方法可以应用于如图1所示的电子设备100，图1所示的电子设备100可以是还包含其他功能诸如个人数字助理和/或音乐播放器功能的便携式电子设备，诸如手机、平板电脑、具备无线通讯功能的可穿戴设备(如智能手表)等。上述便携式电子设备诸如具有触控面板的膝上型计算机等。The method for detecting muscle fatigue of a user after exercise provided by the embodiments of the present application can be applied to the electronic device 100 shown in FIG. 1, and the electronic device 100 shown in FIG. Portable electronic devices with player functions, such as mobile phones, tablet computers, wearable devices with wireless communication functions (such as smart watches), etc. The above-mentioned portable electronic devices are, for example, laptop computers with touch panels.
在一些实施例中，电子设备100包括直面显示屏、曲面显示屏或可折叠显示屏。电子设备100通过采集用户持握电子设备100时预设区域的触摸点，并将触摸点上传至云端服务器200，云端服务器200根据触摸点判断用户的持握姿态，并将持握姿态反馈给电子设备100。在其他实施例中，电子终端100也可以自己根据触摸点判断用户的持握姿态，在此不做限定。In some embodiments, the electronic device 100 includes a straight-face display screen, a curved display screen, or a foldable display screen. The electronic device 100 collects the touch points in a preset area when the user holds the electronic device 100, and uploads the touch points to the cloud server 200. The cloud server 200 determines the user's holding posture according to the touch points, and feeds back the holding posture to the electronic device. Equipment 100. In other embodiments, the electronic terminal 100 can also determine the user's holding posture based on the touch point, which is not limited herein.
如图1所示，下面以电子设备100为例对实施例进行具体说明。As shown in FIG. 1, the electronic device 100 is taken as an example to describe the embodiments in detail.
电子设备100可以包括处理器110，外部存储器接口120，内部存储器121，通用串行总线(universal serial bus，USB)接口130，充电管理模块140，电源管理模块141，电池142，天线1，天线2，移动通信模块150，无线通信模块160，音频模块170，扬声器170A，受话器170B，麦克风170C，耳机接口170D，传感器模块180，按键190，马达191，指示器192，摄像头193，显示屏194，以及用户标识模块(subscriber identification module，SIM)卡接口195等。其中传感器模块180可以包括压力传感器180A，陀螺仪传感器180B，气压传感器180C，磁传感器180D，加速度传感器180E，距离传感器180F，接近光传感器180G，指纹传感器180H，温度传感器180J，触摸传感器180K，环境光传感器180L，骨传导传感器180M等。The electronic device 100 may include a processor 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, and an antenna 2. , Mobile communication module 150, wireless communication module 160, audio module 170, speaker 170A, receiver 170B, microphone 170C, earphone jack 170D, sensor module 180, buttons 190, motor 191, indicator 192, camera 193, display screen 194, and Subscriber identification module (subscriber identification module, SIM) card interface 195, etc. The sensor module 180 may include a pressure sensor 180A, a gyroscope sensor 180B, an air pressure sensor 180C, a magnetic sensor 180D, an acceleration sensor 180E, a distance sensor 180F, a proximity sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, and ambient light Sensor 180L, bone conduction sensor 180M, etc.
可以理解的是，本申请实施例示意的结构并不构成对电子设备100的具体限定。在本申请另一些实施例中，电子设备100可以包括比图示更多或更少的部件，或者组合某些部件，或者拆分某些部件，或者不同的部件布置。图示的部件可以以硬件，软件或软件和硬件的组合实现。It can be understood that the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the electronic device 100. In other embodiments of the present application, the electronic device 100 may include more or fewer components than those shown in the figure, or combine certain components, or split certain components, or arrange different components. The illustrated components can be implemented in hardware, software, or a combination of software and hardware.
处理器110可以包括一个或多个处理单元，例如：处理器110可以包括应用处理器(application processor，AP)，调制解调处理器，图形处理器(graphics processing unit，GPU)，图像信号处理器(image signal processor，ISP)，控制器，视频编解码器，数字信号处理器(digital signal processor，DSP)，基带处理器，和/或神经网络处理器(neural-network processing unit，NPU)等。其中，不同的处理单元可以是独立的器件，也可以集成在一个或多个处理器中。The processor 110 may include one or more processing units. For example, the processor 110 may include an application processor (AP), a modem processor, a graphics processing unit (GPU), and an image signal processor. (image signal processor, ISP), controller, video codec, digital signal processor (digital signal processor, DSP), baseband processor, and/or neural-network processing unit (NPU), etc. Among them, the different processing units may be independent devices or integrated in one or more processors.
控制器可以根据指令操作码和时序信号，产生操作控制信号，完成取指令和执行指令的控制。The controller can generate operation control signals according to the instruction operation code and timing signals to complete the control of fetching instructions and executing instructions.
处理器110中还可以设置存储器，用于存储计算机程序和数据。在一些实施例中，处理器110中的存储器为高速缓冲存储器。该存储器可以保存处理器110刚用过或循环使用的指令或数据。如果处理器110需要再次使用该指令或数据，可从所述存储器中直接调用。避免了重复存取，减少了处理器110的等待时间，因而提高了***的效率。A memory may also be provided in the processor 110 for storing computer programs and data. In some embodiments, the memory in the processor 110 is a cache memory. The memory can store instructions or data that have just been used or recycled by the processor 110. If the processor 110 needs to use the instruction or data again, it can be directly called from the memory. Repeated accesses are avoided, the waiting time of the processor 110 is reduced, and the efficiency of the system is improved.
在一些实施例中，处理器110可以包括一个或多个接口。接口可以包括集成电路(inter-integrated circuit，I2C)接口，集成电路内置音频(inter-integrated circuit sound，I2S)接口，脉冲编码调制(pulse code modulation，PCM)接口，通用异步收发传输器(universal asyncHRonous receiver/transmitter，UART)接口，移动产业处理器接口(moBIle industry processor interface，MIPI)，通用输入输出(general-purpose input/output，GPIO)接口，用户标识模块(subscriber identity module，SIM)接口，和/或通用串行总线(universal serial bus，USB)接口等。In some embodiments, the processor 110 may include one or more interfaces. The interface can include an integrated circuit (inter-integrated circuit, I2C) interface, an integrated circuit built-in audio (inter-integrated circuit sound, I2S) interface, a pulse code modulation (pulse code modulation, PCM) interface, and a universal asynchronous transmitter (universal asyncHRonous) interface. receiver/transmitter, UART) interface, mobile industry processor interface (MIPI), general-purpose input/output (GPIO) interface, subscriber identity module (SIM) interface, and / Or Universal Serial Bus (USB) interface, etc.
I2C接口是一种双向同步串行总线，包括一根串行数据线(serial data line，SDA)和一根串行时钟线(derail clock line，SCL)。在一些实施例中，处理器110可以包含多组I2C总线。处理器110可以通过不同的I2C总线接口分别耦合触摸传感器180K，充电器，闪光灯，摄像头193等。例如：处理器110可以通过I2C接口耦合触摸传感器180K，使处理器110与触摸传感器180K通过I2C总线接口通信，实现电子设备100的触摸功能。The I2C interface is a bidirectional synchronous serial bus, including a serial data line (SDA) and a serial clock line (SCL). In some embodiments, the processor 110 may include multiple sets of I2C buses. The processor 110 may be coupled to the touch sensor 180K, charger, flash, camera 193, etc., respectively through different I2C bus interfaces. For example, the processor 110 may couple the touch sensor 180K through an I2C interface, so that the processor 110 and the touch sensor 180K communicate through the I2C bus interface to implement the touch function of the electronic device 100.
I2S接口可以用于音频通信。在一些实施例中，处理器110可以包含多组I2S总线。处理器110可以通过I2S总线与音频模块170耦合，实现处理器110与音频模块170之间的通信。在一些实施例中，音频模块170可以通过I2S接口向无线通信模块160传递音频信号，实现通过蓝牙耳机接听电话的功能。The I2S interface can be used for audio communication. In some embodiments, the processor 110 may include multiple sets of I2S buses. The processor 110 may be coupled with the audio module 170 through an I2S bus to implement communication between the processor 110 and the audio module 170. In some embodiments, the audio module 170 may transmit audio signals to the wireless communication module 160 through an I2S interface, so as to realize the function of answering calls through a Bluetooth headset.
PCM接口也可以用于音频通信，将模拟信号抽样，量化和编码。在一些实施例中，音频模块170与无线通信模块160可以通过PCM总线接口耦合。在一些实施例中，音频模块170也可以通过PCM接口向无线通信模块160传递音频信号，实现通过蓝牙耳机接听电话的功能。所述I2S接口和所述PCM接口都可以用于音频通信。The PCM interface can also be used for audio communication to sample, quantize and encode analog signals. In some embodiments, the audio module 170 and the wireless communication module 160 may be coupled through a PCM bus interface. In some embodiments, the audio module 170 may also transmit audio signals to the wireless communication module 160 through the PCM interface, so as to realize the function of answering calls through the Bluetooth headset. Both the I2S interface and the PCM interface can be used for audio communication.
UART接口是一种通用串行数据总线，用于异步通信。该总线可以为双向通信总线。它将要传输的数据在串行通信与并行通信之间转换。在一些实施例中，UART接口通常被用于连接处理器110与无线通信模块160。例如：处理器110通过UART接口与无线通信模块160中的蓝牙模块通信，实现蓝牙功能。在一些实施例中，音频模块170可以通过UART接口向无线通信模块160传递音频信号，实现通过蓝牙耳机播放音乐的功能。The UART interface is a universal serial data bus used for asynchronous communication. The bus can be a two-way communication bus. It converts the data to be transmitted between serial communication and parallel communication. In some embodiments, the UART interface is generally used to connect the processor 110 and the wireless communication module 160. For example, the processor 110 communicates with the Bluetooth module in the wireless communication module 160 through the UART interface to realize the Bluetooth function. In some embodiments, the audio module 170 may transmit audio signals to the wireless communication module 160 through a UART interface, so as to realize the function of playing music through a Bluetooth headset.
MIPI接口可以被用于连接处理器110与显示屏194，摄像头193等***器件。MIPI接口包括摄像头串行接口(camera serial interface，CSI)，显示屏串行接口(display serial interface，DSI)等。 在一些实施例中，处理器110和摄像头193通过CSI接口通信，实现电子设备100的拍摄功能。处理器110和显示屏194通过DSI接口通信，实现电子设备100的显示功能。The MIPI interface can be used to connect the processor 110 with the display screen 194, the camera 193 and other peripheral devices. The MIPI interface includes a camera serial interface (camera serial interface, CSI), a display serial interface (display serial interface, DSI), and so on. In some embodiments, the processor 110 and the camera 193 communicate through a CSI interface to implement the shooting function of the electronic device 100. The processor 110 and the display screen 194 communicate through a DSI interface to realize the display function of the electronic device 100.
GPIO接口可以通过软件配置。GPIO接口可以被配置为控制信号，也可被配置为数据信号。在一些实施例中，GPIO接口可以用于连接处理器110与摄像头193，显示屏194，无线通信模块160，音频模块170，传感器模块180等。GPIO接口还可以被配置为I2C接口，I2S接口，UART接口，MIPI接口等。The GPIO interface can be configured through software. The GPIO interface can be configured as a control signal or as a data signal. In some embodiments, the GPIO interface can be used to connect the processor 110 with the camera 193, the display screen 194, the wireless communication module 160, the audio module 170, the sensor module 180, and so on. The GPIO interface can also be configured as an I2C interface, I2S interface, UART interface, MIPI interface, etc.
USB接口130是符合USB标准规范的接口，具体可以是Mini USB接口，Micro USB接口，USB Type C接口等。USB接口130可以用于连接充电器为电子设备100充电，也可以用于电子设备100与***设备之间传输数据。也可以用于连接耳机，通过耳机播放音频。该接口还可以用于连接其他电子设备，例如AR设备等。The USB interface 130 is an interface that complies with the USB standard specification, and specifically may be a Mini USB interface, a Micro USB interface, a USB Type C interface, and so on. The USB interface 130 can be used to connect a charger to charge the electronic device 100, and can also be used to transfer data between the electronic device 100 and peripheral devices. It can also be used to connect earphones and play audio through earphones. This interface can also be used to connect other electronic devices, such as AR devices.
可以理解的是，本申请实施例示意的各模块间的接口连接关系，只是示意性说明，并不构成对电子设备100的结构限定。在本申请另一些实施例中，电子设备100也可以采用上述实施例中不同的接口连接方式，或多种接口连接方式的组合。It can be understood that the interface connection relationship between the modules illustrated in the embodiment of the present application is merely a schematic description, and does not constitute a structural limitation of the electronic device 100. In other embodiments of the present application, the electronic device 100 may also adopt different interface connection modes in the foregoing embodiments, or a combination of multiple interface connection modes.
充电管理模块140用于从充电器接收充电输入。其中，充电器可以是无线充电器，也可以是有线充电器。在一些有线充电的实施例中，充电管理模块140可以通过USB接口130接收有线充电器的充电输入。在一些无线充电的实施例中，充电管理模块140可以通过电子设备100的无线充电线圈接收无线充电输入。充电管理模块140为电池142充电的同时，还可以通过电源管理模块141为电子设备100供电。The charging management module 140 is used to receive charging input from the charger. Among them, the charger can be a wireless charger or a wired charger. In some wired charging embodiments, the charging management module 140 may receive the charging input of the wired charger through the USB interface 130. In some embodiments of wireless charging, the charging management module 140 may receive the wireless charging input through the wireless charging coil of the electronic device 100. While the charging management module 140 charges the battery 142, it can also supply power to the electronic device 100 through the power management module 141.
电源管理模块141用于连接电池142，充电管理模块140与处理器110。电源管理模块141接收电池142和/或充电管理模块140的输入，为处理器110，内部存储器121，显示屏194，摄像头193，和无线通信模块160等供电。电源管理模块141还可以用于监测电池容量，电池循环次数，电池健康状态(漏电，阻抗)等参数。在其他一些实施例中，电源管理模块141也可以设置于处理器110中。在另一些实施例中，电源管理模块141和充电管理模块140也可以设置于同一个器件中。The power management module 141 is used to connect the battery 142, the charging management module 140 and the processor 110. The power management module 141 receives input from the battery 142 and/or the charge management module 140, and supplies power to the processor 110, the internal memory 121, the display screen 194, the camera 193, and the wireless communication module 160. The power management module 141 can also be used to monitor parameters such as battery capacity, battery cycle times, and battery health status (leakage, impedance). In some other embodiments, the power management module 141 may also be provided in the processor 110. In other embodiments, the power management module 141 and the charging management module 140 may also be provided in the same device.
电子设备100的无线通信功能可以通过天线1，天线2，移动通信模块150，无线通信模块160，调制解调处理器以及基带处理器等实现。The wireless communication function of the electronic device 100 can be implemented by the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, the modem processor, and the baseband processor.
天线1和天线2用于发射和接收电磁波信号。电子设备100中的每个天线可用于覆盖单个或多个通信频带。不同的天线还可以复用，以提高天线的利用率。例如：可以将天线1复用为无线局域网的分集天线。在另外一些实施例中，天线可以和调谐开关结合使用。The antenna 1 and the antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in the electronic device 100 can be used to cover a single or multiple communication frequency bands. Different antennas can also be reused to improve antenna utilization. For example: Antenna 1 can be multiplexed as a diversity antenna of a wireless local area network. In other embodiments, the antenna can be used in combination with a tuning switch.
移动通信模块150可以提供应用在电子设备100上的包括2G/3G/4G/5G等无线通信的解决方案。移动通信模块150可以包括至少一个滤波器，开关，功率放大器，低噪声放大器(low noise amplifier，LNA)等。移动通信模块150可以由天线1接收电磁波，并对接收的电磁波进行滤波，放大等处理，传送至调制解调处理器进行解调。移动通信模块150还可以对经调制解调处理器调制后的信号放大，经天线1转为电磁波辐射出去。在一些实施例中，移动通信模块150的至少部分功能模块可以被设置于处理器110中。在一些实施例中，移动通信模块150的至少部分功能模块可以与处理器110的至少部分模块被设置在同一个器件中。The mobile communication module 150 can provide a wireless communication solution including 2G/3G/4G/5G and the like applied to the electronic device 100. The mobile communication module 150 may include at least one filter, a switch, a power amplifier, a low noise amplifier (LNA), and the like. The mobile communication module 150 can receive electromagnetic waves by the antenna 1, and perform processing such as filtering, amplifying and transmitting the received electromagnetic waves to the modem processor for demodulation. The mobile communication module 150 can also amplify the signal modulated by the modem processor, and convert it into electromagnetic waves for radiation via the antenna 1. In some embodiments, at least part of the functional modules of the mobile communication module 150 may be provided in the processor 110. In some embodiments, at least part of the functional modules of the mobile communication module 150 and at least part of the modules of the processor 110 may be provided in the same device.
调制解调处理器可以包括调制器和解调器。其中，调制器用于将待发送的低频基带信号调制成中高频信号。解调器用于将接收的电磁波信号解调为低频基带信号。随后解调器将解调得到的低频基带信号传送至基带处理器处理。低频基带信号经基带处理器处理后，被传递给应用处理器。 应用处理器通过音频设备(不限于扬声器170A，受话器170B等)输出声音信号，或通过显示屏194显示图像或视频。在一些实施例中，调制解调处理器可以是独立的器件。在另一些实施例中，调制解调处理器可以独立于处理器110，与移动通信模块150或其他功能模块设置在同一个器件中。The modem processor may include a modulator and a demodulator. Among them, the modulator is used to modulate the low frequency baseband signal to be sent into a medium and high frequency signal. The demodulator is used to demodulate the received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then transmits the demodulated low-frequency baseband signal to the baseband processor for processing. After the low-frequency baseband signal is processed by the baseband processor, it is passed to the application processor. The application processor outputs a sound signal through an audio device (not limited to the speaker 170A, the receiver 170B, etc.), or displays an image or video through the display screen 194. In some embodiments, the modem processor may be an independent device. In other embodiments, the modem processor may be independent of the processor 110 and be provided in the same device as the mobile communication module 150 or other functional modules.
无线通信模块160可以提供应用在电子设备100上的包括无线局域网(wireless local area networks，WLAN)(如无线保真(wireless fidelity，Wi-Fi)网络)，蓝牙(bluetooth，BT)，全球导航卫星***(gloBAl navigation satellite system，GNSS)，调频(frequency modulation，FM)，近距离无线通信技术(near field communication，NFC)，红外技术(infrared，IR)等无线通信的解决方案。无线通信模块160可以是集成至少一个通信处理模块的一个或多个器件。无线通信模块160经由天线2接收电磁波，将电磁波信号调频以及滤波处理，将处理后的信号发送到处理器110。无线通信模块160还可以从处理器110接收待发送的信号，对其进行调频，放大，经天线2转为电磁波辐射出去。The wireless communication module 160 can provide applications on the electronic device 100 including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), bluetooth (BT), and global navigation satellites. System (gloBAl navigation satellite system, GNSS), frequency modulation (frequency modulation, FM), near field communication technology (near field communication, NFC), infrared technology (infrared, IR) and other wireless communication solutions. The wireless communication module 160 may be one or more devices integrating at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via the antenna 2, frequency modulates and filters the electromagnetic wave signals, and sends the processed signals to the processor 110. The wireless communication module 160 may also receive a signal to be sent from the processor 110, perform frequency modulation, amplify, and convert it into electromagnetic waves to radiate through the antenna 2.
