CN112807632A

CN112807632A - Parameter optimization method and system of training instrument, muscle training equipment and device

Info

Publication number: CN112807632A
Application number: CN201911128540.7A
Authority: CN
Inventors: 杜建峰; 章鸿; 林建勋; 伍浩; 谢采风
Original assignee: Edan Instruments Inc
Current assignee: Edan Instruments Inc
Priority date: 2019-11-18
Filing date: 2019-11-18
Publication date: 2021-05-18

Abstract

The application relates to the technical field of muscle training, and particularly discloses a parameter optimization method, a parameter optimization system, muscle training equipment and a parameter optimization device of a training instrument, wherein the method comprises the following steps: collecting a plurality of self-evaluation information of a trainer; collecting muscle information of a trainer during a first training scheme; training parameters of the first training regimen are adjusted based on the plurality of self-assessment information and the muscle information to generate a second training regimen. Through the mode, the second targeted training scheme can be generated according to the training situation, and the training effect is improved.

Description

Parameter optimization method and system of training instrument, muscle training equipment and device

Technical Field

The application relates to the technical field of muscle training, in particular to a parameter optimization method and system of a training instrument, muscle training equipment and a device.

Background

At present, the influence of muscle mass on health is gradually studied deeply, the muscle state of the same person can be in different levels at different times, and the best training effect cannot be achieved necessarily by using the same training scheme.

Disclosure of Invention

Based on the above, the application provides a parameter optimization method, a parameter optimization system, muscle training equipment and a parameter optimization device for a training instrument, which can generate a second targeted training scheme according to a training situation, so that a training effect is improved.

In one aspect, the present application provides a method for optimizing parameters of a training apparatus, the method comprising the steps of: collecting a plurality of self-evaluation information of a trainer; collecting muscle information of a trainer during a first training scheme; training parameters of the first training regimen are adjusted based on the plurality of self-assessment information and the muscle information to generate a second training regimen.

In another aspect, the present application provides a muscle training system comprising a training apparatus; the training instrument comprises a human-computer interaction interface, an electrode device and a processor, wherein the processor is connected with the human-computer interaction interface and the electrode device; the human-computer interaction interface is used for collecting a plurality of self-evaluation information of the trainer; the electrode device is used for collecting muscle information of a trainer during a first training scheme; the processor is configured to receive the plurality of self-assessment information and the muscle information and adjust training parameters of the first training regimen based on the plurality of self-assessment information and the muscle information to generate a second training regimen.

In yet another aspect, the present application provides a muscle training apparatus comprising: the training instrument parameter optimization method comprises a first acquisition unit, a second acquisition unit and a processing unit, wherein the processing unit is connected with the first acquisition unit and the second acquisition unit, and the first acquisition unit, the second acquisition unit and the processing unit are matched to realize the steps of the parameter optimization method of the training instrument.

In a further aspect, the present application provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method for parameter optimization of a training apparatus as described above.

The beneficial effect of this application is: different from the prior art, the comprehensive assessment information is obtained by collecting a plurality of self-assessment information of the trainer and obtaining muscle information of the trainer during the last training scheme, so that the current training situation of the trainer is obtained, the training method is timely adjusted according to the training situation, a second targeted training scheme is generated, and the training effect is improved.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts. Wherein:

FIG. 1 is a schematic flow chart diagram illustrating an embodiment of a method for optimizing parameters of a training machine according to the present application;

FIG. 2 is a schematic flow chart of step S10 in FIG. 1;

FIG. 3 is a schematic flow chart of step S20 in FIG. 1;

FIG. 4 is a schematic flow chart diagram illustrating another embodiment of a method for optimizing parameters of a training machine according to the present application;

FIG. 5 is a schematic flow chart of step S30 in FIG. 1;

FIG. 6 is a schematic flow chart diagram illustrating a method for optimizing parameters of a training machine according to another embodiment of the present application;

FIG. 7 is a schematic diagram of the structure of an embodiment of the muscle training system of the present application;

FIG. 8 is a schematic diagram of the structure of an embodiment of the muscle training apparatus of the present application;

FIG. 9 is a schematic structural diagram of an embodiment of a computer-readable storage medium of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

Referring to fig. 1, fig. 1 is a schematic flow chart of an embodiment of a parameter optimization method of a training machine of the present application, the method including the following steps:

s10: a plurality of self-assessment information of the trainer is collected.