在一些实施例中，电子设备100的天线1和移动通信模块150耦合，天线2和无线通信模块160耦合，使得电子设备100可以通过无线通信技术与网络以及其他设备通信。所述无线通信技术可以包括全球移动通讯***(gloBAl system for moBIle communications，GSM)，通用分组无线服务(general packet radio service，GPRS)，码分多址接入(code division multiple access，CDMA)，宽带码分多址(wideBAnd code division multiple access，WCDMA)，时分码分多址(time-division code division multiple access，TD-SCDMA)，长期演进(long term evolution，LTE)，BT，GNSS，WLAN，NFC，FM，和/或IR技术等。所述GNSS可以包括全球卫星定位***(gloBAl positioning system，GPS)，全球导航卫星***(gloBAl navigation satellite system，GLONASS)，北斗卫星导航***(beidou navigation satellite system，BDS)，准天顶卫星***(quasi-zenith satellite system，QZSS)和/或星基增强***(satellite BAsed augmentation systems，SBAS)。In some embodiments, the antenna 1 of the electronic device 100 is coupled with the mobile communication module 150, and the antenna 2 is coupled with the wireless communication module 160, so that the electronic device 100 can communicate with the network and other devices through wireless communication technology. The wireless communication technology may include global system for mobile communications (GSM), general packet radio service (GPRS), code division multiple access (CDMA), broadband Code division multiple access (wideBAnd code division multiple access, WCDMA), time-division code division multiple access (TD-SCDMA), long term evolution (LTE), BT, GNSS, WLAN, NFC , FM, and/or IR technology, etc. The GNSS may include global satellite positioning system (gloBAl positioning system, GPS), global navigation satellite system (gloBAl navigation satellite system, GLONASS), Beidou navigation satellite system (BDS), quasi-zenith satellite system (quasi -zenith satellite system, QZSS) and/or satellite BAsed augmentation systems (SBAS).
电子设备100通过GPU，显示屏194，以及应用处理器等实现显示功能。GPU为图像处理的微处理器，连接显示屏194和应用处理器。GPU用于执行数学和几何计算，用于图形渲染。处理器110可包括一个或多个GPU，其执行程序指令以生成或改变显示信息。The electronic device 100 implements a display function through a GPU, a display screen 194, an application processor, and the like. The GPU is an image processing microprocessor, which is connected to the display screen 194 and the application processor. The GPU is used to perform mathematical and geometric calculations and is used for graphics rendering. The processor 110 may include one or more GPUs that execute program instructions to generate or change display information.
显示屏194用于显示图像，视频等。显示屏194包括显示面板。显示面板可以采用液晶显示屏(liquid crystal display，LCD)，有机发光二极管(organic light-emitting diode，OLED)，有源矩阵有机发光二极体或主动矩阵有机发光二极体(active-matrix organic light emitting diode的，AMOLED)，柔性发光二极管(flex light-emitting diode，FLED)，Miniled，MicroLed，Micro-oLed，量子点发光二极管(quantum dot light emitting diodes，QLED)等。在一些实施例中，电子设备100可以包括1个或N个显示屏194，N为大于1的正整数。在本实施例中，显示屏194为曲面显示屏或可折叠显示屏。The display screen 194 is used to display images, videos, and the like. The display screen 194 includes a display panel. The display panel can use liquid crystal display (LCD), organic light-emitting diode (OLED), active matrix organic light-emitting diode or active-matrix organic light-emitting diode (active-matrix organic light-emitting diode). AMOLED, flexible light-emitting diode (FLED), Miniled, MicroLed, Micro-oLed, quantum dot light-emitting diode (QLED), etc. In some embodiments, the electronic device 100 may include one or N display screens 194, and N is a positive integer greater than one. In this embodiment, the display screen 194 is a curved display screen or a foldable display screen.
电子设备100可以通过ISP，摄像头193，视频编解码器，GPU，显示屏194以及应用处理器等实现拍摄功能。The electronic device 100 can implement a shooting function through an ISP, a camera 193, a video codec, a GPU, a display screen 194, and an application processor.
ISP用于处理摄像头193反馈的数据。例如，拍照时，打开快门，光线通过镜头被传递到摄像头感光元件上，光信号转换为电信号，摄像头感光元件将所述电信号传递给ISP处理，转化为肉眼可见的图像。ISP还可以对图像的噪点，亮度，肤色进行算法优化。ISP还可以对拍摄场景的曝光，色温等参数优化。在一些实施例中，ISP可以设置在摄像头193中。The ISP is used to process the data fed back from the camera 193. For example, when taking a picture, the shutter is opened, the light is transmitted to the photosensitive element of the camera through the lens, the light signal is converted into an electrical signal, and the photosensitive element of the camera transmits the electrical signal to the ISP for processing and is converted into an image visible to the naked eye. ISP can also optimize the image noise, brightness, and skin color. ISP can also optimize the exposure, color temperature and other parameters of the shooting scene. In some embodiments, the ISP may be provided in the camera 193.
摄像头193用于捕获静态图像或视频。物体通过镜头生成光学图像投射到感光元件。感光元件可以是电荷耦合器件(charge coupled device，CCD)或互补金属氧化物半导体(complementary  metal-oxide-semiconductor，CMOS)光电晶体管。感光元件把光信号转换成电信号，之后将电信号传递给ISP转换成数字图像信号。ISP将数字图像信号输出到DSP加工处理。DSP将数字图像信号转换成标准的RGB，YUV等格式的图像信号。在一些实施例中，电子设备100可以包括1个或N个摄像头193，N为大于1的正整数。The camera 193 is used to capture still images or videos. The object generates an optical image through the lens and is projected to the photosensitive element. The photosensitive element may be a charge coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the optical signal into an electrical signal, and then transfers the electrical signal to the ISP to convert it into a digital image signal. ISP outputs digital image signals to DSP for processing. DSP converts digital image signals into standard RGB, YUV and other formats of image signals. In some embodiments, the electronic device 100 may include one or N cameras 193, and N is a positive integer greater than one.
数字信号处理器用于处理数字信号，除了可以处理数字图像信号，还可以处理其他数字信号。例如，当电子设备100在频点选择时，数字信号处理器用于对频点能量进行傅里叶变换等。Digital signal processors are used to process digital signals. In addition to digital image signals, they can also process other digital signals. For example, when the electronic device 100 selects the frequency point, the digital signal processor is used to perform Fourier transform on the energy of the frequency point.
视频编解码器用于对数字视频压缩或解压缩。电子设备100可以支持一种或多种视频编解码器。这样，电子设备100可以播放或录制多种编码格式的视频，例如：动态图像专家组(moving picture experts group，MPEG)1，MPEG2，MPEG3，MPEG4等。Video codecs are used to compress or decompress digital video. The electronic device 100 may support one or more video codecs. In this way, the electronic device 100 can play or record videos in multiple encoding formats, such as: moving picture experts group (MPEG) 1, MPEG2, MPEG3, MPEG4, and so on.
NPU为神经网络(neural-network，NN)计算处理器，通过借鉴生物神经网络结构，例如借鉴人脑神经元之间传递模式，对输入信息快速处理，还可以不断的自学习。通过NPU可以实现电子设备100的智能认知等应用，例如：图像识别，人脸识别，语音识别，文本理解等。NPU is a neural-network (NN) computing processor. By drawing on the structure of biological neural networks, for example, the transfer mode between human brain neurons, it can quickly process input information, and it can also continuously self-learn. Through the NPU, applications such as intelligent cognition of the electronic device 100 can be realized, such as image recognition, face recognition, voice recognition, text understanding, and so on.
外部存储器接口120可以用于连接外部存储卡，例如Micro SD卡，实现扩展电子设备100的存储能力。外部存储卡通过外部存储器接口120与处理器110通信，实现数据存储功能。例如将音乐，视频等文件保存在外部存储卡中。The external memory interface 120 may be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the electronic device 100. The external memory card communicates with the processor 110 through the external memory interface 120 to realize the data storage function. For example, save music, video and other files in an external memory card.
内部存储器121可以用于存储计算机可执行程序代码，所述可执行程序代码包括指令。内部存储器121可以包括存储程序区和存储数据区。其中，存储程序区可存储操作***，至少一个功能所需的应用程序(比如声音播放功能，图像播放功能等)等。存储数据区可存储电子设备100使用过程中所创建的数据(比如音频数据，电话本等)等。此外，内部存储器121可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件，闪存器件，通用闪存存储器(universal flash storage，UFS)等。处理器110通过运行存储在内部存储器121的指令，和/或存储在设置于处理器中的存储器的指令，执行电子设备100的各种功能应用以及数据处理。The internal memory 121 may be used to store computer executable program code, where the executable program code includes instructions. The internal memory 121 may include a storage program area and a storage data area. Among them, the storage program area can store an operating system, an application program (such as a sound playback function, an image playback function, etc.) required by at least one function, and the like. The data storage area can store data (such as audio data, phone book, etc.) created during the use of the electronic device 100. In addition, the internal memory 121 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, a universal flash storage (UFS), and the like. The processor 110 executes various functional applications and data processing of the electronic device 100 by running instructions stored in the internal memory 121 and/or instructions stored in a memory provided in the processor.
电子设备100可以通过音频模块170，扬声器170A，受话器170B，麦克风170C，耳机接口170D，以及应用处理器等实现音频功能。例如音乐播放，录音等。The electronic device 100 can implement audio functions through the audio module 170, the speaker 170A, the receiver 170B, the microphone 170C, the earphone interface 170D, and the application processor. For example, music playback, recording, etc.
音频模块170用于将数字音频信息转换成模拟音频信号输出，也用于将模拟音频输入转换为数字音频信号。音频模块170还可以用于对音频信号编码和解码。在一些实施例中，音频模块170可以设置于处理器110中，或将音频模块170的部分功能模块设置于处理器110中。The audio module 170 is used to convert digital audio information into an analog audio signal for output, and is also used to convert an analog audio input into a digital audio signal. The audio module 170 can also be used to encode and decode audio signals. In some embodiments, the audio module 170 may be provided in the processor 110, or part of the functional modules of the audio module 170 may be provided in the processor 110.
扬声器170A，也称“喇叭”，用于将音频电信号转换为声音信号。电子设备100可以通过扬声器170A收听音乐，或收听免提通话。The speaker 170A, also called "speaker", is used to convert audio electrical signals into sound signals. The electronic device 100 can listen to music through the speaker 170A, or listen to a hands-free call.
受话器170B，也称“听筒”，用于将音频电信号转换成声音信号。当电子设备100接听电话或语音信息时，可以通过将受话器170B靠近人耳接听语音。The receiver 170B, also called "earpiece", is used to convert audio electrical signals into sound signals. When the electronic device 100 answers a call or voice message, it can receive the voice by bringing the receiver 170B close to the human ear.
麦克风170C，也称“话筒”，“传声器”，用于将声音信号转换为电信号。当拨打电话或发送语音信息时，用户可以通过人嘴靠近麦克风170C发声，将声音信号输入到麦克风170C。电子设备100可以设置至少一个麦克风170C。在另一些实施例中，电子设备100可以设置两个麦克风170C，除了采集声音信号，还可以实现降噪功能。在另一些实施例中，电子设备100还可以设置三个，四个或更多麦克风170C，实现采集声音信号，降噪，还可以识别声音来源，实现定向录音功能等。The microphone 170C, also called "microphone", "microphone", is used to convert sound signals into electrical signals. When making a call or sending a voice message, the user can make a sound by approaching the microphone 170C through the human mouth, and input the sound signal into the microphone 170C. The electronic device 100 may be provided with at least one microphone 170C. In other embodiments, the electronic device 100 may be provided with two microphones 170C, which can implement noise reduction functions in addition to collecting sound signals. In other embodiments, the electronic device 100 may also be provided with three, four or more microphones 170C to collect sound signals, reduce noise, identify sound sources, and realize directional recording functions.
耳机接口170D用于连接有线耳机。耳机接口170D可以是USB接口130，也可以是3.5mm的开放移动电子设备平台(open moBIle terminal platform，OMTP)标准接口，美国蜂窝电信工业协 会(cellular telecommunications industry association of the USA，CTIA)标准接口。The earphone interface 170D is used to connect wired earphones. The earphone interface 170D may be a USB interface 130, or a 3.5mm open mobile terminal platform (OMTP) standard interface, and a cellular telecommunications industry association of the USA (CTIA) standard interface.
压力传感器180A用于感受压力信号，可以将压力信号转换成电信号。在一些实施例中，压力传感器180A可以设置于显示屏194。压力传感器180A的种类很多，如电阻式压力传感器，电感式压力传感器，电容式压力传感器等。电容式压力传感器可以是包括至少两个具有导电材料的平行板。当有力作用于压力传感器180A，电极之间的电容改变。电子设备100根据电容的变化确定压力的强度。当有触摸操作作用于显示屏194，电子设备100根据压力传感器180A检测所述触摸操作强度。电子设备100也可以根据压力传感器180A的检测信号计算触摸的位置。在一些实施例中，作用于相同触摸位置，但不同触摸操作强度的触摸操作，可以对应不同的操作指令。例如：当有触摸操作强度小于第一压力阈值的触摸操作作用于短消息应用图标时，执行查看短消息的指令。当有触摸操作强度大于或等于第一压力阈值的触摸操作作用于短消息应用图标时，执行新建短消息的指令。The pressure sensor 180A is used to sense the pressure signal and can convert the pressure signal into an electrical signal. In some embodiments, the pressure sensor 180A may be provided on the display screen 194. There are many types of pressure sensors 180A, such as resistive pressure sensors, inductive pressure sensors, capacitive pressure sensors and so on. The capacitive pressure sensor may include at least two parallel plates with conductive materials. When a force is applied to the pressure sensor 180A, the capacitance between the electrodes changes. The electronic device 100 determines the intensity of the pressure according to the change in capacitance. When a touch operation acts on the display screen 194, the electronic device 100 detects the intensity of the touch operation according to the pressure sensor 180A. The electronic device 100 may also calculate the touched position according to the detection signal of the pressure sensor 180A. In some embodiments, touch operations that act on the same touch position but have different touch operation strengths may correspond to different operation instructions. For example: when a touch operation whose intensity of the touch operation is less than the first pressure threshold is applied to the short message application icon, an instruction to view the short message is executed. When a touch operation with a touch operation intensity greater than or equal to the first pressure threshold acts on the short message application icon, an instruction to create a new short message is executed.
陀螺仪传感器180B可以用于确定电子设备100的运动姿态。在一些实施例中，可以通过陀螺仪传感器180B确定电子设备100围绕三个轴(即，x，y和z轴)的角速度。陀螺仪传感器180B可以用于拍摄防抖。示例性的，当按下快门，陀螺仪传感器180B检测电子设备100抖动的角度，根据角度计算出镜头模组需要补偿的距离，让镜头通过反向运动抵消电子设备100的抖动，实现防抖。陀螺仪传感器180B还可以用于导航，体感游戏场景。The gyro sensor 180B may be used to determine the movement posture of the electronic device 100. In some embodiments, the angular velocity of the electronic device 100 around three axes (ie, x, y, and z axes) can be determined by the gyro sensor 180B. The gyro sensor 180B can be used for image stabilization. Exemplarily, when the shutter is pressed, the gyro sensor 180B detects the shake angle of the electronic device 100, calculates the distance that the lens module needs to compensate according to the angle, and allows the lens to counteract the shake of the electronic device 100 through reverse movement to achieve anti-shake. The gyro sensor 180B can also be used for navigation and somatosensory game scenes.
气压传感器180C用于测量气压。在一些实施例中，电子设备100通过气压传感器180C测得的气压值计算海拔高度，辅助定位和导航。The air pressure sensor 180C is used to measure air pressure. In some embodiments, the electronic device 100 calculates the altitude based on the air pressure value measured by the air pressure sensor 180C to assist positioning and navigation.
磁传感器180D包括霍尔传感器。电子设备100可以利用磁传感器180D检测翻盖皮套的开合。在一些实施例中，当电子设备100是翻盖机时，电子设备100可以根据磁传感器180D检测翻盖的开合。进而根据检测到的皮套的开合状态或翻盖的开合状态，设置翻盖自动解锁等特性。The magnetic sensor 180D includes a Hall sensor. The electronic device 100 may use the magnetic sensor 180D to detect the opening and closing of the flip holster. In some embodiments, when the electronic device 100 is a flip machine, the electronic device 100 can detect the opening and closing of the flip according to the magnetic sensor 180D. Furthermore, according to the detected opening and closing state of the leather case or the opening and closing state of the flip cover, features such as automatic unlocking of the flip cover are set.
加速度传感器180E可检测电子设备100在各个方向上(一般为三轴)加速度的大小。当电子设备100静止时可检测出重力的大小及方向。还可以用于识别电子设备100的姿态，应用于横竖屏切换，计步器等应用。The acceleration sensor 180E can detect the magnitude of the acceleration of the electronic device 100 in various directions (generally three axes). When the electronic device 100 is stationary, the magnitude and direction of gravity can be detected. It can also be used to identify the posture of the electronic device 100, and be used in applications such as horizontal and vertical screen switching, pedometers, and the like.
距离传感器180F，用于测量距离。电子设备100可以通过红外或激光测量距离。在一些实施例中，拍摄场景，电子设备100可以利用距离传感器180F测距以实现快速对焦。Distance sensor 180F, used to measure distance. The electronic device 100 can measure the distance by infrared or laser. In some embodiments, when shooting a scene, the electronic device 100 may use the distance sensor 180F to measure the distance to achieve fast focusing.
接近光传感器180G可以包括例如发光二极管(LED)和光检测器，例如光电二极管。发光二极管可以是红外发光二极管。电子设备100通过发光二极管向外发射红外光。电子设备100使用光电二极管检测来自附近物体的红外反射光。当检测到充分的反射光时，可以确定电子设备100附近有物体。当检测到不充分的反射光时，电子设备100可以确定电子设备100附近没有物体。电子设备100可以利用接近光传感器180G检测用户手持电子设备100贴近耳朵通话，以便自动熄灭屏幕达到省电的目的。接近光传感器180G也可用于皮套模式，口袋模式自动解锁与锁屏。The proximity light sensor 180G may include, for example, a light emitting diode (LED) and a light detector such as a photodiode. The light emitting diode may be an infrared light emitting diode. The electronic device 100 emits infrared light to the outside through the light emitting diode. The electronic device 100 uses a photodiode to detect infrared reflected light from nearby objects. When sufficient reflected light is detected, it can be determined that there is an object near the electronic device 100. When insufficient reflected light is detected, the electronic device 100 can determine that there is no object near the electronic device 100. The electronic device 100 can use the proximity light sensor 180G to detect that the user holds the electronic device 100 close to the ear to talk, so as to automatically turn off the screen to save power. The proximity light sensor 180G can also be used in leather case mode, and the pocket mode will automatically unlock and lock the screen.
环境光传感器180L用于感知环境光亮度。电子设备100可以根据感知的环境光亮度自适应调节显示屏194亮度。环境光传感器180L也可用于拍照时自动调节白平衡。环境光传感器180L还可以与接近光传感器180G配合，检测电子设备100是否在口袋里，以防误触。The ambient light sensor 180L is used to sense the brightness of the ambient light. The electronic device 100 can adaptively adjust the brightness of the display screen 194 according to the perceived brightness of the ambient light. The ambient light sensor 180L can also be used to automatically adjust the white balance when taking pictures. The ambient light sensor 180L can also cooperate with the proximity light sensor 180G to detect whether the electronic device 100 is in the pocket to prevent accidental touch.
指纹传感器180H用于采集指纹。电子设备100可以利用采集的指纹特性实现指纹解锁，访问应用锁，指纹拍照，指纹接听来电等。The fingerprint sensor 180H is used to collect fingerprints. The electronic device 100 can use the collected fingerprint characteristics to implement fingerprint unlocking, access application locks, fingerprint photographs, fingerprint answering calls, and so on.