Specifically, a plurality of self-assessment information of the trainer may be acquired by the physiological information detection device, such as: the self-evaluation information can be at least one of body temperature, electrocardio, heart rate, blood oxygen, blood pressure, blood sugar, pulse, respiration, body fat rate, body mass index, segment muscle or body weight.

Or a questionnaire is provided through a human-computer interaction interface, and a plurality of self-evaluation information is filled in and submitted by the trainer. The trainee can fill in the questionnaire for many times, and the latest evaluation data is the effective record of the self-evaluation information. The self-assessment information may further include at least one of training patterns, total time of training, number of times per week of training, time per training, intensity range at the time of training.

S20: muscle information of a trainer during a first training regimen is collected.

Specifically, the first training scheme is a current training scheme of the trainer, and the training scheme is composed of a plurality of training parameters, for example: at least one of training times, training duration, training actions, electrical stimulation current signals and air pressure of the air bag, wherein the training actions can be as follows: can be used in standing position, sitting position and lying position. The anus is contracted first, and then the urethra is contracted, so that the sense of lifting the levator ani muscle is generated. The thigh and abdominal muscles remain relaxed during the anus, urethra and contraction. Continuously contracting and extracting the anus for not less than 3 seconds, relaxing and resting for 2-6 seconds, and continuously doing for 15-30 minutes.

The myoelectric signal or muscle tension value of the muscle when the trainer performs the first training scheme can be obtained by the corresponding electrode device.

The electrode device comprises a carrier, and an electrode plate and a muscle tension sensor which are arranged on the outer wall of the carrier, wherein the carrier is an elastic air bag. Every two electrode plates form a corresponding myoelectric channel, every two electrode plates can be connected with the 1-path differential signal acquisition circuit through an analog switch circuit, then myoelectric signals of muscles are acquired through the differential signal acquisition circuit, and myotension images of the muscles in a normal state are acquired through the myotension sensor.

Furthermore, when the muscle to be trained is in a force application state, the electromyographic signal can be acquired based on a higher acquisition frequency, and the more data acquired in unit time is, so that in the process of analyzing the electromyographic signal by a subsequent processor, the more accurate evaluation can be made on the training effect of the muscle to be trained based on the electromyographic signal with higher data acquisition precision in the force application state. When the muscle to be trained is in a relaxed state, the electromyographic signal can be acquired based on a lower acquisition frequency, and the smaller the data volume acquired in unit time. The myoelectric signals acquired in the relaxed state have low evaluation effect on the training effect of the muscles to be trained, so the myoelectric signals with less data volume can improve the analysis efficiency of the processor.

S30: training parameters of the first training regimen are adjusted based on the plurality of self-assessment information and the muscle information to generate a second training regimen.

Specifically, an analysis result and a corresponding second training scheme are generated according to the self-evaluation information and the muscle information, and the analysis result and the second training scheme are sent to a human-computer interaction interface or a remote terminal for displaying. When the second training scheme generated by the muscle training system is not suitable for the trainer, the background workstation can adaptively correct the second training scheme by checking the second training scheme or re-input the generation parameters of the second training scheme, so that the muscle training system generates a new second training scheme.

Different from the prior art, the comprehensive assessment information is obtained by collecting a plurality of self-assessment information of the trainer and obtaining muscle information of the trainer during the last training scheme, so that the current training situation of the trainer is obtained, the training method is timely adjusted according to the training situation, a second targeted training scheme is generated, and the training effect is improved.

Referring to fig. 2, fig. 2 is a schematic flowchart of step S10 in fig. 1, and in an embodiment, step S10 includes:

s11: personal information of the trainer is acquired.

Specifically, the personal information may be information entered when the trainer performs initial training, wherein the personal information of the trainer includes at least one of age, gender, family genetic history, marital condition, fertility condition, and past medical history.

The acquisition method can be that reading the identification card carried by the trainer and acquiring the personal information of the trainer prestored in the identification card; or the personal information of the trainer is formed into a two-dimensional code image by utilizing two-dimensional code coding software, and the two-dimensional code image is acquired by using a scanner or a camera so as to acquire the personal information of the trainer; or the trainer inputs personal information on a man-machine interaction interface.

S12: a self-assessment questionnaire is generated based on the personal information of the trainer.

Specifically, the content of the self-assessment questionnaire may be:

1. the following questions, numbers 1 to 7 represent a scale from "very bad" to "very good", please circle the most appropriate answer to you between 1 and 7.

How do you assess your general health in the past week?

How do you assess leakage during the past week?

2. The following question, please select the answer of "yes" or "no".

Is you hard when doing the last exercise of the training program?

Is you painful when you take the action of the last training regimen?

3. The following question is the choice of the corresponding item.

The training purpose is as follows: A. b, losing weight, increasing muscle and C, shaping;

days of training desired weekly: day A.3, day B.5, day C.7

S13: and acquiring the self-evaluation questionnaire filled by the trainer to acquire a plurality of self-evaluation information in the self-evaluation questionnaire.

Specifically, the push human-computer interface is filled in by the trainer, for example, a human-computer interface for the trainer to input text is provided, for example, a chat window and the like are provided, and the trainer can input answer information of a questionnaire in a text form through the chat window; or a plurality of virtual keys are arranged on the human-computer interaction interface: the trainer can input the answer information of the questionnaire through the virtual keys by selecting a question key, a question switching key, a page turning key, a question starting key, an answer ending and answering hand-off key and a keyboard for inputting characters and numbers.

In one embodiment, the muscles are pelvic floor muscles, and the muscles to be trained are pelvic floor muscle groups, which may include levator ani, sphincter ani, obturator muscle, sphincter urethras, sphincters, and the like.

Referring to fig. 3, fig. 3 is a schematic flowchart of step S20 in fig. 1, and step S20 includes:

s21: the myoelectric signals and pressure signals of the pelvic floor muscles are collected by the corresponding electrode devices.

Specifically, the electrode device comprises a carrier and electrode plates arranged on the outer wall of the carrier, wherein the electrode plates are made of conductive materials, the conductive materials are attached to the outer surface of the carrier to form a plurality of electrode plates through printing, laser three-dimensional circuit direct forming, pasting, surface treatment or 3D printing methods, and every two electrode plates form a corresponding myoelectric channel. When a trainer carries out a first training scheme, the electromyographic signals are collected through the electrode plates.

The electrode device also comprises a pressure sensor arranged on the outer wall of the carrier, and the pressure sensor acquires pressure signals of muscles so as to obtain a muscle tension image.

Further, when the muscle to be trained is in a force state, the myoelectric signal can be acquired based on a higher acquisition frequency, the more data volume acquired in unit time is, and therefore, in the process of analyzing the myoelectric signal by a subsequent processor, the myoelectric signal with higher data acquisition precision is acquired based on the force state. When the muscle to be trained is in a relaxed state, the electromyographic signals can be acquired based on a lower acquisition frequency, so that the amount of data acquired in unit time is smaller, and the electromyographic signals with smaller data amount can improve the analysis efficiency of the processor.

S22: the electromyographic signal and the pressure signal are analyzed to generate muscle information.

The muscle information comprises at least one of a real-time myoelectric value, a real-time myoelectric tension value, a real-time myoelectric signal continuous active time or a real-time muscle pressure value.

Specifically, after a muscle tension sensor collects a muscle tension image of a muscle, a real-time muscle tension value or a real-time muscle pressure value is obtained by extracting the amplitude and frequency distribution condition of the muscle tension in the muscle tension image.

After the electromyographic signals are collected, the electromyographic signals collected by the differential signal collecting circuit can be filtered through a low-pass filter, high-frequency noise can be filtered, low-frequency signals can be filtered through an EMI filter, electromagnetic interference signals can be filtered, and the filtered electromyographic signals are obtained. And drawing a frequency domain-based frequency spectrum graph and a time domain-based potential graph of the filtered electromyographic signals. And extracting a real-time myoelectric value or a real-time myoelectric signal continuous active time from the frequency spectrogram and the potential map.

Referring to fig. 4, fig. 4 is a schematic flow chart of another embodiment of the parameter optimization method of the training apparatus of the present application, and in one embodiment, before step S30, the method further includes the following steps:

s40: and constructing a muscle training scheme library.