温度传感器180J用于检测温度。在一些实施例中，电子设备100利用温度传感器180J检测的温度，执行温度处理策略。例如，当温度传感器180J上报的温度超过阈值，电子设备100执行 降低位于温度传感器180J附近的处理器的性能，以便降低功耗实施热保护。在另一些实施例中，当温度低于另一阈值时，电子设备100对电池142加热，以避免低温导致电子设备100异常关机。在其他一些实施例中，当温度低于又一阈值时，电子设备100对电池142的输出电压执行升压，以避免低温导致的异常关机。The temperature sensor 180J is used to detect temperature. In some embodiments, the electronic device 100 uses the temperature detected by the temperature sensor 180J to execute a temperature processing strategy. For example, when the temperature reported by the temperature sensor 180J exceeds a threshold value, the electronic device 100 executes to reduce the performance of the processor located near the temperature sensor 180J, so as to reduce power consumption and implement thermal protection. In other embodiments, when the temperature is lower than another threshold, the electronic device 100 heats the battery 142 to avoid abnormal shutdown of the electronic device 100 due to low temperature. In some other embodiments, when the temperature is lower than another threshold, the electronic device 100 boosts the output voltage of the battery 142 to avoid abnormal shutdown caused by low temperature.
触摸传感器180K，也称“触控面板”。触摸传感器180K可以设置于显示屏194，由触摸传感器180K与显示屏194组成触摸屏，也称“触控屏”。触摸传感器180K用于检测作用于其上或附近的触摸操作。触摸传感器可以将检测到的触摸操作传递给应用处理器，以确定触摸事件类型。可以通过显示屏194提供与触摸操作相关的视觉输出。在另一些实施例中，触摸传感器180K也可以设置于电子设备100的表面，与显示屏194所处的位置不同。Touch sensor 180K, also called "touch panel". The touch sensor 180K may be disposed on the display screen 194, and the touch screen is composed of the touch sensor 180K and the display screen 194, which is also called a “touch screen”. The touch sensor 180K is used to detect touch operations acting on or near it. The touch sensor can pass the detected touch operation to the application processor to determine the type of touch event. The visual output related to the touch operation can be provided through the display screen 194. In other embodiments, the touch sensor 180K may also be disposed on the surface of the electronic device 100, which is different from the position of the display screen 194.
骨传导传感器180M可以获取振动信号。在一些实施例中，骨传导传感器180M可以获取人体声部振动骨块的振动信号。骨传导传感器180M也可以接触人体脉搏，接收血压跳动信号。在一些实施例中，骨传导传感器180M也可以设置于耳机中，结合成骨传导耳机。音频模块170可以基于所述骨传导传感器180M获取的声部振动骨块的振动信号，解析出语音信号，实现语音功能。应用处理器可以基于所述骨传导传感器180M获取的血压跳动信号解析心率信息，实现心率检测功能。The bone conduction sensor 180M can acquire vibration signals. In some embodiments, the bone conduction sensor 180M can obtain the vibration signal of the vibrating bone mass of the human voice. The bone conduction sensor 180M can also contact the human pulse and receive the blood pressure pulse signal. In some embodiments, the bone conduction sensor 180M may also be provided in the earphone, combined with the bone conduction earphone. The audio module 170 can parse the voice signal based on the vibration signal of the vibrating bone block of the voice obtained by the bone conduction sensor 180M, and realize the voice function. The application processor can analyze the heart rate information based on the blood pressure beating signal obtained by the bone conduction sensor 180M, and realize the heart rate detection function.
按键190包括开机键，音量键等。按键190可以是机械按键。也可以是触摸式按键。电子设备100可以接收按键输入，产生与电子设备100的用户设置以及功能控制有关的键信号输入。The button 190 includes a power-on button, a volume button, and so on. The button 190 may be a mechanical button. It can also be a touch button. The electronic device 100 may receive key input, and generate key signal input related to user settings and function control of the electronic device 100.
马达191可以产生振动提示。马达191可以用于来电振动提示，也可以用于触摸振动反馈。例如，作用于不同应用(例如拍照，音频播放等)的触摸操作，可以对应不同的振动反馈效果。作用于显示屏194不同区域的触摸操作，马达191也可对应不同的振动反馈效果。不同的应用场景(例如：时间提醒，接收信息，闹钟，游戏等)也可以对应不同的振动反馈效果。触摸振动反馈效果还可以支持自定义。The motor 191 can generate vibration prompts. The motor 191 can be used for incoming call vibration notification, and can also be used for touch vibration feedback. For example, touch operations applied to different applications (such as photographing, audio playback, etc.) can correspond to different vibration feedback effects. Acting on touch operations in different areas of the display screen 194, the motor 191 can also correspond to different vibration feedback effects. Different application scenarios (for example: time reminding, receiving information, alarm clock, games, etc.) can also correspond to different vibration feedback effects. The touch vibration feedback effect can also support customization.
指示器192可以是指示灯，可以用于指示充电状态，电量变化，也可以用于指示消息，未接来电，通知等。The indicator 192 may be an indicator light, which may be used to indicate the charging status, power change, or to indicate messages, missed calls, notifications, and so on.
SIM卡接口195用于连接SIM卡。SIM卡可以通过***SIM卡接口195，或从SIM卡接口195拔出，实现和电子设备100的接触和分离。电子设备100可以支持1个或N个SIM卡接口，N为大于1的正整数。SIM卡接口195可以支持Nano SIM卡，Micro SIM卡，SIM卡等。同一个SIM卡接口195可以同时***多张卡。所述多张卡的类型可以相同，也可以不同。SIM卡接口195也可以兼容不同类型的SIM卡。SIM卡接口195也可以兼容外部存储卡。电子设备100通过SIM卡和网络交互，实现通话以及数据通信等功能。在一些实施例中，电子设备100采用eSIM，即：嵌入式SIM卡。eSIM卡可以嵌在电子设备100中，不能和电子设备100分离。The SIM card interface 195 is used to connect to the SIM card. The SIM card can be inserted into the SIM card interface 195 or pulled out from the SIM card interface 195 to achieve contact and separation with the electronic device 100. The electronic device 100 may support 1 or N SIM card interfaces, and N is a positive integer greater than 1. The SIM card interface 195 can support Nano SIM cards, Micro SIM cards, SIM cards, etc. The same SIM card interface 195 can insert multiple cards at the same time. The types of the multiple cards can be the same or different. The SIM card interface 195 can also be compatible with different types of SIM cards. The SIM card interface 195 may also be compatible with external memory cards. The electronic device 100 interacts with the network through the SIM card to implement functions such as call and data communication. In some embodiments, the electronic device 100 adopts an eSIM, that is, an embedded SIM card. The eSIM card can be embedded in the electronic device 100 and cannot be separated from the electronic device 100.
电子设备100的软件***可以采用分层架构，事件驱动架构，微核架构，微服务架构，或云架构。本发明实施例以分层架构的Android***为例，示例性说明电子设备100的软件结构。The software system of the electronic device 100 may adopt a layered architecture, an event-driven architecture, a microkernel architecture, a microservice architecture, or a cloud architecture. The embodiment of the present invention takes an Android system with a layered architecture as an example to illustrate the software structure of the electronic device 100.
图2是本发明实施例的电子设备100的软件结构框图。FIG. 2 is a block diagram of the software structure of the electronic device 100 according to an embodiment of the present invention.
分层架构将软件分成若干个层，每一层都有清晰的角色和分工。层与层之间通过软件接口通信。在一些实施例中，将Android***分为四层，从上至下分别为应用程序层，应用程序框架层，安卓运行时(Android runtime)和***库，以及内核层。The layered architecture divides the software into several layers, and each layer has a clear role and division of labor. Communication between layers through software interface. In some embodiments, the Android system is divided into four layers, from top to bottom, the application layer, the application framework layer, the Android runtime and system library, and the kernel layer.
应用程序层可以包括一系列应用程序包。The application layer can include a series of application packages.
如图2所示，应用程序包可以包括相机，图库，日历，通话，地图，导航，WLAN，蓝牙， 音乐，视频，短信息等应用程序。As shown in Figure 2, the application package may include applications such as camera, gallery, calendar, call, map, navigation, WLAN, Bluetooth, music, video, short message, etc.
应用程序框架层为应用程序层的应用程序提供应用编程接口(application programming interface，API)和编程框架。应用程序框架层包括一些预先定义的函数。The application framework layer provides an application programming interface (application programming interface, API) and a programming framework for applications in the application layer. The application framework layer includes some predefined functions.
如图2所示，应用程序框架层可以包括窗口管理器，内容提供器，视图***，电话管理器，资源管理器，通知管理器等。As shown in Figure 2, the application framework layer can include a window manager, a content provider, a view system, a phone manager, a resource manager, and a notification manager.
窗口管理器用于管理窗口程序。窗口管理器可以获取显示屏大小，判断是否有状态栏，锁定屏幕，截取屏幕等。The window manager is used to manage window programs. The window manager can obtain the size of the display screen, determine whether there is a status bar, lock the screen, take a screenshot, etc.
内容提供器用来存放和获取数据，并使这些数据可以被应用程序访问。所述数据可以包括视频，图像，音频，拨打和接听的电话，浏览历史和书签，电话簿等。The content provider is used to store and retrieve data and make these data accessible to applications. The data may include videos, images, audios, phone calls made and received, browsing history and bookmarks, phone book, etc.
视图***包括可视控件，例如显示文字的控件，显示图片的控件等。视图***可用于构建应用程序。显示界面可以由一个或多个视图组成的。例如，包括短信通知图标的显示界面，可以包括显示文字的视图以及显示图片的视图。The view system includes visual controls, such as controls that display text, controls that display pictures, and so on. The view system can be used to build applications. The display interface can be composed of one or more views. For example, a display interface that includes a short message notification icon may include a view that displays text and a view that displays pictures.
电话管理器用于提供电子设备100的通信功能。例如通话状态的管理(包括接通，挂断等)。The phone manager is used to provide the communication function of the electronic device 100. For example, the management of the call status (including connecting, hanging up, etc.).
资源管理器为应用程序提供各种资源，比如本地化字符串，图标，图片，布局文件，视频文件等等。The resource manager provides various resources for the application, such as localized strings, icons, pictures, layout files, video files, and so on.
通知管理器使应用程序可以在状态栏中显示通知信息，可以用于传达告知类型的消息，可以短暂停留后自动消失，无需用户交互。比如通知管理器被用于告知下载完成，消息提醒等。通知管理器还可以是以图表或者滚动条文本形式出现在***顶部状态栏的通知，例如后台运行的应用程序的通知，还可以是以对话窗口形式出现在屏幕上的通知。例如在状态栏提示文本信息，发出提示音，电子设备振动，指示灯闪烁等。The notification manager enables the application to display notification information in the status bar, which can be used to convey notification-type messages, and it can automatically disappear after a short stay without user interaction. For example, the notification manager is used to notify download completion, message reminders, and so on. The notification manager can also be a notification that appears in the status bar at the top of the system in the form of a chart or a scroll bar text, such as a notification of an application running in the background, or a notification that appears on the screen in the form of a dialog window. For example, text messages are prompted in the status bar, prompt sounds, electronic devices vibrate, and indicator lights flash.
Android Runtime包括核心库和虚拟机。Android runtime负责安卓***的调度和管理。Android Runtime includes core libraries and virtual machines. Android runtime is responsible for the scheduling and management of the Android system.
核心库包含两部分：一部分是java语言需要调用的功能函数，另一部分是安卓的核心库。The core library consists of two parts: one part is the function functions that the java language needs to call, and the other part is the core library of Android.
应用程序层和应用程序框架层运行在虚拟机中。虚拟机将应用程序层和应用程序框架层的java文件执行为二进制文件。虚拟机用于执行对象生命周期的管理，堆栈管理，线程管理，安全和异常的管理，以及垃圾回收等功能。The application layer and application framework layer run in a virtual machine. The virtual machine executes the java files of the application layer and the application framework layer as binary files. The virtual machine is used to perform functions such as object life cycle management, stack management, thread management, security and exception management, and garbage collection.
***库可以包括多个功能模块。例如：表面管理器(surface manager)，媒体库(Media LiBraries)，三维图形处理库(例如：OpenGL ES)，2D图形引擎(例如：SGL)等。The system library can include multiple functional modules. For example: surface manager (surface manager), media library (Media LiBraries), 3D graphics processing library (for example: OpenGL ES), 2D graphics engine (for example: SGL), etc.
表面管理器用于对显示子***进行管理，并且为多个应用程序提供了2D和3D图层的融合。The surface manager is used to manage the display subsystem and provides a combination of 2D and 3D layers for multiple applications.
媒体库支持多种常用的音频，视频格式回放和录制，以及静态图像文件等。媒体库可以支持多种音视频编码格式，例如:MPEG4，H.264，MP3，AAC，AMR，JPG，PNG等。The media library supports playback and recording of a variety of commonly used audio and video formats, as well as still image files. The media library can support multiple audio and video encoding formats, such as: MPEG4, H.264, MP3, AAC, AMR, JPG, PNG, etc.
三维图形处理库用于实现三维图形绘图，图像渲染，合成，和图层处理等。The 3D graphics processing library is used to implement 3D graphics drawing, image rendering, synthesis, and layer processing.
2D图形引擎是2D绘图的绘图引擎。The 2D graphics engine is a drawing engine for 2D drawing.
内核层是硬件和软件之间的层。内核层至少包含显示驱动，摄像头驱动，音频驱动，传感器驱动。The kernel layer is the layer between hardware and software. The kernel layer contains at least display driver, camera driver, audio driver, and sensor driver.
下面结合捕获拍照场景，示例性说明电子设备100软件以及硬件的工作流程。In the following, the workflow of the software and hardware of the electronic device 100 will be exemplified in conjunction with capturing a photo scene.
当触摸传感器180K接收到触摸操作，相应的硬件中断被发给内核层。内核层将触摸操作加工成原始输入事件(包括触摸坐标，触摸操作的时间戳等信息)。原始输入事件被存储在内核层。应用程序框架层从内核层获取原始输入事件，识别该输入事件所对应的控件。以该触摸操作是触摸单击操作，该单击操作所对应的控件为相机应用图标的控件为例，相机应用调用应用框架层的 接口，启动相机应用，进而通过调用内核层启动摄像头驱动，通过摄像头193捕获静态图像或视频。When the touch sensor 180K receives a touch operation, the corresponding hardware interrupt is sent to the kernel layer. The kernel layer processes touch operations into original input events (including touch coordinates, time stamps of touch operations, etc.). The original input events are stored in the kernel layer. The application framework layer obtains the original input event from the kernel layer and identifies the control corresponding to the input event. Taking the touch operation as a touch click operation, and the control corresponding to the click operation is the control of the camera application icon as an example, the camera application calls the interface of the application framework layer to start the camera application, and then starts the camera driver by calling the kernel layer. The camera 193 captures still images or videos.
运动肌肉疲劳是指机体生理过程不能持续其机能在一特定水平上或不能维持预定的运动强度。现有技术中，运动后肌肉疲劳的检测方法主要有：血乳酸检测法和表面肌电信号(EMG)检测法。Exercise muscle fatigue refers to the inability of the body's physiological processes to continue its function at a certain level or the inability to maintain a predetermined exercise intensity. In the prior art, the detection methods of muscle fatigue after exercise mainly include: blood lactate detection method and surface electromyography (EMG) detection method.
其中，血乳酸检测法需要采集血液，不能被大部分用户群体接受，而表面肌电测量方法需要贴放多种电极，操作繁琐，不适合用户自行测量，基于现有技术，本发明提供了一种运动后肌肉疲劳度的检测方法，能够方便用户自行测量且用户接收度高，且不需要专门的检测设备。Among them, the blood lactic acid detection method requires blood collection and cannot be accepted by most user groups, while the surface electromyography measurement method requires multiple electrodes to be attached, which is cumbersome to operate and is not suitable for users to measure by themselves. Based on the prior art, the present invention provides a A method for detecting muscle fatigue after exercise is convenient for users to measure by themselves and has high user acceptance, and does not require special testing equipment.
图3为本发明实施例提供的一种运动后肌肉疲劳度的检测方法的流程图；3 is a flowchart of a method for detecting muscle fatigue after exercise provided by an embodiment of the present invention;
一种运动后肌肉疲劳度的检测方法，该方法可以应用于运动后肌肉疲劳度的检测装置，检测装置可以为上述的电子设备100(例如手机)，也可以为体重测量设备(例如体重秤或者体脂秤或者其它具有体重测量功能的设备)，当然，还可以是其它的电子设备，如图3所示，该方法包括：A method for detecting muscle fatigue after exercise. The method can be applied to a device for detecting muscle fatigue after exercise. The detecting device may be the above-mentioned electronic device 100 (such as a mobile phone) or a weight measuring device (such as a weight scale or Body fat scale or other equipment with weight measurement function), of course, can also be other electronic equipment, as shown in Figure 3, the method includes:
步骤S11：获取用户运动前的身体状态参数以及运动后的身体状态参数。Step S11: Obtain the body state parameters of the user before exercise and the body state parameters after exercise.
步骤S12：根据用户运动前的身体状态参数以及用户在运动后的身体状态参数计算用户运动后的肌肉疲劳度。Step S12: Calculate the user's muscle fatigue after exercise according to the user's body state parameters before exercise and the user's body state parameters after exercise.
可以理解，本发明实施例通过获取用户运动前的身体状态参数以及运动后的身体状态参数，然后根据用户运动前的身体状态参数以及用户在运动后的身体状态参数计算用户运动后的肌肉疲劳度，无需采集用户的血液且操作简单，用户接受度高，同时，依靠体重测量设备就能够实现肌肉疲劳度检测，无需专门的检测设备。It can be understood that the embodiment of the present invention obtains the user's body state parameters before exercise and post-exercise body state parameters, and then calculates the user's muscle fatigue after exercise according to the user's body state parameters before exercise and the user's body state parameters after exercise. , No need to collect the user's blood, simple operation, high user acceptance, and at the same time, the muscle fatigue detection can be achieved by relying on the weight measurement equipment, without the need for special testing equipment.
具体实现中，步骤S11和步骤S12可以由终端设备(例如电子设备100)执行，也可以由体重测量设备(例如体重秤)执行。In specific implementation, step S11 and step S12 may be executed by a terminal device (for example, the electronic device 100), or may be executed by a weight measurement device (for example, a weight scale).
其中，针对由终端设备(例如电子设备100)执行的场景，具体可以为：终端设备与体重测量设备通信连接，体重测量设备基于压力测试测得用户在运动前和运动后施加的压力信号后，将压力信号发送给终端设备，终端设备根据压力信号分别确定用户在运动前和运动后的身体状态参数，从而计算出肌肉疲劳度并显示给用户。Among them, for the scenario executed by the terminal device (for example, the electronic device 100), it may specifically be: the terminal device is in communication connection with the weight measurement device, and the weight measurement device measures the pressure signal applied by the user before and after the exercise based on the stress test, The pressure signal is sent to the terminal device, and the terminal device separately determines the user's body state parameters before and after the exercise according to the pressure signal, thereby calculating the muscle fatigue and displaying it to the user.
当然，在该场景的其他实现方式中，也可由体重测量设备对自身所测得的压力信号进行处理，得到用户在运动前和运动后的身体状态参数，再将用户在运动前和运动后的身体状态参数发送给终端设备，终端设备再根据运动前和运动后的身体状态参数计算出肌肉疲劳度。Of course, in other implementations of this scenario, the weight measurement device can also process the pressure signal measured by itself to obtain the user's body state parameters before and after exercise, and then calculate the user's body state parameters before and after exercise. The body state parameters are sent to the terminal device, and the terminal device calculates the degree of muscle fatigue based on the body state parameters before and after exercise.
需要说明的是，为了对运动前和运动后的压力信号进行区分，本发明实施例将运动前的压力信号称为“第一压力信号”，将运动后的压力信号称为“第二压力信号”。It should be noted that, in order to distinguish pre-exercise and post-exercise pressure signals, the embodiment of the present invention refers to the pre-exercise pressure signal as the "first pressure signal" and the post-exercise pressure signal as the "second pressure signal". ".
还需要说明的是，用户的状态分为运动前、运动中和运动后，其中，如果用户在运动前进行体重测量时，如果已存在历史运动前(静息态下)的身体状态参数，那么可以将本次测量所的到的身体状态参数与历史运动前(静息态下)的身体状态参数进行加权平均运算得到新的历史运动前(静息态下)的身体状态参数。当然，在一些实施例中，也可不进行加权平均运算，在用户在运动前进行多次测量时，直接将最新的运动前(静息态下)的身体状态参数进作为运动前(静息态下)的身体状态参数。在本发明一个或者多个实施例中，可以根据用户在体重测量时所选择的状态(运动前/运动后)确定用户的状态为运动前或者运动后。It should also be noted that the user's state is divided into pre-exercise, in-exercise and post-exercise. Among them, if the user performs weight measurement before exercise, if there are historical body state parameters before exercise (at rest), then The body state parameters obtained in this measurement and the body state parameters before exercise (at rest) can be weighted and averaged to obtain the new body state parameters before exercise (at rest). Of course, in some embodiments, the weighted average calculation may not be performed. When the user performs multiple measurements before exercise, the latest pre-exercise (resting state) body state parameter is directly used as the pre-exercise (resting state). Below) the physical state parameters. In one or more embodiments of the present invention, the state of the user can be determined as pre-exercise or post-exercise according to the state selected by the user during weight measurement (before exercise/after exercise).
其中，针对由体重测量设备(例如体重秤)执行的场景，具体可以为：体重测量设备基于压力测试测得用户在运动前施加的第一压力信号后，根据第一压力信号确定用户在运动前的身体状 态参数；体重测量设备基于压力测试测得用户在运动前施加的第二压力信号后，根据第二压力信号确定用户在运动后的身体状态参数，即本发明实施例提供的运动后肌肉疲劳度的检测方法的各个步骤均由体重测量设备执行；Among them, for a scenario executed by a weight measurement device (such as a weight scale), it may specifically be: after the weight measurement device measures the first pressure signal applied by the user before exercise based on the stress test, it determines that the user is before exercise according to the first pressure signal The body state parameter of the body; the weight measurement device measures the second pressure signal applied by the user before exercise based on the stress test, and then determines the body state parameter of the user after exercise according to the second pressure signal, that is, the post-exercise muscle provided by the embodiment of the present invention Each step of the fatigue detection method is executed by the weight measurement equipment;
可以理解，本发明实施例提供的检测方法基于压力检测，无需更改硬件产品形态。It can be understood that the detection method provided by the embodiment of the present invention is based on pressure detection, and there is no need to change the form of the hardware product.
基于上述，在一种可选地实施方式中，获取用户运动前的身体状态参数，包括：Based on the foregoing, in an optional implementation manner, acquiring the user's physical state parameters before exercise includes:
接收体重测量设备生成的第一压力信号，根据第一压力信号确定用户在运动前的身体状态参数，其中，第一压力信号由体重测量设备在用户运动前使用体重测量设备进行体重测量时产生；Receiving the first pressure signal generated by the weight measuring device, and determining the user's body state parameter before exercise according to the first pressure signal, wherein the first pressure signal is generated by the weight measuring device before the user uses the weight measuring device to perform weight measurement;
获取用户运动后的身体状态参数，包括：Obtain the physical state parameters of the user after exercise, including:
接收体重测量设备生成的第二压力信号，根据第二压力信号确定用户运动后的身体状态参数，其中，第二压力信号由体重测量设备在用户运动后使用体重测量设备进行体重测量时产生。The second pressure signal generated by the weight measurement device is received, and the body state parameter of the user after exercise is determined according to the second pressure signal, wherein the second pressure signal is generated by the weight measurement device when the user uses the weight measurement device to perform weight measurement after exercise.
基于上述，在另一种可选地实施方式中，获取用户运动前的身体状态参数，包括：Based on the foregoing, in another optional implementation manner, acquiring the user's physical state parameters before exercise includes:
在用户运动前，对用户进行体重测量，根据用户所施加的压力生成第一压力信号，根据第一压力信号确定用户在运动前的身体状态参数；Before the user exercises, measure the user's weight, generate a first pressure signal according to the pressure applied by the user, and determine the user's body state parameters before the exercise according to the first pressure signal;
获取用户运动后的身体状态参数，包括：Obtain the physical state parameters of the user after exercise, including:
在用户运动后，对用户进行体重测量，根据用户所施加的压力生成第一压力信号，根据第一压力信号确定用户在运动后的身体状态参数。After the user exercises, the weight of the user is measured, the first pressure signal is generated according to the pressure applied by the user, and the physical state parameters of the user after the exercise are determined according to the first pressure signal.
基于上述，在另一种可选地实施方式中，接收体重测量设备生成的用户运动前的身体状态参数以及运动后的身体状态参数，其中，用户运动前的身体状态参数由体重测量设备根据所测得的第一压力信号生成，用户运动后的身体状态参数由体重测量设备根据所测得的第二压力信号生成。Based on the foregoing, in another optional implementation manner, the body state parameters of the user before exercise and the body state parameters after exercise generated by the weight measurement device are received, wherein the body state parameters of the user before exercise are determined by the weight measurement device. The measured first pressure signal is generated, and the body state parameters of the user after exercise are generated by the weight measurement device according to the measured second pressure signal.
在一种可选的实施方式中，用户运动前的身体状态参数包括但不限于：身体平稳度BA1、心率HR1、呼吸频率BR1及呼吸强度BI1中的一种或者任意多种。In an optional implementation manner, the physical state parameters of the user before exercise include, but are not limited to: one or any more of body stability BA1, heart rate HR1, respiratory rate BR1, and respiratory intensity BI1.
在一种可选的实施方式中，用户运动后的身体状态参数包括但不限于：身体平稳度BA2、心率HR2、呼吸频率BR2及呼吸强度BI2中的一种或者任意多种。In an optional implementation manner, the physical state parameters of the user after exercise include, but are not limited to, one or any more of body stability BA2, heart rate HR2, breathing rate BR2, and breathing intensity BI2.
在一种可选的实施方式中，接收体重测量设备生成的第一压力信号，还包括：In an optional implementation manner, receiving the first pressure signal generated by the weight measurement device further includes:
获取用户运动对应的运动参数；Obtain the motion parameters corresponding to the user's motion;
根据用户运动前的身体状态参数、用户运动后的身体状态参数以及用户的运动参数计算用户的心脏恢复指数RH及肺恢复指数RL。The user's heart recovery index RH and lung recovery index RL are calculated according to the user's body state parameters before exercise, the user's body state parameters after exercise, and the user's exercise parameters.
在一种可选的实施方式中，根据用户运动前的身体状态参数以及用户在运动后的身体状态参数计算用户运动后的肌肉疲劳度，可以包括：In an optional implementation manner, calculating the user's muscle fatigue after exercise according to the user's body state parameters before exercise and the user's body state parameters after exercise may include:
根据用户运动前的身体状态参数、用户在运动后的身体状态参数以及用户的运动参数计算用户运动后的肌肉疲劳度FM。The user's muscle fatigue degree FM after exercise is calculated according to the user's body state parameters before exercise, the user's body state parameters after exercise, and the user's exercise parameters.
可以理解，本发明实施例能够结合用户的运动参数计算用户的肌肉疲劳度FM，当然，也可以不结合用户的运动参数计算用户的肌肉疲劳度FM，本发明实施例还可以结合用户的运动参数、用户运动前的身体状态参数、用户在运动后的身体状态参数计算用户心脏恢复指数RH和/或肺恢复指数RL。It can be understood that the embodiment of the present invention can calculate the user's muscle fatigue FM in combination with the user's exercise parameters. Of course, it is also possible to calculate the user's muscle fatigue FM without combining the user's exercise parameters. The embodiment of the present invention can also be combined with the user's exercise parameters. Calculate the user's heart recovery index RH and/or lung recovery index RL with the user's body state parameters before exercise and the user's body state parameters after exercise.
在一种可选的实施方式中，运动参数包括运动类型s、运动强度m、运动时长t、运动后时间T、运动参数可信度w中的一种或者任意多种。In an optional embodiment, the exercise parameter includes one or any more of exercise type s, exercise intensity m, exercise duration t, post-exercise time T, and exercise parameter credibility w.
其中，在本发明实施例中，运动参数的获取方式可以有：在执行上述图3中的运动后肌肉疲劳度的检测方法的设备的运动健康类App(Application，应用程序)或者用户佩戴的运动手表、 运动手环等智能设备获取。Among them, in the embodiment of the present invention, the method for acquiring the exercise parameter may include: the exercise health App (Application, application) of the device that executes the method for detecting muscle fatigue after exercise in FIG. 3 or the exercise worn by the user Obtained from smart devices such as watches and sports bracelets.
具体实现中，用户进行完运动(例如跑步)时，对应的设备(例如手机)的运动健康类App可以获知用户该次运动的相关运动参数，例如运动类型s、运动强度m、运动时长t、运动后时间T等等中的一种或者多种，当用户在运动时携带该设备时，该设备可以根据用户的动作自动获得用户的运动类型、运动强度、运动时长、运动后时长等等，当然，也可以由用户输入至运动健康类App或者运动手表或者运动手环中，然后，执行上述图3中的运动后肌肉疲劳度的检测方法的设备从相关运动健康类App获得相关运动参数，其中，运动健康类App可以获知用户的相关运动参数的具体实现过程现有技术已普遍应用，本发明实施例不一一赘述。In a specific implementation, when the user finishes an exercise (for example, running), the sports and health app of the corresponding device (for example, a mobile phone) can learn the related exercise parameters of the user's exercise, such as exercise type s, exercise intensity m, exercise duration t, One or more of the post-exercise time T, etc. When the user carries the device while exercising, the device can automatically obtain the user’s exercise type, exercise intensity, exercise duration, post-exercise duration, etc. according to the user’s actions, Of course, it can also be input by the user into a sports health app or a sports watch or a sports bracelet, and then the device that executes the method for detecting muscle fatigue after exercise in Figure 3 above obtains the relevant exercise parameters from the related sports health app. Among them, the specific implementation process of the sports health app that can learn the user's related sports parameters. The prior art has been widely used, and the embodiments of the present invention will not repeat them one by one.
当然，在另一种具体实现中，也可以通过用户手动输入来获得相关运动参数，或者，部分运动参数从运动健康类App处获取，部分运动参数通过用户手动输入获取，当然，全部运动参数或者部分运动参数也可以是非必要的，例如，全部运动参数或者部分运动参数可以为空，本发明对此不做具体限定。Of course, in another specific implementation, the relevant exercise parameters can also be obtained through manual input by the user, or some exercise parameters are obtained from the sports health app, and some exercise parameters are obtained through manual input by the user. Of course, all exercise parameters or Part of the motion parameters may also be unnecessary. For example, all the motion parameters or some of the motion parameters may be empty, which is not specifically limited in the present invention.
其中，运动参数还可以包括运动参数可信度w。在本发明实施例中，运动参数可信度w可以与除运动参数可信度w以外的其它运动参数的来源和/或完整度和/或数量等相关联，例如，当除运动参数可信度w以外的其它运动参数为从运动健康类App或手表手环中自动获取时，运动参数可信度最高，可以设为w＝1；用户手动输入时，运动参数可信度次之，可以设为w＝0.5；当除运动参数可信度w以外的其它运动参数均为空时，运动参数可信度最低，可以设为w＝0，运动参数可信度w的确定方式可以根据实际需求灵活设置。Wherein, the motion parameter may also include the reliability w of the motion parameter. In the embodiment of the present invention, the sports parameter credibility w may be associated with the source and/or completeness and/or quantity of other sports parameters other than the sports parameter credibility w, for example, when the sports parameter is credible When other sports parameters other than the degree w are automatically obtained from sports health apps or watch bracelets, the sports parameters have the highest credibility and can be set to w=1; when the user manually enters the sports parameters, the credibility of the sports parameters is the second, yes. Set to w=0.5; when the other motion parameters except for the motion parameter credibility w are all empty, the motion parameter credibility is the lowest, which can be set to w=0. The determination method of the motion parameter credibility w can be based on actual conditions. Flexible settings are required.
在本发明实施例中，根据用户运动前的身体状态参数、用户在运动后的身体状态参数计算和/或用户的运动参数计算用户运动后的肌肉疲劳度FM、心脏恢复指数RH、肺恢复指数RL，计算用户运动后的肌肉疲劳度FM、心脏恢复指数RH、肺恢复指数RL的方法包括但不限于通过加权平均算法、神经网络等方式。In the embodiment of the present invention, the user’s muscle fatigue FM, heart recovery index RH, and lung recovery index after exercise are calculated according to the user’s body state parameters before exercise, the user’s body state parameter calculations after exercise, and/or the user’s exercise parameters. RL, methods for calculating the user's muscle fatigue FM, heart recovery index RH, and lung recovery index RL after exercise include but are not limited to methods such as weighted average algorithm and neural network.
在本发明实施例中，计算出用户的肌肉疲劳度FM、和/或心脏恢复指数RH、肺恢复指数RL中的之后，本发明还可以包括：显示计算得到的用户的肌肉疲劳度FM、心脏恢复指数RH及肺恢复指数RL中的任意一个或者任意多个，以及根据用户的肌肉疲劳度、心脏恢复指数及肺恢复指数中的任意一个或者任意多个生成运动量评估和/或身体机能评估和/或运动建议和/或身体恢复建议并显示上述运动量评估和/或身体机能评估和/或运动建议和/或身体恢复建议给用户，上述具体实现过程下文会进行具体说明。In the embodiment of the present invention, after calculating the user’s muscle fatigue FM, and/or heart recovery index RH, lung recovery index RL, the present invention may further include: displaying the calculated user’s muscle fatigue FM, heart Any one or more of the recovery index RH and the lung recovery index RL, and any one or more of the user’s muscle fatigue, heart recovery index, and lung recovery index generate exercise volume assessment and/or physical function assessment and / Or exercise suggestion and/or body recovery suggestion and display the above exercise volume assessment and/or physical function assessment and/or exercise suggestion and/or body recovery suggestion to the user. The above specific implementation process will be described in detail below.
可以理解，本发明实施例通过向用户显示检测结果，并给出运动量评估和/或身体机能评估和/或运动建议和/或身体恢复建议，通过人机交互的方式便于用户了解当前身体状态，同时为用户提供运动和身体恢复上的指导，能够避免用户进行强度、时间不合理的运动训练以及引导用户快速恢复身体机能。It can be understood that the embodiment of the present invention displays the detection result to the user, and provides exercise volume assessment and/or physical function assessment and/or exercise suggestion and/or physical recovery suggestion, which facilitates the user to understand the current physical state through human-computer interaction. At the same time, it provides users with guidance on exercise and physical recovery, which can prevent users from performing exercise training with unreasonable intensity and time and guide users to quickly restore physical functions.
图4为本发明实施例提供的运动后肌肉疲劳度的检测方法的一种示例性的应用框架图；4 is an exemplary application framework diagram of a method for detecting muscle fatigue after exercise provided by an embodiment of the present invention;
如图4所示，用户计算肌肉疲劳度、心脏恢复指数及肺恢复指数的过程，具体可以包括：As shown in Figure 4, the user's calculation of muscle fatigue, heart recovery index, and lung recovery index may specifically include:
步骤(1)：用户运动前(静息态时)，体重测量设备(例如体重秤，还可以是体脂秤等具有体重检测功能的设备)检测用户对体重秤的压力，生成压力信号(即上述的第一压力信号)，体重测量设备或者终端设备再根据第一压力信号计算身体平稳度BA1、心率HR1、呼吸频率BR1、呼吸强度BI1，作为用户静息态下的身体状态参数；Step (1): Before the user exercises (at rest), the weight measurement device (such as a weight scale, or a device with a weight detection function such as a body fat scale) detects the user’s pressure on the weight scale and generates a pressure signal (ie The above-mentioned first pressure signal), the weight measuring device or the terminal device calculates the body stability BA1, the heart rate HR1, the breathing rate BR1, and the breathing intensity BI1 according to the first pressure signal as the body state parameters of the user in the resting state;
步骤(2)：用户运动后，体重测量设备(例如体重秤，还可以是体脂秤等具有体重检测功能的设备)检测用户对体重秤的压力，生成上述的压力信号(即上述的第二压力信号)，体重测量设备或者终端设备再根据第二压力信号中计算身体平稳度BA2、心率HR2、呼吸频率BR2、呼吸强度BI2，作为用户运动后的身体状态参数；Step (2): After the user exercises, the weight measurement device (for example, a weight scale, or a device with a weight detection function such as a body fat scale) detects the pressure of the user on the weight scale, and generates the above-mentioned pressure signal (that is, the above-mentioned second Pressure signal), the weight measuring device or terminal device calculates the body stability BA2, the heart rate HR2, the breathing rate BR2, and the breathing intensity BI2 according to the second pressure signal as the physical state parameters of the user after exercise;
步骤(3)：用户运动后，体重测量设备或者终端设备获取用户的运动参数，包括运动类型s、运动强度m、运动时长t、运动后时间T、运动参数可信度w，获取方式优选为从运动健康类App或相应的手表、手环等设备中自动获取，也可为用户手动输入，也可为空。运动参数为从运动健康类App或手表、手环等设备中自动获取时，运动参数可信度最高，可以设为w＝1；用户手动输入时，运动参数可信度次之，可以设为w＝0.5；运动参数为空时，运动参数可信度最低，可以设为w＝0。Step (3): After the user exercises, the weight measuring device or terminal device obtains the user's exercise parameters, including exercise type s, exercise intensity m, exercise duration t, post-exercise time T, and exercise parameter credibility w. The acquisition method is preferably It can be automatically obtained from sports and health apps or corresponding watches, bracelets and other devices. It can also be entered manually by the user, or it can be empty. When the exercise parameters are automatically obtained from sports and health apps or watches, bracelets and other devices, the exercise parameters have the highest credibility, which can be set to w=1; when the user manually enters the exercise parameters, the credibility of the exercise parameters is second, which can be set w=0.5; when the motion parameter is empty, the reliability of the motion parameter is the lowest, which can be set to w=0.
其中，步骤(3)还可与步骤(2)同时进行，或者先进行步骤(3)，再进行步骤(2)。Among them, step (3) can also be performed simultaneously with step (2), or step (3) is performed first, and then step (2) is performed.
步骤(4)：根据用户的静息态和运动后的身体状态参数，以及运动参数，计算肌肉疲劳度FM、心脏恢复指数RH、肺恢复指数RL，计算方法包括但不限于通过加权平均算法或神经网络等。Step (4): Calculate muscle fatigue FM, heart recovery index RH, and lung recovery index RL according to the user’s resting state and post-exercise physical state parameters, as well as exercise parameters. The calculation methods include but are not limited to weighted average algorithm or Neural network, etc.
步骤(5)：将计算得到的肌肉疲劳度、心脏恢复指数、肺恢复指数、身体平稳度、心率、呼吸频率、呼吸强度显示在界面上，并给出运动量和身体机能评估，以及运动建议和恢复建议等。Step (5): Display the calculated muscle fatigue, heart recovery index, lung recovery index, body stability, heart rate, breathing rate, and breathing intensity on the interface, and give an assessment of exercise volume and physical function, as well as exercise suggestions and Recovery suggestions, etc.