Wherein, the muscle training scheme library is a training scheme formulated or modified by the background workstation. The muscle training scheme library stores a plurality of muscle training schemes, wherein the muscle training schemes in the training database comprise combinations of a plurality of standard training actions and quality requirements of the standard training actions, and the muscle training schemes are formed by combining a plurality of training parameters. The training parameters include at least one of muscle contraction mode, training group number, activity range, training speed and inter-group interval rest time, for example, activity degree of each muscle, training time, limitation of posture and angle of contraction action, angle and holding time of relaxation action, supporting surface and holding time of balance action, and the like.

S50: and setting a muscle training scheme generation rule.

Wherein the muscle training scenario generation rule is used to specify parameter values of the training parameters based on the plurality of self-assessment information and the muscle information.

Specifically, based on the plurality of self-assessment information and the muscle information, the muscle training regimen generation rule specifies parameter values of training parameters of the training regimen, such as at least one of a muscle contraction pattern, a number of training groups, a number of times per group, an activity range, a training speed, and an inter-group interval rest time.

Referring to fig. 5, fig. 5 is a schematic flowchart of step S30 in fig. 1, and in an embodiment, step S30 includes:

s31: and scoring the self-assessment information and the muscle information according to a preset quantitative scoring standard to generate a scoring result.

Specifically, 1. the following questions, numbers 1 to 7 represent a scale from "very bad" to "very good", please circle the most appropriate answer to you between 1 and 7.

Such as: the question "how do you assess your general health in the past week? ", the number 1 represents" poor ", scoring 0, the number 7 represents" good ", scoring 5; the question "how do you assess leakage during the past week? "leak urine more than 7 times in the past week, record 0 point, leak urine less than 3 times in the past week, record 5 points; and recording the score of 0 when the real-time muscle tension value is greater than or equal to the pre-stored muscle tension value, and recording the score of 5 when the real-time muscle tension value is less than the preset muscle tension value, wherein the preset muscle tension value. The scoring result is the sum of scoring values of the plurality of self-assessment information and muscle information.

S32: and according to the grading result and the muscle training scheme generation rule, the parameter value of the training parameter is specified.

In particular, the muscle training regimen generation rule further indicates a training recommendation from which the trainer may select a training according to the training category. The muscle training scenario generation rule may be a rule defined based on scientific training principles such that it ensures that the assignment of training scenarios is correct, e.g. setting at least one break day between training days of the same training category, or running and upper body training on monday, cycling and lower body training on wednesday, etc. Further, after obtaining at least one of the time when the trainer wants to train, the duration the trainer wants to train at the training regimen, and the duration of the training regimen, the above will be used in constructing the personalized training regimen, e.g. which times are preferred for each training regimen to ensure that a personalized training regimen is constructed that is optimized for the trainer and according to the principles of scientific training.

S33: and selecting a second training scheme from the muscle training scheme library according to the parameter values of the training parameters.

For example, the action of the muscle training regimen selected according to the parameter values of the training parameters is as follows: can be used in standing position, sitting position and lying position. The anus is contracted firstly, and then the urethra is contracted, so that the sense of lifting the anus and the muscle is generated. The thigh and abdominal muscles remain relaxed during the anus, urethra and contraction. Continuously contracting and extracting the anus for not less than 3 seconds, relaxing and resting for 2-6 seconds, and continuously doing for 15-30 minutes.

Referring to fig. 6, fig. 6 is a schematic flow chart of a further embodiment of the parameter optimization method of the training apparatus of the present application, in an embodiment, after step S30, the method further includes:

s60: and pushing the second training scheme and personal information of the trainer to the background workstation.

Specifically, after the communication relationship with the background workstation is established, the second training scheme and the personal information of the trainer are pushed to the background workstation, so that the background workstation can inquire and store the second training scheme and the personal information of the trainer, and further, the effectiveness of the second training scheme is evaluated.

S70: and receiving confirmation information of the background workstation, and updating the second training scheme to the training instrument so that the training instrument executes the second training scheme.

Alternatively, the flow proceeds to step S80: and receiving the modification information of the background workstation and the modified second training scheme, and updating the modified second training scheme to the training instrument so that the training instrument executes the modified second training scheme.