图5为本发明实施例提供的一种示例性的用户运动前身体状态参数的检测流程图；FIG. 5 is an exemplary detection flow chart of a user's body state parameter before exercise according to an embodiment of the present invention;
如图5所示，用户运动前(静息态下)的身体状态参数的检测流程，上述流程可以由体重测量设备或者终端设备实现，该流程包括：As shown in Figure 5, the process of detecting body state parameters of the user before exercise (at rest), the above process can be implemented by a weight measurement device or a terminal device, and the process includes:
步骤S21：采集第一压力信号，具体可由体重测量设备采集；Step S21: Collect the first pressure signal, which can be collected by the body weight measurement device;
步骤S22：对第一压力信号依次进行高通放大和低通滤波处理；Step S22: sequentially perform high-pass amplification and low-pass filtering processing on the first pressure signal;
步骤S23：对进行高通放大和低通滤波处理后的第一压力信号进行低通滤波处理，得到运动前/静息态下的身体平稳度BA0，以及对进行高通放大和低通滤波处理后的第一压力信号进行高通处理，得到用户运动前/静息态下的心率HR0、呼吸频率BR0、呼吸强度BI0；Step S23: Perform low-pass filter processing on the first pressure signal after the high-pass amplification and low-pass filter processing, to obtain the body stability BA0 before exercise/resting state, and perform the high-pass amplification and low-pass filter processing on the The first pressure signal is subjected to high-pass processing to obtain the user's pre-exercise/rest heart rate HR0, respiratory rate BR0, and respiratory intensity BI0;
步骤S24：判断是否储存有历史静息态下的身体状态参数(包括历史静息态下的身体平稳度BA3、历史静息态下的心率HR3、历史静息态下的呼吸频率BR3、历史静息态下的呼吸强度BI3中的任意一种或者多种)，如果有，则将步骤S23中得到的身体状态参数与对应的历史静息态下的身体状态参数进行加权平均运算，得到新的用户运动前/静息态时的身体状态参数，如果没有，则使用步骤S23中得到的身体状态参数作为用户运动前(静息态下)的身体状态参数。Step S24: Determine whether the body state parameters in the historical resting state are stored (including the body stability BA3 in the historical resting state, the heart rate HR3 in the historical resting state, the respiratory rate BR3 in the historical resting state, and the historical resting state). Any one or more of the breathing intensity BI3 in the rest state), if there is, the body state parameter obtained in step S23 and the corresponding body state parameter in the historical resting state are subjected to a weighted average operation to obtain a new The body state parameters of the user before exercise/resting state. If not, the body state parameters obtained in step S23 are used as the body state parameters of the user before exercise (resting state).
具体地，当储存有历史静息态的身体平稳度BA3时，用户运动前/静息态时的身体平稳度BA1＝Wnew1*BA0+Wold1*BA3，其中，Wnew1与Wold1为加权系数，Wnew1与Wold1之和为1；当没有储存有历史静息态的身体平稳度BA3时，BA1＝BA0。Specifically, when the historical resting state of body stability BA3 is stored, the user's body stability before exercise/resting state BA1=Wnew1*BA0+Wold1*BA3, where Wnew1 and Wold1 are weighted coefficients, and Wnew1 is equal to The sum of Wold1 is 1; when there is no historical resting state of body stability BA3 stored, BA1=BA0.
具体地，当储存有历史静息态下的心率HR3时，用户运动前/静息态下的心率HR1＝Wnew2*HR0+Wold2*HR3,其中，Wnew2与Wold2为加权系数，Wnew2与Wold2之和为1；当没有储存有历史静息态的心率HR3时，HR1＝HR0。Specifically, when the historical resting state heart rate HR3 is stored, the user's heart rate before exercise/resting state HR1=Wnew2*HR0+Wold2*HR3, where Wnew2 and Wold2 are weighting coefficients, and the sum of Wnew2 and Wold2 It is 1; when there is no historical resting heart rate HR3 stored, HR1=HR0.
具体地，当储存有历史静息态下的呼吸频率BR3时，用户运动前/静息态下的呼吸频率BR1＝Wnew3*BR0+Wold3*BR3,其中，Wnew3与Wold3为加权系数，Wnew3与Wold3之和为1；当没有储存有历史静息态的身体平稳度BR3时，HR1＝HR0。Specifically, when the historical resting state breathing rate BR3 is stored, the user's pre-exercise/resting state breathing rate BR1=Wnew3*BR0+Wold3*BR3, where Wnew3 and Wold3 are weighting coefficients, Wnew3 and Wold3 The sum is 1; when the body stability BR3 of the historical resting state is not stored, HR1=HR0.
具体地，当储存有历史静息态下的呼吸强度BI3时，用户运动前/静息态下的呼吸强度 BI1＝Wnew4*BI0+Wold2*BI3,其中，Wnew4与Wold4为加权系数，Wnew4与Wold4之和为1；当没有储存有历史静息态的呼吸强度BI3时，BI1＝BI0。Specifically, when the historical resting state breathing intensity BI3 is stored, the user's pre-exercise/rest breathing intensity BI1=Wnew4*BI0+Wold2*BI3, where Wnew4 and Wold4 are weighted coefficients, Wnew4 and Wold4 The sum is 1; when no historical resting state breathing intensity BI3 is stored, BI1=BI0.
步骤S24：存储并显示运动前(静息态下)的身体状态参数：身体平稳度BA1、心率HR1、呼吸频率BR1、呼吸强度BI1。Step S24: Store and display the body state parameters before exercise (at rest): body stability BA1, heart rate HR1, respiration rate BR1, respiration intensity BI1.
图6为本发明实施例提供的一种示例性的用户运动后检测肌肉疲劳度、心脏恢复指数和肺恢复指数的流程图；FIG. 6 is an exemplary flow chart for detecting muscle fatigue, heart recovery index, and lung recovery index after exercise of a user according to an embodiment of the present invention;
如图6所示，用户运动后检测肌肉疲劳度FM、心脏恢复指数RH和肺恢复指数RL的流程可以由体重测量设备或者终端设备实现，该流程包括：As shown in Figure 6, the process of detecting muscle fatigue FM, heart recovery index RH, and lung recovery index RL after the user exercises can be implemented by a weight measurement device or a terminal device, and the process includes:
步骤S31:获取用户的运动参数，其中，运动参数包括运动类型s、运动强度m、运动时长t、运动后时长T及运动参数可信度w的一种或者任意多种；如图6所示，在获取用户的运动参数前，可以对用户的运动参数的获取方式进行设置。Step S31: Obtain the user's exercise parameters, where the exercise parameters include one or more of exercise type s, exercise intensity m, exercise duration t, post-exercise duration T, and exercise parameter credibility w; as shown in FIG. 6 Before acquiring the user's motion parameters, the user's motion parameter acquisition method can be set.
一种可选的实施方式中，获取用户的运动参数的方式可以包括：In an optional implementation manner, the manner of obtaining the user's motion parameters may include:
通过运动健康类App或相应的手表、手环等设备中自动获取运动参数(运动参数可以包括运动类型s1、运动强度m1、运动时长t1、运动后时间T1、运动参数可信度w1)；Automatically obtain exercise parameters through sports health apps or corresponding watches, bracelets and other devices (exercise parameters can include exercise type s1, exercise intensity m1, exercise duration t1, post-exercise time T1, exercise parameter credibility w1);
或者，通过用户输入运动参数(运动参数可以包括运动类型s2、运动强度m2、运动时长t2、运动后时间T2、运动参数可信度w2)；Alternatively, the user can input the exercise parameter (the exercise parameter may include exercise type s2, exercise intensity m2, exercise duration t2, post-exercise time T2, exercise parameter credibility w2);
又或者，部分运动参数从运动健康类App或相应的手表、手环等设备获取，部分运动参数通过用户输入；Or, some sports parameters are obtained from sports health apps or corresponding watches, bracelets and other devices, and some sports parameters are input by the user;
当然，运动参数可以是非必要的，当无法从运动健康类App或相应的手表、手环等设备中获取运动参数，且用户没有输入运动参数时，运动参数可以为空。Of course, the exercise parameters may be unnecessary. When the exercise parameters cannot be obtained from the sports health app or corresponding watches, bracelets and other devices, and the user does not input the exercise parameters, the exercise parameters can be empty.
具体实现中，可以优选通过运动健康类App或相应的手表、手环等设备中自动获取运动参数，当无法从运动健康类App或相应的手表、手环等设备中获取运动参数时，再通过用户输入运动参数，用户没有输入运动参数时，运动参数为空。In specific implementation, it is preferable to automatically obtain sports parameters through sports and health apps or corresponding watches, bracelets and other devices. When the sports parameters cannot be obtained from sports and health apps or corresponding watches, bracelets and other devices, pass The user enters the motion parameter, and the user does not enter the motion parameter, the motion parameter is empty.
用户输入运动参数具体可以为向用户显示供用户输入运动参数的输入界面，用户在输入界面上进行输入和/或选择等操作实现运动参数的输入。The user's input of the motion parameter may specifically be an input interface for the user to input the motion parameter displayed to the user, and the user performs input and/or selection operations on the input interface to realize the input of the motion parameter.
步骤S32:采集第二压力信号，具体可由体重测量设备采集；Step S32: Collect the second pressure signal, which can be collected by the body weight measurement device;
步骤S33：对第一压力信号依次进行高通放大处理和低通滤波处理；Step S33: sequentially perform high-pass amplification processing and low-pass filtering processing on the first pressure signal;
步骤S34：对进行高通放大处理和低通滤波处理后的第二压力信号进行低通滤波处理，得到运动前(静息态下)的身体平稳度BA2，以及对高通放大和低通滤波处理后的第二压力信号进行高通处理，得到用户运动前/静息态下的心率HR2、呼吸频率BR2、呼吸强度BI2；Step S34: Perform low-pass filter processing on the second pressure signal after the high-pass amplification processing and low-pass filter processing, to obtain the body stability BA2 before exercise (at rest), and after the high-pass amplification and low-pass filter processing Perform high-pass processing on the second pressure signal of the user to obtain the user's pre-exercise/rest heart rate HR2, respiratory rate BR2, and respiratory intensity BI2;
步骤S35：结合用户运动前(静息态下)的身体状态参数以及运动参数计算用户的肌肉疲劳度FM、心脏恢复指数RH和肺恢复指数RL。Step S35: Calculate the user's muscle fatigue FM, heart recovery index RH, and lung recovery index RL in combination with the body state parameters and exercise parameters of the user before exercise (at rest).
步骤S36：显示计算得到的肌肉疲劳度FM、心脏恢复指数RH和肺恢复指数RL，根据计算得到的肌肉疲劳度FM、心脏恢复指数RH和肺恢复指数RL给出运动量评估、身体机能评估、运动建议及身体恢复建议。Step S36: Display the calculated muscle fatigue FM, heart recovery index RH, and lung recovery index RL. According to the calculated muscle fatigue FM, heart recovery index RH, and lung recovery index RL, provide exercise volume evaluation, physical function evaluation, and exercise Suggestions and suggestions for body recovery.
需要说明的是，上述步骤S31-步骤S36为一个示例性的实现过程，有些步骤可以是非必要的或者可替代的，例如，步骤S36中的根据计算得到的肌肉疲劳度FM、心脏恢复指数RH和肺恢复指数RL给出运动量评估、身体机能评估、运动建议及身体恢复建议可以是非必要的，或者可以 只给出运动量评估、身体机能评估、运动建议及身体恢复建议中的一部分，例如只给出运动量评估和身体机能评估。It should be noted that the above steps S31 to S36 are an exemplary implementation process, and some steps may be unnecessary or alternative. For example, the muscle fatigue degree FM, the heart recovery index RH and the heart recovery index RH obtained from the calculation in step S36 The lung recovery index RL gives exercise volume assessment, physical function assessment, exercise advice, and physical recovery advice. It may be unnecessary, or it can only give exercise volume assessment, physical function assessment, exercise advice, and body recovery advice. For example, only Exercise volume assessment and physical function assessment.
图7为本发明实施例提供的一种示例性的测量用户运动前/运动后的身体状态参数的示意图。FIG. 7 is an exemplary schematic diagram of measuring a user's body state parameters before/after exercise according to an embodiment of the present invention.
如图7所示，以信号0为第一压力信号为例，根据第一压力信号确定用户在运动前的身体状态参数，包括：As shown in Figure 7, taking signal 0 as the first pressure signal as an example, the determination of the user's body state parameters before exercise according to the first pressure signal includes:
步骤S41：对第一压力信号(信号0)依次进行高通放大处理和低通滤波处理后得到第一信号(即图7所示的信号2)；Step S41: the first pressure signal (signal 0) is sequentially subjected to high-pass amplification processing and low-pass filtering processing to obtain the first signal (that is, signal 2 shown in FIG. 7);
其中，步骤S41还可以包括：对第一压力信号(信号0)进行低通滤波处理，得到体重信息(即图7所示的信号1)。Wherein, step S41 may further include: performing low-pass filtering processing on the first pressure signal (signal 0) to obtain weight information (that is, signal 1 shown in FIG. 7).
步骤S42(a)：将第一信号(信号2)进行高通滤波处理后得到运动后的心冲击信号BCG(信号3)，根据运动后的心冲击信号(信号3)确定用户运动前的心率HR1，根据运动前的心冲击信号(信号3)的波形变化确定运动前的心冲击信号(信号3)的波形轮廓，根据运动前的心冲击信号(信号3)的波形轮廓的峰值特征点计算运动前的呼吸频率BR1，以及根据运动前的心冲击信号(信号3)的波形轮廓的峰峰值计算运动前的呼吸强度BI1。Step S42(a): The first signal (signal 2) is subjected to high-pass filtering to obtain the post-exercise cardiac shock signal BCG (signal 3), and the user’s heart rate HR1 before exercise is determined according to the post-exercise cardiac shock signal (signal 3) , Determine the waveform contour of the pre-exercise cardiac shock signal (signal 3) according to the waveform change of the pre-exercise cardiac shock signal (signal 3), and calculate the exercise based on the peak feature points of the pre-exercise cardiac shock signal (signal 3) waveform contour The pre-exercise respiration frequency BR1 and the pre-exercise respiration intensity BI1 are calculated based on the peak-to-peak value of the waveform profile of the pre-exercise cardiac shock signal (signal 3).
步骤S42(b)：对第一信号(即图7所示的信号2)进行低通滤波处理，得到第二信号(即图7所示的信号4)，计算出第二信号(信号4)的主频、峰峰值及标准差，根据第二信号(信号4)的主频、峰峰值及标准差计算运动前的身体平稳度BA1，其中，身体平温度BA1的计算方法包括但不限于多元线性回归。Step S42(b): Perform low-pass filtering on the first signal (ie signal 2 shown in FIG. 7) to obtain a second signal (ie signal 4 shown in FIG. 7), and calculate the second signal (signal 4) The main frequency, peak-to-peak value and standard deviation of the second signal (signal 4) are used to calculate the pre-exercise body stability BA1 according to the main frequency, peak-to-peak value and standard deviation of the second signal (signal 4). Among them, the calculation method of the body flat temperature BA1 includes but is not limited to multiple Linear regression.
其中，步骤S42(a)和步骤S42(b)可以同时进行，也可以分先后次序进行，本发明实施例对先后次序不做限定。Among them, step S42(a) and step S42(b) can be performed simultaneously or in a sequence. The embodiment of the present invention does not limit the sequence.
继续如图7所示，以信号0为第二压力信号为例，根据第二压力信号确定用户在运动后的身体状态参数，包括：Continuing as shown in Figure 7, taking signal 0 as the second pressure signal as an example, the user’s body state parameters after exercise are determined according to the second pressure signal, including:
步骤S51：对第二压力信号(信号0)依次进行高通放大处理和低通滤波处理后得到第三信号(即图7所示的信号2)；Step S51: perform high-pass amplification and low-pass filtering on the second pressure signal (signal 0) in order to obtain a third signal (that is, signal 2 shown in FIG. 7);
其中，步骤S51还可以包括：对第二压力信号(信号0)进行低通滤波处理，得到体重信息(即图7所示的信号1)。Wherein, step S51 may further include: performing low-pass filtering processing on the second pressure signal (signal 0) to obtain weight information (that is, signal 1 shown in FIG. 7).
步骤S52(a)：将第三信号(信号2)进行高通滤波处理后得到运动后的心冲击信号BCG(信号3)，根据运动后的心冲击信号(信号3)确定用户运动后的心率HR2，根据运动后的心冲击信号(信号3)的波形变化确定运动后的心冲击信号(信号3)的波形轮廓，根据运动后的心冲击信号(信号3)的波形轮廓的峰值特征点计算运动后的呼吸频率BR2，以及根据运动后的心冲击信号(信号3)的波形轮廓的峰峰值计算运动后的呼吸强度BI2。Step S52(a): Perform high-pass filtering on the third signal (signal 2) to obtain the post-exercise cardiac shock signal BCG (signal 3), and determine the user’s post-exercise heart rate HR2 according to the post-exercise cardiac shock signal (signal 3) , Determine the waveform contour of the cardiac shock signal (signal 3) after exercise according to the waveform change of the cardiac shock signal (signal 3) after exercise, and calculate the movement based on the peak feature points of the waveform contour of the cardiac shock signal (signal 3) after exercise The post-exercise respiration frequency BR2 and the post-exercise respiration intensity BI2 are calculated based on the peak-to-peak value of the waveform profile of the post-exercise cardiac shock signal (signal 3).
步骤S52(b)：对第三信号(即图7所示的信号2)进行低通滤波处理，得到第四信号(即图7所示的信号4)，计算出第四信号(信号4)的主频、峰峰值及标准差，根据第四信号(信号4)的主频、峰峰值及标准差计算运动后的身体平稳度BA2，其中，身体平温度BA2的计算方法包括但不限于多元线性回归。Step S52(b): Perform low-pass filtering on the third signal (ie signal 2 shown in FIG. 7) to obtain a fourth signal (ie signal 4 shown in FIG. 7), and calculate the fourth signal (signal 4) The main frequency, peak-to-peak value and standard deviation of the fourth signal (signal 4) are used to calculate the post-exercise body stability BA2 according to the main frequency, peak-to-peak value and standard deviation of the fourth signal (signal 4). Among them, the calculation method of body flat temperature BA2 includes but is not limited to multiple Linear regression.
其中，步骤S52(a)和步骤S52(b)可以同时进行，也可以分先后次序进行，本发明实施例对先后次序不做限定。Among them, step S52(a) and step S52(b) can be performed simultaneously or in a sequence. The embodiment of the present invention does not limit the sequence.
可以理解，图7所示的实施例通过体重测量设备测量人体对其的压力，生成压力信号，将压 力信号经过不同的高通、低通、放大处理，解析出体重、心率、呼吸频率、呼吸强度、身体平稳度等多种信号，无需额外的传感器，只需要压力信号即可获得多种人体生理信号(即身体状态参数)。It can be understood that the embodiment shown in FIG. 7 measures the pressure of the human body against it by a weight measuring device, generates a pressure signal, and processes the pressure signal through different high-pass, low-pass, and amplifying processes to analyze the weight, heart rate, respiratory frequency, and respiratory intensity. , Body stability and other signals, without additional sensors, only pressure signals can be used to obtain a variety of human physiological signals (that is, body state parameters).
在本发明实施例中，体重测量设备或者终端设备可以采用以下方式计算用户的肌肉疲劳度、心脏恢复指数及肺恢复指数。In the embodiment of the present invention, the weight measurement device or the terminal device may use the following methods to calculate the user's muscle fatigue, heart recovery index, and lung recovery index.
方式一：通过加权平均算法。Method 1: Through the weighted average algorithm.
具体实现中，采用加权算法计算用户的肌肉疲劳度FM可以包括：In specific implementation, using a weighting algorithm to calculate the user's muscle fatigue FM may include:
步骤S61：利用运动前的身体平稳度BA1、心率HR1、呼吸频率BR1、呼吸强度BI1，和运动后的身体平稳度BA2、心率HR2、呼吸频率BR2、呼吸强度BI2，分别计算运动前后的生理参数差值(身体平稳度差值BA、心率差值HR、呼吸频率差值BR及呼吸强度差值BI)。Step S61: Calculate the physiological parameters before and after exercise by using the body stability before exercise BA1, the heart rate HR1, the respiratory frequency BR1, the respiratory intensity BI1, and the post exercise body stability BA2, the heart rate HR2, the respiratory frequency BR2, and the respiratory intensity BI2. Difference (body stability difference BA, heart rate difference HR, respiratory rate difference BR, and respiratory intensity difference BI).
其中，身体平稳度差值BA＝BA2-BA1，心率差值HR＝HR2-HR1，呼吸频率差值BR＝BR2-BR1，呼吸强度差值BI＝BI2-BI1。Among them, the body stability difference BA=BA2-BA1, the heart rate difference HR=HR2-HR1, the respiratory rate difference BR=BR2-BR1, and the respiratory intensity difference BI=BI2-BI1.
步骤S62：利用加权平均算法，根据身体平稳度差值BA、心率差值HR、呼吸频率差值BR及呼吸强度差值BI计算肌肉疲劳度FM。Step S62: Using a weighted average algorithm, calculate the muscle fatigue FM based on the body stability difference BA, the heart rate difference HR, the respiratory rate difference BR, and the respiratory intensity difference BI.
具体地，FM＝a1*BA+a2*HR+a3*BR+a4*BI；Specifically, FM=a1*BA+a2*HR+a3*BR+a4*BI;
其中，a1，a2，a3，a4为系数，可以根据实际需求灵活设置。Among them, a1, a2, a3, and a4 are coefficients, which can be flexibly set according to actual needs.
具体实现中，采用加权算法计算用户的心脏恢复指数RH可以包括：In specific implementation, using a weighting algorithm to calculate the user's heart recovery index RH may include:
步骤S71：利用运动前的身体平稳度BA1、心率HR1、呼吸频率BR1、呼吸强度BI1，和运动后的身体平稳度BA2、心率HR2、呼吸频率BR2、呼吸强度BI2，分别计算运动前后的生理参数差值(身体平稳度差值BA、心率差值HR、呼吸频率差值BR及呼吸强度差值BI)。Step S71: Calculate the physiological parameters before and after exercise by using body stability BA1, heart rate HR1, breathing rate BR1, breathing intensity BI1, and body stability after exercise BA2, heart rate HR2, breathing frequency BR2, and breathing intensity BI2 Difference (body stability difference BA, heart rate difference HR, respiratory rate difference BR, and respiratory intensity difference BI).
步骤S72：利用加权平均算法，根据身体平稳度差值BA、心率差值HR、呼吸频率差值BR及呼吸强度差值BI，并结合运动类型s1、运动强度m1、运动时长t1、运动后时间T1、运动参数可信度w1计算心脏恢复指数RH。Step S72: Using weighted average algorithm, according to body stability difference BA, heart rate difference HR, respiratory rate difference BR and respiratory intensity difference BI, combined with exercise type s1, exercise intensity m1, exercise duration t1, and post-exercise time T1, exercise parameter credibility w1 to calculate the heart recovery index RH.
具体地，RH＝b1*BA+b2*HR+b3*BR+b4*BI+b5*s1+b6*m1+b7*t1+b8*T1+b9*w1；Specifically, RH=b1*BA+b2*HR+b3*BR+b4*BI+b5*s1+b6*m1+b7*t1+b8*T1+b9*w1;
其中，b1，b2，b3，b4，b5，b6，b7，b8，b9为系数，可以根据实际需求灵活设置。Among them, b1, b2, b3, b4, b5, b6, b7, b8, and b9 are coefficients, which can be flexibly set according to actual needs.
具体实现中，采用加权算法计算用户的肺恢复指数RL可以包括：In a specific implementation, calculating the user's lung recovery index RL by using a weighting algorithm may include:
步骤S81：利用运动前的身体平稳度BA1、心率HR1、呼吸频率BR1、呼吸强度BI1，和运动后的身体平稳度BA2、心率HR2、呼吸频率BR2、呼吸强度BI2，分别计算运动前后的生理参数差值(身体平稳度差值BA、心率差值HR、呼吸频率差值BR及呼吸强度差值BI)。Step S81: Use the body stability before exercise BA1, heart rate HR1, breathing rate BR1, breathing intensity BI1, and the body stability after exercise BA2, heart rate HR2, breathing frequency BR2, and breathing intensity BI2 to calculate the physiological parameters before and after exercise. Difference (body stability difference BA, heart rate difference HR, respiratory rate difference BR, and respiratory intensity difference BI).
步骤S82：利用加权平均算法，根据身体平稳度差值BA、心率差值HR、呼吸频率差值BR及呼吸强度差值BI，并结合运动类型s、运动强度m、运动时长t、运动后时间T、运动参数可信度w计算心脏恢复指数RL。Step S82: Using weighted average algorithm, according to body stability difference BA, heart rate difference HR, respiratory rate difference BR and respiratory intensity difference BI, combined with exercise type s, exercise intensity m, exercise duration t, and post-exercise time T, exercise parameter credibility w, calculate the heart recovery index RL.
具体地，RL＝c1*BA+c2*HR+c3*BR+c4*BI+c5*s+c6*m+c7*t+c8*T+c9*w；Specifically, RL=c1*BA+c2*HR+c3*BR+c4*BI+c5*s+c6*m+c7*t+c8*T+c9*w;
其中，c1，c2，c3，c4，c5，c6，c7，c8，c9为系数，可以根据实际需求灵活设置。Among them, c1, c2, c3, c4, c5, c6, c7, c8, and c9 are coefficients, which can be flexibly set according to actual needs.
可以理解，本发明实施例通过计算运动前后身体平稳度、心率、呼吸频率、呼吸强度等生理参数的差值，表征运动前后的生理性能变化，再结合运动参数，使用加权平均算法计算用户运动后的肌肉疲劳度、心脏恢复能力及肺恢复能力，准确度高且计算简便。It can be understood that the embodiment of the present invention calculates the difference in physiological parameters such as body stability, heart rate, breathing frequency, and breathing intensity before and after exercise to characterize changes in physiological performance before and after exercise. Combined with exercise parameters, a weighted average algorithm is used to calculate the user’s post-exercise. The muscular fatigue, heart recovery ability and lung recovery ability are highly accurate and easy to calculate.
方式二：通过神经网络。Method 2: Through neural network.
通过构建神经网络，然后对构建的神经网络进行训练，得到训练后的神经网络，然后根据训练后的神经网络对肌肉疲劳度FM、心脏恢复指数RH及心脏恢复指数RL。By constructing a neural network, and then training the constructed neural network, the trained neural network is obtained, and then the muscle fatigue FM, the heart recovery index RH and the heart recovery index RL are calculated according to the trained neural network.
其中，对神经网络模型进行训练时的训练集可以包括输入训练集和输入训练集：其中，输入训练集可以包括：用户运动前后的生理参数差值(身体平稳度差值BA、心率差值HR、呼吸频率差值BR、呼吸强度差值BI)和运动参数(运动类型s、运动强度m、运动时长t、运动后时间T、运动参数可信度w)；输出训练集可以包括：肌肉疲劳度FM、心脏恢复能力RH和肺恢复能力RL，其中，肌肉疲劳度FM可以采用血液乳酸法测量，也可以采用肌电法测量。心脏恢复能力RH和肺恢复能力RL可以采用运动测试机测量得到。Among them, the training set when training the neural network model may include the input training set and the input training set: where the input training set may include: the physiological parameter difference before and after the user exercise (body stability difference BA, heart rate difference HR , Breathing rate difference BR, breathing intensity difference BI) and exercise parameters (exercise type s, exercise intensity m, exercise duration t, post-exercise time T, exercise parameter credibility w); the output training set can include: muscle fatigue Degree FM, heart recovery capacity RH and lung recovery capacity RL, among which muscle fatigue FM can be measured by blood lactic acid method or electromyography method. The heart recovery capacity RH and the lung recovery capacity RL can be measured by an exercise testing machine.
通过对神经网络进行训练，可以得到神经网络的计算参数对于新的输入数据，利用神经网络中的参数计算肌肉疲劳度FM、心脏恢复能力RH、肺恢复能力RL。By training the neural network, the calculation parameters of the neural network can be obtained. For new input data, the parameters in the neural network are used to calculate muscle fatigue FM, heart recovery capacity RH, and lung recovery capacity RL.
图8为本发明实施例提供的一种通过神经网络计算肌肉疲劳度FM、心脏恢复指数RH和肺恢复指数RL的示意图；8 is a schematic diagram of calculating muscle fatigue FM, heart recovery index RH, and lung recovery index RL through neural network according to an embodiment of the present invention;
如图8所示，训练后的神经网络的输入信息可以包括：用户运动前后的生理参数差值(身体平稳度差值BA、心率差值HR、呼吸频率差值BR、呼吸强度差值BI)和运动参数(运动类型s、运动强度m、运动时长t、运动后时间T、运动参数可信度w)。As shown in Figure 8, the input information of the neural network after training may include: the physiological parameter difference before and after the user exercise (body stability difference BA, heart rate difference HR, respiratory rate difference BR, respiratory intensity difference BI) And exercise parameters (exercise type s, exercise intensity m, exercise duration t, post-exercise time T, exercise parameter credibility w).
训练后的神经网络通过上述输入信息可以计算得到肌肉疲劳度FM、心脏恢复指数RH和肺恢复指数RL并输出。The trained neural network can calculate the muscle fatigue FM, the heart recovery index RH and the lung recovery index RL through the above input information and output them.
可以理解，本发明实施例通过采用神经网络计算肌肉疲劳度FM、心脏恢复指数RH和肺恢复指数RL，随着运动健康、可穿戴设备的发展，可测量的人体生理参数会逐步增加，使用神经网络可以有效地进行多参数融合以获得更深层的生理参数。It can be understood that the embodiment of the present invention uses a neural network to calculate muscle fatigue FM, heart recovery index RH, and lung recovery index RL. With the development of sports health and wearable devices, the measurable physiological parameters of the human body will gradually increase. The network can effectively perform multi-parameter fusion to obtain deeper physiological parameters.
下面结合附图对本发明实施例提供的运动后肌肉疲劳度的检测装置的界面交互过程进行说明。The interface interaction process of the device for detecting muscle fatigue after exercise provided by the embodiments of the present invention will be described below with reference to the accompanying drawings.
图9为本发明实施例提供的一种示例性的运动后肌肉疲劳度的检测装置运动前和运动后的状态选择界面；9 is a state selection interface before and after exercise of an exemplary device for detecting muscle fatigue after exercise provided by an embodiment of the present invention;
用户可以通过状态选择界面选择用户的状态，是运动前的静息态，还是运动后的状态，如果用户是运动前的静息态，则可以选择图9中的【安静态】，如果用户是运动后的状态，则可以选择图9中的【运动后】。The user can select the state of the user through the state selection interface, whether it is the resting state before exercise or the state after exercise. If the user is in the resting state before exercise, they can select [An static state] in Figure 9, if the user is For the state after exercise, you can select [After exercise] in Figure 9.
图10本发明实施例提供的一种示例性的运动参数自动获取的设置界面；Fig. 10 is an exemplary setting interface for automatic acquisition of sports parameters provided by an embodiment of the present invention;
运动参数自动获取的设置界面用于向用户展示是否自动从运动健康App中获取运动参数，运动参数包括运动类型s、运动强度m、运动时长t、运动后时间T、运动参数可信度w中的一种或者任意多种。The setting interface for automatic acquisition of exercise parameters is used to show the user whether to automatically acquire exercise parameters from the Sports Health App. The exercise parameters include exercise type s, exercise intensity m, exercise duration t, post-exercise time T, and exercise parameter credibility w. One or any number of.
如果用户选择图10中的【是】，则运动后肌肉疲劳度的检测装置将设置为从运动健康App中获取运动参数。If the user selects [Yes] in Fig. 10, the device for detecting muscle fatigue after exercise will be set to obtain exercise parameters from the Sports Health App.
如果运动后肌肉疲劳度的检测装置没有检测到运动数据，则可以向用户展示如图11所示的手动输入选择界面，如果用户选择图10中的【否】或者图11中的【是】，则可以向用户展示如图12所示的运动参数输入界面，用户通过该界面可手动输入或者设置运动参数等，具体可以让用户选择或者输入运动类型s、运动强度m和/或运动时长t、运动后时间(运动结束时间)T。If the muscle fatigue detection device does not detect the exercise data after exercise, the manual input selection interface as shown in Figure 11 can be shown to the user. If the user selects [No] in Figure 10 or [Yes] in Figure 11, Then the user can be shown the exercise parameter input interface shown in Figure 12, through which the user can manually input or set the exercise parameters, etc., specifically allowing the user to select or input the exercise type s, exercise intensity m and/or exercise duration t, Post-exercise time (exercise end time) T.
具体地，可以使用下拉菜单的方式让用户选择运动类型以及运动后时间(运动结束时间)T，运动类型例如跑步、游泳、深蹲、打篮球等等，当用户选择运动类型后，根据用户所选择的运动 类型让用户选择或者输入用户所选择的运动类型的运动强度/时长。例如，如果用户所选择的运动类型为跑步/游泳，则展开让用户选择或者设置跑步时长的菜单。如果用户所选择的运动类型为深蹲，则展开让用户选择或者设置深蹲次数的菜单，具体可参见图12和图13，图12和图13提供了两种运动参数输入界面示意图。Specifically, a drop-down menu can be used to allow the user to select the type of exercise and the time after exercise (exercise end time) T, such as running, swimming, squatting, basketball, etc. When the user selects the type of exercise, according to the user’s The selected exercise type allows the user to select or input the exercise intensity/duration of the exercise type selected by the user. For example, if the type of exercise selected by the user is running/swimming, expand the menu for the user to select or set the length of running time. If the exercise type selected by the user is squat, the menu for the user to select or set the number of squats is expanded. For details, please refer to Figure 12 and Figure 13. Figure 12 and Figure 13 provide schematic diagrams of two exercise parameter input interfaces.
图14为本发明实施例提供的一种示例性的测量结果显示界面；FIG. 14 is an exemplary measurement result display interface provided by an embodiment of the present invention;
如图14所示，测量结果显示界面向用户展示有：用户的体重、心率HR2、心脏恢复指数RH、呼吸频率BR2、呼吸强度BI2、肺恢复指数RH、身体平稳度BA2、肌肉疲劳度FM以及身体恢复和运动建议。As shown in Figure 14, the measurement result display interface shows the user: the user’s weight, heart rate HR2, heart recovery index RH, breathing rate BR2, breathing intensity BI2, lung recovery index RH, body stability BA2, muscle fatigue FM, and Physical recovery and exercise recommendations.
当然，图14仅作为一种示例性的实施例，在其它实施例中，可以仅展示用户的体重、心率HR2、心脏恢复指数RH、呼吸频率BR2、呼吸强度BI2、肺恢复指数RH、身体平稳度BA2、肌肉疲劳度FM以及身体恢复和运动建议中的部分信息或者还可以向用户展示其它的信息。Of course, FIG. 14 is only an exemplary embodiment. In other embodiments, it may only display the user's weight, heart rate HR2, heart recovery index RH, respiratory rate BR2, respiratory intensity BI2, lung recovery index RH, and body stability. The degree of BA2, the degree of muscle fatigue FM, and some of the information in the body recovery and exercise recommendations or other information may also be shown to the user.
可以理解，本发明实施例通过向用户显示检测结果，通过人机交互的方式便于用户了解当前身体状态，同时为用户提供运动和身体恢复上的指导，能够避免用户进行强度、时间不合理的运动训练以及引导用户快速恢复身体机能。It can be understood that the embodiment of the present invention displays the detection results to the user, facilitates the user to understand the current physical state through human-computer interaction, and provides the user with guidance on exercise and physical recovery, which can prevent the user from exercising with unreasonable intensity and time. Train and guide users to quickly restore physical functions.
本申请实施例还公开了一种运动后肌肉疲劳度的检测装置，运动后肌肉疲劳度的检测装置可以是上文的体重测量设备，也可以是上文的终端设备，应理解，装置400能够执行运动后肌肉疲劳度的检测方法中的各个步骤，为了避免重复，此处不再详述。如图14所示，装置400包括：第一获取模块410及第一计算模块420。The embodiment of the application also discloses a device for detecting muscle fatigue after exercise. The device for detecting muscle fatigue after exercise may be the above body weight measurement device or the above terminal device. It should be understood that the device 400 can Perform each step in the method for detecting muscle fatigue after exercise, in order to avoid repetition, it will not be described in detail here. As shown in FIG. 14, the device 400 includes: a first acquisition module 410 and a first calculation module 420.
第一获取模块410，用于获取用户运动前的身体状态参数以及运动后的身体状态参数，其中，身体状态参数包括身体平稳度、心率、呼吸频率及呼吸强度；以及The first acquiring module 410 is used to acquire the body state parameters of the user before exercise and the body state parameters after exercise, where the body state parameters include body stability, heart rate, breathing rate, and breathing intensity; and
第一计算模块420，用于根据用户运动前的身体状态参数以及用户在运动后的身体状态参数计算用户运动后的肌肉疲劳度。The first calculation module 420 is configured to calculate the user's muscle fatigue after exercise according to the user's body state parameters before exercise and the user's body state parameters after exercise.
可以理解，本发明实施例通过获取用户运动前的身体状态参数以及运动后的身体状态参数，然后根据用户运动前的身体状态参数以及用户在运动后的身体状态参数计算用户运动后的肌肉疲劳度，无需采集用户的血液且操作简单，用户接受度高，同时，依靠体重测量设备就能够实现肌肉疲劳度检测，无需专门的检测设备。It can be understood that the embodiment of the present invention obtains the user's body state parameters before exercise and post-exercise body state parameters, and then calculates the user's muscle fatigue after exercise according to the user's body state parameters before exercise and the user's body state parameters after exercise. , No need to collect the user's blood, simple operation, high user acceptance, and at the same time, the muscle fatigue detection can be achieved by relying on the weight measurement equipment, without the need for special testing equipment.
在一种可行的实现方式中，第一获取模块410可以包括：In a feasible implementation manner, the first obtaining module 410 may include:
第一获取单元，用于接收体重测量设备生成的第一压力信号，根据第一压力信号确定用户在运动前的身体状态参数，其中，第一压力信号由体重测量设备在用户运动前使用体重测量设备进行体重测量时产生；以及The first acquisition unit is configured to receive the first pressure signal generated by the weight measurement device, and determine the user's body state parameter before exercise according to the first pressure signal, wherein the first pressure signal is used by the weight measurement device to measure the weight of the user before the exercise Generated when the device is taking weight measurements; and
第二获取单元，用于接收体重测量设备生成的第二压力信号，根据第二压力信号确定用户运动后的身体状态参数，其中，第二压力信号由体重测量设备在用户运动后使用体重测量设备进行体重测量时产生。The second acquisition unit is configured to receive a second pressure signal generated by the weight measurement device, and determine the body state parameter of the user after exercise according to the second pressure signal, wherein the second pressure signal is used by the weight measurement device after the user exercises the weight measurement device Generated when taking a weight measurement.
在一种可行的实现方式中，第一获取模块410可以包括：In a feasible implementation manner, the first obtaining module 410 may include:
第一确定单元，用于在用户运动前，对用户进行体重测量，根据用户所施加的压力生成第一压力信号，根据第一压力信号确定用户在运动前的身体状态参数；以及The first determining unit is configured to measure the weight of the user before the user exercises, generate a first pressure signal according to the pressure applied by the user, and determine the physical state parameters of the user before the exercise according to the first pressure signal; and
第二确定单元，用于在用户运动后，对用户进行体重测量，根据用户所施加的压力生成第二压力信号，根据第二压力信号确定用户在运动后的身体状态参数。The second determining unit is configured to measure the weight of the user after the user exercises, generate a second pressure signal according to the pressure applied by the user, and determine the body state parameter of the user after the exercise according to the second pressure signal.
在一种可行的实现方式中，装置400还可以包括：In a feasible implementation manner, the apparatus 400 may further include:
第二获取模块，用于获取用户运动对应的运动参数；以及The second acquiring module is used to acquire the motion parameters corresponding to the user's motion; and
第二计算模块，用于根据用户运动前的身体状态参数、用户运动后的身体状态参数以及用户的运动参数计算用户的心脏恢复指数及肺恢复指数。The second calculation module is used to calculate the user's heart recovery index and lung recovery index according to the user's body state parameters before exercise, the user's body state parameters after exercise, and the user's exercise parameters.