Referring to fig. 7, fig. 7 is a schematic structural diagram of an embodiment of the muscle training system 100 of the present application, which includes a training apparatus 101.

The training apparatus 101 comprises a human-computer interaction interface 111, an electrode device 112 and a processor 113, wherein the processor 113 is connected with the human-computer interaction interface 111 and the electrode device 112. The electrode assembly 112 includes a carrier and an electrode sheet disposed on an outer wall of the carrier. A plurality of myoelectric channels are attached to the outer surface of the carrier of the electrode device 112, the number of the electrode plates is twice of the number of the myoelectric channels, and each two electrode plates form a corresponding myoelectric channel. The electrode plates are made of conductive materials, and the conductive materials are attached to the outer surface of the carrier to form a plurality of electrode plates by printing, laser three-dimensional circuit direct forming, pasting, surface treatment or 3D printing, and each two electrode plates form a corresponding myoelectricity channel.

The human-computer interface 111 is used for collecting a plurality of self-evaluation information of the trainer.

The electrode arrangement 112 is used to collect muscle information of the trainee when performing the first training protocol. When a trainer carries out a first training scheme, the electromyographic signals are collected through the electrode plates. The electrode device also comprises a pressure sensor arranged on the outer wall of the carrier, and the pressure sensor acquires pressure signals of muscles so as to obtain a muscle tension image.

The processor 113 is configured to receive the plurality of self-assessment information and the muscle information and adjust the training parameters of the first training regimen based on the plurality of self-assessment information and the muscle information to generate a second training regimen.

The human-computer interaction interface 111 is used for acquiring personal information of a trainer;

the processor 113 is configured to receive personal information of the trainer and generate a self-evaluation questionnaire according to the personal information of the trainer;

the human-computer interface 111 is configured to output a self-evaluation questionnaire, and obtain the self-evaluation questionnaire filled by the trainer to obtain a plurality of self-evaluation information in the self-evaluation questionnaire.

The electrode device 112 is used for collecting myoelectric signals and pressure signals of pelvic floor muscles;

the processor 113 is configured to analyze the electromyographic signal and the pressure signal to generate muscle information, wherein the muscle information includes at least one of a real-time electromyographic value, a real-time myotonic value, a real-time electromyographic signal continuous active time, or a real-time muscle pressure value.

The processor 113 is configured to construct a muscle training scheme library, where the muscle training scheme library stores a plurality of muscle training schemes, and the muscle training schemes are formed by combining a plurality of training parameters;

the processor 113 is further configured to set a muscle training scenario generation rule, where the muscle training scenario generation rule is configured to specify a parameter value of a training parameter according to the plurality of self-assessment information and the muscle information;

wherein the training parameters comprise at least one of muscle contraction mode, training group number, times of each group, activity range, training speed and interval rest time between groups.

The processor 113 is configured to score the plurality of self-assessment information and muscle information according to a preset quantitative scoring standard to generate a scoring result; according to the scoring result and the muscle training scheme, generating a parameter value of a rule designated training parameter; and selecting a second training scheme from the muscle training scheme library according to the parameter values of the training parameters.

The system 100 further comprises: the communication device 102 is connected with the background workstation 103 and the training instrument 101, so that data transmission between the background workstation 103 and the training instrument 101 is realized;

the trainer 101 pushes the second training scheme and the personal information of the trainer to the background workstation 103 through the communication equipment 102;

the training instrument 101 receives the confirmation information of the background workstation 103 through the communication device 102, and the training instrument 101 is used for executing a second training scheme; alternatively, the first and second electrodes may be,

the training apparatus 101 receives the modification information of the background workstation 103 and the modified second training scenario via the communication device 102, and the training apparatus 101 is configured to execute the modified second training scenario.

It should be noted that the system of the present embodiment can execute the steps in the method, and for a detailed description of related contents, refer to the method section, which is not described herein again.

Referring to fig. 8, fig. 8 is a schematic structural diagram of an embodiment of the muscle training apparatus 200 of the present application, including: the device comprises a first acquisition unit 201, a second acquisition unit 202, a processing unit 203 and a communication unit 204, wherein the processing unit 203 is connected with the first acquisition unit 201, the second acquisition unit 202 and the communication unit 204.