在一种可行的实现方式中，第一计算模块410可以包括：In a feasible implementation manner, the first calculation module 410 may include:
第一计算单元，用于根据用户运动前的身体状态参数、用户在运动后的身体状态参数以及用户的运动参数计算用户运动后的肌肉疲劳度。The first calculation unit is used to calculate the user's muscle fatigue after exercise according to the user's body state parameters before exercise, the user's body state parameters after exercise, and the user's exercise parameters.
在一种可行的实现方式中，运动参数包括运动类型、运动强度、运动时长、运动后时长及运动参数可信度中的一种或者任意多种。In a feasible implementation manner, the exercise parameter includes one or more of exercise type, exercise intensity, exercise duration, post-exercise duration, and exercise parameter credibility.
在一种可行的实现方式中，第一获取模块或者第一确定模块可以包括：In a feasible implementation manner, the first acquiring module or the first determining module may include:
第一处理单元，用于对第一压力信号依次进行高通放大处理和低通滤波处理后得到第一信号，将第一信号进行高通滤波处理后得到运动前的心冲击信号，根据运动前的心冲击信号确定运动前的心率，根据运动前的心冲击信号的波形变化确定运动前的心冲击信号的波形轮廓，根据运动前的心冲击信号的波形轮廓的峰值特征点计算运动前的呼吸频率以及根据运动前的心冲击信号的波形轮廓的峰峰值计算运动前的呼吸强度；以及The first processing unit is used to sequentially perform high-pass amplification and low-pass filtering on the first pressure signal to obtain the first signal, perform high-pass filtering on the first signal to obtain the pre-exercise cardiac shock signal, and according to the pre-exercise cardiac The shock signal determines the heart rate before exercise, the waveform profile of the pre-exercise cardiac shock signal is determined according to the waveform change of the pre-exercise cardiac shock signal, and the pre-exercise respiration frequency is calculated according to the peak feature points of the waveform profile of the pre-exercise cardiac shock signal and Calculate the pre-exercise respiration intensity based on the peak-to-peak value of the waveform profile of the pre-exercise cardiac shock signal; and
第二处理单元，用于对第一信号进行低通滤波处理，得到第二信号，计算出第二信号的主频、峰峰值及标准差，根据第二信号的主频、峰峰值及标准差计算运动前的身体平稳度。The second processing unit is used to perform low-pass filter processing on the first signal to obtain the second signal, calculate the dominant frequency, peak-to-peak value and standard deviation of the second signal, according to the dominant frequency, peak-to-peak value and standard deviation of the second signal Calculate the body stability before exercise.
在一种可行的实现方式中，第二获取模块或者第二确定模块可以包括：In a feasible implementation manner, the second acquiring module or the second determining module may include:
第三处理单元，用于对第二压力信号依次进行高通放大处理和低通滤波处理后得到第三信号，将第三信号进行高通滤波处理后得到运动后的心冲击信号，根据运动后的心冲击信号确定运动后的心率，根据运动后的心冲击信号的波形变化确定运动后的心冲击信号的波形轮廓，根据运动后的心冲击信号的波形轮廓的峰值特征点计算运动后的呼吸频率以及根据运动后的心冲击信号的波形轮廓的峰峰值计算运动后的呼吸强度；以及The third processing unit is used to sequentially perform high-pass amplification processing and low-pass filter processing on the second pressure signal to obtain a third signal, perform high-pass filter processing on the third signal to obtain a post-exercise cardiac shock signal, according to the post-exercise heart The shock signal determines the heart rate after exercise, the waveform profile of the cardiac shock signal after exercise is determined according to the waveform change of the cardiac shock signal after exercise, and the respiration frequency after exercise is calculated according to the peak feature points of the waveform contour of the cardiac shock signal after exercise. Calculate the post-exercise respiration intensity based on the peak-to-peak value of the waveform profile of the post-exercise cardiac shock signal; and
第四处理单元，用于对第三信号进行低通滤波处理，得到第四信号，计算出第四信号的主频、峰峰值及标准差，根据第四信号的主频、峰峰值及标准差计算运动后的身体平稳度。The fourth processing unit is used to perform low-pass filter processing on the third signal to obtain the fourth signal, calculate the dominant frequency, peak-to-peak value and standard deviation of the fourth signal, according to the dominant frequency, peak-to-peak value and standard deviation of the fourth signal Calculate the body stability after exercise.
在一种可行的实现方式中，第一计算模块420可以包括：In a feasible implementation manner, the first calculation module 420 may include:
第二计算单元，用于计算用户运动前的身体平稳度与用户运动后的身体平稳度之间的身体平稳度差值，以及计算用户运动前的心率与用户运动后的心率之间的心率差值，以及计算用户运动前的呼吸频率与用户运动后的呼吸频率之间的呼吸频率差值，以及计算用户运动前的呼吸强度与用户运动后的呼吸强度之间的呼吸强度差值；以及The second calculation unit is used to calculate the body stability difference between the user's body stability before exercise and the user's body stability after exercise, and to calculate the heart rate difference between the user's heart rate before exercise and the user's heart rate after exercise Calculate the difference between the user's breathing rate before exercise and the user's breathing rate after exercise, and calculate the difference between the user's breathing intensity before exercise and the user's breathing intensity after exercise; and
第三计算单元，用于根据身体平稳度差值、心率差值、呼吸频率差值及呼吸强度差值，采用加权平均算法计算用户运动后的肌肉疲劳度。The third calculation unit is used to calculate the user's muscle fatigue after exercise by using a weighted average algorithm based on the difference in body stability, the difference in heart rate, the difference in respiratory frequency, and the difference in respiratory intensity.
在一种可行的实现方式中，第二计算单元可以包括：In a feasible implementation manner, the second calculation unit may include:
第一计算子单元，用于根据身体平稳度差值、心率差值、呼吸频率差值及呼吸强度差值，并结合用户的运动参数，采用加权平均算法计算用户运动后的心脏恢复指数及肺恢复指数。The first calculation subunit is used to calculate the heart recovery index and lungs of the user after exercise by using the weighted average algorithm according to the difference in body stability, heart rate, respiratory rate, and respiratory intensity, and combined with the user's exercise parameters. Recovery index.
在一种可行的实现方式中，第一计算单元可以包括：In a feasible implementation manner, the first calculation unit may include:
第二计算子单元，用于计算用户运动前的身体平稳度与用户运动后的身体平稳度之间的身体平稳度差值，以及计算用户运动前的心率与用户运动后的心率之间的心率差值，以及计算用户运动前的呼吸频率与用户运动后的呼吸频率之间的呼吸频率差值，以及计算用户运动前的呼吸强度 与用户运动后的呼吸强度之间的呼吸强度差值；以及The second calculation subunit is used to calculate the body stability difference between the user's body stability before exercise and the user's body stability after exercise, and to calculate the heart rate between the user's heart rate before exercise and the user's heart rate after exercise Difference, and calculating the difference between the user's respiration rate before exercise and the user’s respiration rate after exercise, and calculating the difference between the user’s respiration intensity before exercise and the user’s respiration intensity after exercise; and
第三计算子单元，用于根据身体平稳度差值、心率差值、呼吸频率差值及呼吸强度差值，结合用户的运动参数，使用神经网络计算用户的肌肉疲劳度、心脏恢复指数及肺恢复指数。The third calculation subunit is used to calculate the user’s muscle fatigue, heart recovery index, and lungs based on the difference in body stability, heart rate, respiratory rate, and respiratory intensity, combined with the user’s exercise parameters, and the neural network. Recovery index.
在一种可行的实现方式中，装置400还可以包括：In a feasible implementation manner, the apparatus 400 may further include:
第一显示模块，用于显示计算得到的用户的肌肉疲劳度、心脏恢复指数及肺恢复指数中的任意一个或者任意多个，以及根据用户的肌肉疲劳度、心脏恢复指数及肺恢复指数中的一个或者任意多个生成并显示运动量评估、身体机能评估、运动建议、身体恢复建议中的一个或者任意多个。The first display module is used to display any one or more of the calculated user’s muscle fatigue, heart recovery index, and lung recovery index, and according to the user’s muscle fatigue, heart recovery index, and lung recovery index. One or any number of generating and displaying one or any number of exercise volume assessment, physical function assessment, exercise suggestion, and physical recovery suggestion.
可以理解，本发明实施例通过向用户显示检测结果，通过人机交互的方式便于用户了解当前身体状态，同时为用户提供运动和身体恢复上的指导，能够避免用户进行强度、时间不合理的运动训练以及引导用户快速恢复身体机能。It can be understood that the embodiment of the present invention displays the detection results to the user, facilitates the user to understand the current physical state through human-computer interaction, and provides the user with guidance on exercise and physical recovery, which can prevent the user from exercising with unreasonable intensity and time. Train and guide users to quickly restore physical functions.
本申请还提供的一种电子设备，如图1所示，电子设备100包括存储器121及处理器110，存储器121中存储有计算机程序，处理器110与存储器121连接，处理器110执行计算机程序以实现如上述的运动后肌肉疲劳度的检测方法。This application also provides an electronic device. As shown in FIG. 1, the electronic device 100 includes a memory 121 and a processor 110. The memory 121 stores a computer program, the processor 110 is connected to the memory 121, and the processor 110 executes the computer program to Realize the above-mentioned method for detecting muscle fatigue after exercise.
本申请还提供的一种体重测量设备，体重测量设备包括存储器及处理器，存储器中存储有计算机程序，处理器与存储器连接，处理器执行计算机程序以实现如上述的运动后肌肉疲劳度的检测方法。This application also provides a body weight measurement device, which includes a memory and a processor, and a computer program is stored in the memory. The processor is connected to the memory, and the processor executes the computer program to realize the detection of muscle fatigue after exercise as described above. method.
本申请还提供了一种计算机存储介质，包括计算机指令，当计算机指令在电子设备100或者体重测量设备上运行时，使得电子设备100或者体重测量设备执行如上述的运动后肌肉疲劳度的检测方法中的各个步骤。This application also provides a computer storage medium, including computer instructions. When the computer instructions run on the electronic device 100 or the weight measurement device, the electronic device 100 or the weight measurement device executes the above-mentioned method for detecting muscle fatigue after exercise. The various steps in.
本申请还提供了一种计算机程序产品，当计算机程序产品在计算机上运行时，该计算机程序产品在计算机上运行时，使得计算机执行上述运动后肌肉疲劳度的检测方法中的各个步骤。The present application also provides a computer program product. When the computer program product runs on a computer, when the computer program product runs on the computer, the computer executes the steps in the above-mentioned post-exercise muscle fatigue detection method.
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的***、装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。Those skilled in the art can clearly understand that, for the convenience and conciseness of description, the specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiment, which will not be repeated here.
以上，仅为本申请的具体实施方式，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。本申请的保护范围应以权利要求的保护范围为准。The above are only specific implementations of this application. Any person skilled in the art can easily conceive of changes or replacements within the technical scope disclosed in this application, which should be covered by the protection scope of this application. The protection scope of this application shall be subject to the protection scope of the claims.

Claims (28)

  1. 一种运动后肌肉疲劳度的检测方法，其特征在于，所述方法包括：A method for detecting muscle fatigue after exercise, characterized in that the method comprises:
    获取用户运动前的身体状态参数以及运动后的身体状态参数，其中，所述身体状态参数包括身体平稳度、心率、呼吸频率及呼吸强度；Acquiring the body state parameters of the user before exercise and the body state parameters after exercise, where the body state parameters include body stability, heart rate, breathing frequency, and breathing intensity;
    根据所述用户运动前的身体状态参数以及所述用户在运动后的身体状态参数计算所述用户运动后的肌肉疲劳度。The muscle fatigue degree of the user after exercise is calculated according to the body state parameter of the user before exercise and the body state parameter of the user after exercise.
  2. 根据权利要求1所述的方法，其特征在于，所述获取所述用户运动前的身体状态参数，包括：The method according to claim 1, wherein said obtaining the physical state parameters of the user before exercise comprises:
    接收体重测量设备生成的第一压力信号，根据所述第一压力信号确定所述用户在运动前的身体状态参数，其中，所述第一压力信号由体重测量设备在所述用户运动前使用所述体重测量设备进行体重测量时产生；Receive a first pressure signal generated by a weight measurement device, and determine the physical state parameter of the user before exercise according to the first pressure signal, wherein the first pressure signal is used by the weight measurement device before the user exercises Said weight measurement device is generated when the weight measurement is performed;
    所述获取所述用户运动后的身体状态参数，包括：The acquiring the physical state parameters of the user after exercise includes:
    接收所述体重测量设备生成的第二压力信号，根据所述第二压力信号确定所述用户运动后的身体状态参数，其中，所述第二压力信号由体重测量设备在所述用户运动后使用所述体重测量设备进行体重测量时产生。Receive a second pressure signal generated by the weight measurement device, and determine the body state parameter of the user after exercise according to the second pressure signal, wherein the second pressure signal is used by the weight measurement device after the user exercises The weight measurement device is generated when the weight measurement is performed.
  3. 根据权利要求1所述的方法，其特征在于，所述获取所述用户运动前的身体状态参数，包括：The method according to claim 1, wherein said obtaining the physical state parameters of the user before exercise comprises:
    在所述用户运动前，对所述用户进行体重测量，根据所述用户所施加的压力生成第一压力信号，根据所述第一压力信号确定所述用户在运动前的身体状态参数；Before the user exercises, measure the weight of the user, generate a first pressure signal according to the pressure applied by the user, and determine the physical state parameters of the user before the exercise according to the first pressure signal;
    所述获取所述用户运动后的身体状态参数，包括：The acquiring the physical state parameters of the user after exercise includes:
    在所述用户运动后，对所述用户进行体重测量，根据所述用户所施加的压力生成第二压力信号，根据所述第二压力信号确定所述用户在运动后的身体状态参数。After the user exercises, measure the weight of the user, generate a second pressure signal according to the pressure applied by the user, and determine the body state parameter of the user after the exercise according to the second pressure signal.
  4. 根据权利要求1所述的方法，其特征在于，所述方法还包括：The method according to claim 1, wherein the method further comprises:
    获取所述用户运动对应的运动参数；Acquiring the motion parameter corresponding to the user motion;
    根据所述用户运动前的身体状态参数、所述用户运动后的身体状态参数以及所述用户的运动参数计算所述用户的心脏恢复指数及肺恢复指数。The heart recovery index and the lung recovery index of the user are calculated according to the body state parameters of the user before exercise, the body state parameters of the user after exercise, and the exercise parameters of the user.
  5. 根据权利要求4所述的方法，其特征在于，所述根据所述用户运动前的身体状态参数以及所述用户在运动后的身体状态参数计算所述用户运动后的肌肉疲劳度，包括：The method according to claim 4, wherein the calculating the muscle fatigue of the user after exercise according to the physical state parameters of the user before exercise and the physical state parameters of the user after exercise comprises:
    根据所述用户运动前的身体状态参数、所述用户在运动后的身体状态参数以及所述用户的运动参数计算所述用户运动后的肌肉疲劳度。The muscle fatigue degree of the user after exercise is calculated according to the physical state parameters of the user before exercise, the physical state parameters of the user after exercise, and the exercise parameters of the user.
  6. 根据权利要求4或5所述的方法，其特征在于，所述运动参数包括运动类型、运动强度、运动时长、运动后时长及运动参数可信度中的一种或者任意多种。The method according to claim 4 or 5, wherein the exercise parameter includes one or any more of exercise type, exercise intensity, exercise duration, post-exercise duration, and exercise parameter credibility.
  7. 根据权利要求2或3所述的方法，其特征在于，所述根据所述第一压力信号确定所述用户在运动前的身体状态参数，包括：The method according to claim 2 or 3, wherein the determining the physical state parameters of the user before exercise according to the first pressure signal comprises:
    对所述第一压力信号依次进行高通放大处理和低通滤波处理后得到第一信号，将所述第一信号进行高通滤波处理后得到运动前的心冲击信号，根据所述运动前的心冲击信号确定运动前的心率，根据所述运动前的心冲击信号的波形变化确定所述运动前的心冲击信号的波形轮廓，根据所述运动前的心冲击信号的波形轮廓的峰值特征点计算运动前的呼吸频率以及根据所述运动前的心冲击信号的波形轮廓的峰峰值计算运动前的呼吸强度；The first pressure signal is sequentially subjected to high-pass amplification processing and low-pass filtering processing to obtain a first signal, and the first signal is subjected to high-pass filtering processing to obtain a pre-exercise cardiac shock signal, according to the pre-exercise cardiac shock The signal determines the heart rate before exercise, the waveform profile of the cardiac shock signal before exercise is determined according to the waveform change of the cardiac shock signal before exercise, and the exercise is calculated according to the peak feature points of the waveform contour of the cardiac shock signal before exercise The pre-exercise respiration frequency and the pre-exercise respiration intensity calculated according to the peak-to-peak value of the waveform profile of the pre-exercise cardiac shock signal;
    对所述第一信号进行低通滤波处理，得到第二信号，计算出所述第二信号的主频、峰峰值及标准差，根据所述第二信号的主频、峰峰值及标准差计算运动前的身体平稳度。Perform low-pass filtering on the first signal to obtain a second signal, calculate the dominant frequency, peak-to-peak value and standard deviation of the second signal, and calculate based on the dominant frequency, peak-to-peak value and standard deviation of the second signal Body stability before exercise.
  8. 根据权利要求2或3所述的方法，其特征在于，所述根据所述第二压力信号确定所述用户运动后的身体状态参数，包括：The method according to claim 2 or 3, wherein the determining the physical state parameters of the user after exercise according to the second pressure signal comprises:
    对所述第二压力信号依次进行高通放大处理和低通滤波处理后得到第三信号，将所述第三信号进行高通滤波处理后得到运动后的心冲击信号，根据所述运动后的心冲击信号确定运动后的心率，根据所述运动后的心冲击信号的波形变化确定所述运动后的心冲击信号的波形轮廓，根据所述运动后的心冲击信号的波形轮廓的峰值特征点计算运动后的呼吸频率以及根据所述运动后的心冲击信号的波形轮廓的峰峰值计算运动后的呼吸强度；The second pressure signal is sequentially subjected to high-pass amplification processing and low-pass filtering processing to obtain a third signal, and the third signal is subjected to high-pass filtering processing to obtain a post-exercise cardiac shock signal, according to the post-exercise cardiac shock The signal determines the heart rate after exercise, the waveform contour of the post-exercise cardiac shock signal is determined according to the waveform change of the post-exercise cardiac shock signal, and the exercise is calculated according to the peak feature points of the waveform contour of the post-exercise cardiac shock signal The post-exercise respiration frequency and the post-exercise respiration intensity calculated according to the peak-to-peak value of the waveform profile of the post-exercise cardiac shock signal;
    对所述第三信号进行低通滤波处理，得到第四信号，计算出所述第四信号的主频、峰峰值及标准差，根据所述第四信号的主频、峰峰值及标准差计算运动后的身体平稳度。Perform low-pass filtering on the third signal to obtain a fourth signal, calculate the dominant frequency, peak-to-peak value and standard deviation of the fourth signal, and calculate based on the dominant frequency, peak-to-peak value and standard deviation of the fourth signal Body stability after exercise.
  9. 根据权利要求4所述的方法，其特征在于，所述根据所述用户运动前的身体状态参数以及所述用户在运动后的身体状态参数计算所述用户运动后的肌肉疲劳度，包括：The method according to claim 4, wherein the calculating the muscle fatigue of the user after exercise according to the physical state parameters of the user before exercise and the physical state parameters of the user after exercise comprises:
    计算所述用户运动前的身体平稳度与所述用户运动后的身体平稳度之间的身体平稳度差值，以及计算所述用户运动前的心率与所述用户运动后的心率之间的心率差值，以及计算所述用户运动前的呼吸频率与所述用户运动后的呼吸频率之间的呼吸频率差值，以及计算所述用户运动前的呼吸强度与所述用户运动后的呼吸强度之间的呼吸强度差值；Calculate the body stability difference between the body stability of the user before exercise and the body stability of the user after exercise, and calculate the heart rate between the heart rate of the user before exercise and the heart rate of the user after exercise Difference, and calculating the difference between the breathing frequency of the user before exercise and the breathing frequency of the user after exercise, and calculating the difference between the breathing intensity of the user before exercise and the breathing intensity of the user after exercise Difference in respiratory intensity between;
    根据所述身体平稳度差值、所述心率差值、所述呼吸频率差值及呼吸强度差值，采用加权平均算法计算所述用户运动后的肌肉疲劳度。According to the difference in body stability, the difference in heart rate, the difference in respiratory frequency, and the difference in respiratory intensity, a weighted average algorithm is used to calculate the muscle fatigue of the user after exercise.
  10. 根据权利要求9所述的方法，其特征在于，根据所述用户运动前的身体状态参数、所述用户在运动后的身体状态参数以及所述用户的运动参数计算所述用户运动后的心脏恢复指数及肺恢复指数，包括：The method according to claim 9, characterized in that the heart recovery of the user after exercise is calculated according to the physical state parameters of the user before exercise, the physical state parameters of the user after exercise, and the exercise parameters of the user Index and lung recovery index, including:
    根据所述身体平稳度差值、所述心率差值、所述呼吸频率差值及所述呼吸强度差值，并结合所述用户的运动参数，采用加权平均算法计算所述用户运动后的心脏恢复指数及肺恢复指数。According to the body stability difference, the heart rate difference, the respiratory frequency difference, and the respiratory intensity difference, combined with the user's exercise parameters, a weighted average algorithm is used to calculate the user's heart after exercise Recovery index and lung recovery index.
  11. 根据权利要求5所述的方法，其特征在于，所述根据所述用户运动前的身体状态参数、所述用户在运动后的身体状态参数计算以及所述用户的运动参数计算所述用户的肌肉疲劳度、心脏恢复指数及肺恢复指数，包括：The method according to claim 5, wherein the calculation of the user’s muscles is performed based on the user’s physical state parameters before exercise, the user’s physical state parameter calculations after exercise, and the user’s exercise parameters. Fatigue, heart recovery index and lung recovery index, including:
    计算所述用户运动前的身体平稳度与所述用户运动后的身体平稳度之间的身体平稳度差值，以及计算所述用户运动前的心率与所述用户运动后的心率之间的心率差值，以及计算所述用户运动前的呼吸频率与所述用户运动后的呼吸频率之间的呼吸频率差值，以及计算所述用户运动前的呼吸强度与所述用户运动后的呼吸强度之间的呼吸强度差值；Calculate the body stability difference between the body stability of the user before exercise and the body stability of the user after exercise, and calculate the heart rate between the heart rate of the user before exercise and the heart rate of the user after exercise Difference, and calculating the difference between the breathing frequency of the user before exercise and the breathing frequency of the user after exercise, and calculating the difference between the breathing intensity of the user before exercise and the breathing intensity of the user after exercise Difference in respiratory intensity between;
    根据所述身体平稳度差值、所述心率差值、所述呼吸频率差值及所述呼吸强度差值，结合所述用户的运动参数，使用神经网络计算所述用户的肌肉疲劳度、心脏恢复指数及肺恢复指数。According to the body stability difference, the heart rate difference, the breathing rate difference, and the breathing intensity difference, combined with the user's exercise parameters, a neural network is used to calculate the user's muscle fatigue and heart rate. Recovery index and lung recovery index.
  12. 根据权利要求4或5所述的方法，其特征在于，所述方法还包括：The method according to claim 4 or 5, wherein the method further comprises:
    显示计算得到的所述用户的肌肉疲劳度、心脏恢复指数及肺恢复指数中的任意一个或者任意多个，以及根据所述用户的肌肉疲劳度、心脏恢复指数及肺恢复指数中的一个或者任意多个生成并显示运动量评估、身体机能评估、运动建议、身体恢复建议中的一个或者任意多个。Display any one or any of the calculated muscle fatigue, heart recovery index, and lung recovery index of the user, and one or any of the user’s muscle fatigue, heart recovery index, and lung recovery index Multiple generations and displays one or more of exercise volume evaluation, physical function evaluation, exercise suggestion, and physical recovery suggestion.
  13. 一种运动后肌肉疲劳度的检测装置，其特征在于，所述装置包括：A device for detecting muscle fatigue after exercise, characterized in that the device comprises:
    第一获取模块，用于获取用户运动前的身体状态参数以及运动后的身体状态参数，其中，所述身体状态参数包括身体平稳度、心率、呼吸频率及呼吸强度；以及The first acquisition module is used to acquire the body state parameters of the user before exercise and the body state parameters after exercise, wherein the body state parameters include body stability, heart rate, breathing rate, and breathing intensity; and
    第一计算模块，用于根据所述用户运动前的身体状态参数以及所述用户在运动后的身体状态参数计算所述用户运动后的肌肉疲劳度。The first calculation module is configured to calculate the muscle fatigue of the user after exercise according to the physical state parameters of the user before exercise and the physical state parameters of the user after exercise.
  14. 根据权利要求13所述的运动后肌肉疲劳度的检测装置，其特征在于，所述第一获取模块包括：The device for detecting muscle fatigue after exercise according to claim 13, wherein the first acquiring module comprises:
    第一获取单元，用于接收体重测量设备生成的第一压力信号，根据所述第一压力信号确定所述用户在运动前的身体状态参数，其中，所述第一压力信号由体重测量设备在所述用户运动前使用所述体重测量设备进行体重测量时产生；以及The first acquisition unit is configured to receive the first pressure signal generated by the weight measurement device, and determine the physical state parameter of the user before exercise according to the first pressure signal, wherein the first pressure signal is used by the weight measurement device Generated when the user uses the weight measurement device to perform weight measurement before the user exercises; and
    第二获取单元，用于接收所述体重测量设备生成的第二压力信号，根据所述第二压力信号确 定所述用户运动后的身体状态参数，其中，所述第二压力信号由体重测量设备在所述用户运动后使用所述体重测量设备进行体重测量时产生。The second acquisition unit is configured to receive a second pressure signal generated by the weight measurement device, and determine the body state parameter of the user after exercise according to the second pressure signal, wherein the second pressure signal is generated by the weight measurement device It is generated when the user uses the weight measurement device to perform weight measurement after the user exercises.
  15. 根据权利要求13所述的运动后肌肉疲劳度的检测装置，其特征在于，所述第一获取模块包括：The device for detecting muscle fatigue after exercise according to claim 13, wherein the first acquiring module comprises:
    第一确定单元，用于在所述用户运动前，对所述用户进行体重测量，根据所述用户所施加的压力生成第一压力信号，根据所述第一压力信号确定所述用户在运动前的身体状态参数；以及The first determining unit is configured to measure the weight of the user before the user exercises, generate a first pressure signal according to the pressure applied by the user, and determine according to the first pressure signal that the user is before the exercise Physical state parameters; and
    第二确定单元，用于在所述用户运动后，对所述用户进行体重测量，根据所述用户所施加的压力生成第二压力信号，根据所述第二压力信号确定所述用户在运动后的身体状态参数。The second determining unit is configured to measure the weight of the user after the user exercises, generate a second pressure signal according to the pressure applied by the user, and determine the user after the exercise according to the second pressure signal Physical state parameters.
  16. 根据权利要求13所述的运动后肌肉疲劳度的检测装置，其特征在于，所述装置还包括：The device for detecting muscle fatigue after exercise according to claim 13, wherein the device further comprises:
    第二获取模块，用于获取所述用户运动对应的运动参数；以及The second acquisition module is used to acquire the motion parameters corresponding to the user motion; and
    第二计算模块，用于根据所述用户运动前的身体状态参数、所述用户运动后的身体状态参数以及所述用户的运动参数计算所述用户的心脏恢复指数及肺恢复指数。The second calculation module is configured to calculate the heart recovery index and lung recovery index of the user according to the physical state parameters of the user before exercise, the physical state parameters of the user after exercise, and the exercise parameters of the user.
  17. 根据权利要求14所述的运动后肌肉疲劳度的检测装置，其特征在于，所述第一计算模块包括：The device for detecting muscle fatigue after exercise according to claim 14, wherein the first calculation module comprises:
    第一计算单元，用于根据所述用户运动前的身体状态参数、所述用户在运动后的身体状态参数以及所述用户的运动参数计算所述用户运动后的肌肉疲劳度。The first calculation unit is configured to calculate the muscle fatigue of the user after exercise according to the physical state parameters of the user before exercise, the physical state parameters of the user after exercise, and the exercise parameters of the user.
  18. 根据权利要求16或17所述的运动后肌肉疲劳度的检测装置，其特征在于，所述运动参数包括运动类型、运动强度、运动时长、运动后时长及运动参数可信度中的一种或者任意多种。The device for detecting muscle fatigue after exercise according to claim 16 or 17, wherein the exercise parameter includes one of exercise type, exercise intensity, exercise duration, post exercise duration, and exercise parameter credibility, or Any variety.
  19. 根据权利要求14或15所述的运动后肌肉疲劳度的检测装置，其特征在于，所述第一获取模块或者所述第一确定模块包括：The device for detecting muscle fatigue after exercise according to claim 14 or 15, wherein the first acquiring module or the first determining module comprises:
    第一处理单元，用于对所述第一压力信号依次进行高通放大处理和低通滤波处理后得到第一信号，将所述第一信号进行高通滤波处理后得到运动前的心冲击信号，根据所述运动前的心冲击信号确定运动前的心率，根据所述运动前的心冲击信号的波形变化确定所述运动前的心冲击信号的波形轮廓，根据所述运动前的心冲击信号的波形轮廓的峰值特征点计算运动前的呼吸频率以及根据所述运动前的心冲击信号的波形轮廓的峰峰值计算运动前的呼吸强度；以及The first processing unit is configured to sequentially perform high-pass amplification processing and low-pass filtering processing on the first pressure signal to obtain a first signal, perform high-pass filtering processing on the first signal to obtain a pre-exercise cardiac shock signal, according to The cardiac shock signal before exercise determines the heart rate before exercise, the waveform profile of the cardiac shock signal before exercise is determined according to the waveform change of the cardiac shock signal before exercise, and the waveform of the cardiac shock signal before exercise is determined. Calculating the pre-exercise respiration frequency with the peak feature points of the contour and calculating the pre-exercise respiration intensity according to the peak-to-peak value of the waveform contour of the pre-exercise cardiac shock signal; and
    第二处理单元，用于对所述第一信号进行低通滤波处理，得到第二信号，计算出所述第二信号的主频、峰峰值及标准差，根据所述第二信号的主频、峰峰值及标准差计算运动前的身体平稳度。The second processing unit is configured to perform low-pass filter processing on the first signal to obtain a second signal, calculate the dominant frequency, peak-to-peak value, and standard deviation of the second signal, according to the dominant frequency of the second signal , Peak-to-peak value and standard deviation to calculate the body stability before exercise.
  20. 根据权利要求14或15所述的运动后肌肉疲劳度的检测装置，其特征在于，所述第二获取模块或者所述第二确定模块包括：The device for detecting muscle fatigue after exercise according to claim 14 or 15, wherein the second acquiring module or the second determining module comprises:
    第三处理单元，用于对所述第二压力信号依次进行高通放大处理和低通滤波处理后得到第三信号，将所述第三信号进行高通滤波处理后得到运动后的心冲击信号，根据所述运动后的心冲击信号确定运动后的心率，根据所述运动后的心冲击信号的波形变化确定所述运动后的心冲击信号的波形轮廓，根据所述运动后的心冲击信号的波形轮廓的峰值特征点计算运动后的呼吸频率以及根据所述运动后的心冲击信号的波形轮廓的峰峰值计算运动后的呼吸强度；以及The third processing unit is configured to sequentially perform high-pass amplification processing and low-pass filter processing on the second pressure signal to obtain a third signal, perform high-pass filter processing on the third signal to obtain a post-exercise cardiac shock signal, according to The post-exercise cardiac shock signal determines the post-exercise heart rate, the waveform profile of the post-exercise cardiac shock signal is determined according to the waveform change of the post-exercise cardiac shock signal, and the waveform profile of the post-exercise cardiac shock signal is determined according to the waveform of the post-exercise cardiac shock signal The peak feature points of the contour calculate the post-exercise respiration frequency, and the post-exercise respiration intensity is calculated according to the peak-to-peak value of the waveform contour of the post-exercise cardiac shock signal; and
    第四处理单元，用于对所述第三信号进行低通滤波处理，得到第四信号，计算出所述第四信号的主频、峰峰值及标准差，根据所述第四信号的主频、峰峰值及标准差计算运动后的身体平稳度。The fourth processing unit is configured to perform low-pass filtering processing on the third signal to obtain a fourth signal, calculate the main frequency, peak-to-peak value and standard deviation of the fourth signal, and calculate the main frequency of the fourth signal according to the main frequency of the fourth signal. , Peak-to-peak value and standard deviation to calculate the body stability after exercise.
  21. 根据权利要求16所述的运动后肌肉疲劳度的检测装置，其特征在于，所述第一计算模块包括：The device for detecting muscle fatigue after exercise according to claim 16, wherein the first calculation module comprises:
    第二计算单元，用于计算所述用户运动前的身体平稳度与所述用户运动后的身体平稳度之间的身体平稳度差值，以及计算所述用户运动前的心率与所述用户运动后的心率之间的心率差值，以及计算所述用户运动前的呼吸频率与所述用户运动后的呼吸频率之间的呼吸频率差值，以及计算所述用户运动前的呼吸强度与所述用户运动后的呼吸强度之间的呼吸强度差值；以及The second calculation unit is used to calculate the body stability difference between the body stability of the user before exercise and the body stability of the user after exercise, and to calculate the heart rate of the user before exercise and the user exercise The heart rate difference between the heart rates after the exercise, and the respiration rate difference between the respiration rate of the user before exercise and the respiration rate of the user after exercise, and the calculation of the respiration intensity of the user before exercise and the The difference in breathing intensity between the breathing intensity of the user after exercise; and
    第三计算单元，用于根据所述身体平稳度差值、所述心率差值、所述呼吸频率差值及呼吸强度差值，采用加权平均算法计算所述用户运动后的肌肉疲劳度。The third calculation unit is configured to calculate the muscle fatigue degree of the user after exercise by using a weighted average algorithm according to the difference in body stability, the difference in heart rate, the difference in respiratory frequency, and the difference in respiratory intensity.
  22. 根据权利要求21所述的运动后肌肉疲劳度的检测装置，其特征在于，所述第二计算单元包括：22. The device for detecting muscle fatigue after exercise according to claim 21, wherein the second calculation unit comprises:
    第一计算子单元，用于根据所述身体平稳度差值、所述心率差值、所述呼吸频率差值及所述呼吸强度差值，并结合所述用户的运动参数，采用加权平均算法计算所述用户运动后的心脏恢复指数及肺恢复指数。The first calculation subunit is configured to adopt a weighted average algorithm according to the difference in body stability, the difference in heart rate, the difference in respiratory frequency, and the difference in respiratory intensity, in combination with the exercise parameters of the user Calculate the heart recovery index and lung recovery index of the user after exercise.
  23. 根据权利要求17所述的运动后肌肉疲劳度的检测装置，其特征在于，所述第一计算单元包括：The device for detecting muscle fatigue after exercise according to claim 17, wherein the first calculation unit comprises:
    第二计算子单元，用于计算所述用户运动前的身体平稳度与所述用户运动后的身体平稳度之间的身体平稳度差值，以及计算所述用户运动前的心率与所述用户运动后的心率之间的心率差值，以及计算所述用户运动前的呼吸频率与所述用户运动后的呼吸频率之间的呼吸频率差值，以及计算所述用户运动前的呼吸强度与所述用户运动后的呼吸强度之间的呼吸强度差值；以及The second calculation subunit is used to calculate the difference in body stability between the body stability of the user before exercise and the body stability of the user after exercise, and to calculate the difference between the user’s heart rate before exercise and the user’s The heart rate difference between the heart rates after exercise, and the respiration rate difference between the respiration rate of the user before exercise and the respiration rate of the user after exercise, and the calculation of the respiration intensity of the user before exercise and the total The breathing intensity difference between the breathing intensity of the user after exercise; and
    第三计算子单元，用于根据所述身体平稳度差值、所述心率差值、所述呼吸频率差值及所述呼吸强度差值，结合所述用户的运动参数，使用神经网络计算所述用户的肌肉疲劳度、心脏恢复指数及肺恢复指数。The third calculation subunit is used to calculate the difference in body stability, the difference in heart rate, the difference in respiration rate, and the difference in respiration intensity, in combination with the exercise parameters of the user, using a neural network to calculate Describes the user’s muscle fatigue, heart recovery index and lung recovery index.
  24. 根据权利要求15或17所述的运动后肌肉疲劳度的检测装置，其特征在于，所述装置还包括：The device for detecting muscle fatigue after exercise according to claim 15 or 17, wherein the device further comprises:
    第一显示模块，用于显示计算得到的所述用户的肌肉疲劳度、心脏恢复指数及肺恢复指数中的任意一个或者任意多个，以及根据所述用户的肌肉疲劳度、心脏恢复指数及肺恢复指数中的一个或者任意多个生成并显示运动量评估、身体机能评估、运动建议、身体恢复建议中的一个或者任意多个。The first display module is used to display any one or more of the calculated muscle fatigue, heart recovery index, and lung recovery index of the user, and according to the user’s muscle fatigue, heart recovery index, and lung recovery index. One or any more of the recovery index generates and displays one or any more of exercise volume evaluation, physical function evaluation, exercise suggestion, and physical recovery suggestion.
  25. 一种电子设备，其特征在于，所述电子设备包括存储器及处理器，所述存储器中存储有计算机程序，所述处理器与所述存储器连接，所述处理器执行计算机程序以实现如权利要求1～12中任一项所述的运动后肌肉疲劳度的检测方法。An electronic device, characterized in that the electronic device includes a memory and a processor, a computer program is stored in the memory, the processor is connected to the memory, and the processor executes the computer program to achieve The method for detecting muscle fatigue after exercise according to any one of 1 to 12.
  26. 一种体重测量设备，其特征在于，所述体重测量设备包括存储器及处理器，所述存储器中存储有计算机程序，所述处理器与所述存储器连接，所述处理器执行计算机程序以实现如权利要求1～12中任一项所述的运动后肌肉疲劳度的检测方法。A weight measurement device, wherein the weight measurement device includes a memory and a processor, the memory stores a computer program, the processor is connected to the memory, and the processor executes the computer program to implement The method for detecting muscle fatigue after exercise according to any one of claims 1 to 12.
  27. 一种计算机存储介质，其特征在于，包括计算机指令，当所述计算机指令在电子设备上运行时，使得所述电子设备执行如权利要求1～12中任一项所述的运动后肌肉疲劳度的检测方法的步骤。A computer storage medium, characterized by comprising computer instructions, which when the computer instructions run on an electronic device, cause the electronic device to perform the post-exercise muscle fatigue according to any one of claims 1-12 The steps of the detection method.
  28. 一种计算机程序产品，其特征在于，当所述计算机程序产品在计算机上运行时，使得所述计算机执行如权利要求1～12中任一项所述的运动后肌肉疲劳度的检测方法的步骤。A computer program product, characterized in that, when the computer program product runs on a computer, the computer is caused to execute the steps of the method for detecting post-exercise muscle fatigue according to any one of claims 1-12 .
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