The first collecting unit 201 is used for collecting a plurality of self-evaluation information of the trainer.

The second acquisition unit 202 is used for acquiring muscle information of the trainer during the first training scheme.

The processing unit 203 is configured to adjust the training parameters of the first training regimen according to the plurality of self-assessment information and the muscle information to generate a second training regimen.

The second collecting unit 201 is an electrode device, the electrode device includes a carrier and electrode plates arranged on the outer wall of the carrier, the electrode plates are made of conductive materials, the conductive materials are attached to the outer surface of the carrier to form a plurality of electrode plates by printing, direct forming of a laser three-dimensional circuit, sticking, surface treatment or 3D printing, and each two electrode plates form a corresponding myoelectric channel. When a trainer carries out a first training scheme, the electromyographic signals are collected through the electrode plates. The electrode device also comprises a pressure sensor arranged on the outer wall of the carrier, and the pressure sensor acquires pressure signals of muscles so as to obtain a muscle tension image.

The first acquisition unit 201 is used for acquiring personal information of a trainer;

the first acquisition unit 201 is used for generating a self-evaluation questionnaire according to personal information of a trainer;

the first acquisition unit 201 is configured to acquire a self-evaluation questionnaire filled by a trainer to acquire a plurality of self-evaluation information in the self-evaluation questionnaire.

The second collecting unit 202 is used for collecting myoelectric signals and pressure signals of pelvic floor muscles;

the processing unit 203 is configured to analyze the electromyographic signal and the pressure signal to generate muscle information, where the muscle information includes at least one of a real-time electromyographic value, a real-time myotonic value, a real-time continuous active time of the electromyographic signal, or a real-time muscle pressure value.

The processing unit 203 is configured to construct a muscle training scheme library, where the muscle training scheme library stores a plurality of muscle training schemes, and the muscle training schemes are formed by combining a plurality of training parameters;

the processing unit 203 is configured to set a muscle training scheme generation rule, where the muscle training scheme generation rule is configured to specify a parameter value of a training parameter according to the plurality of self-evaluation information and the muscle information. Wherein the training parameters comprise at least one of muscle contraction mode, training group number, times of each group, activity range, training speed and interval rest time between groups.

The processing unit 203 is configured to score the plurality of self-assessment information and muscle information according to a preset quantitative scoring standard to generate a scoring result; according to the scoring result and the muscle training scheme, generating a parameter value of a rule designated training parameter; and selecting a second training scheme from the muscle training scheme library according to the parameter values of the training parameters.

The communication unit 204 is configured to push the second training scenario and the personal information of the trainer to the background workstation;

the processing unit 203 is configured to receive the confirmation information of the background workstation, and update the second training scheme to the training instrument, so that the training instrument executes the second training scheme; alternatively, the processing unit 203 receives the modification information of the background workstation and the modified second training scheme and updates the modified second training scheme to the training apparatus, so that the training apparatus executes the modified second training scheme.

It should be noted that the muscle training apparatus of the present embodiment can perform the steps of the method, and the detailed description of the related contents refers to the above method section, which is not repeated herein.

Referring to fig. 9, fig. 9 is a schematic structural diagram of an embodiment of a computer-readable storage medium 300 according to the present application, in which a computer program 301 is stored on the computer-readable storage medium 300, and the computer program 301 implements the following steps when being executed by a processor: collecting a plurality of self-evaluation information of a trainer and collecting muscle information of the trainer during a first training scheme; training parameters of the first training regimen are adjusted based on the plurality of self-assessment information and the muscle information to generate a second training regimen.

In one embodiment, the step of collecting a plurality of self-assessment information of the trainer, which is implemented when the computer program 301 is executed by the processor, may include: acquiring personal information of a trainer; generating a self-evaluation questionnaire according to personal information of a trainer; and acquiring the self-evaluation questionnaire filled by the trainer to acquire a plurality of self-evaluation information in the self-evaluation questionnaire.

In one embodiment, where the muscles are pelvic floor muscles, the step of acquiring muscle information of the trainer when the computer program 301 is executed by the processor may comprise: collecting myoelectric signals and pressure signals of pelvic floor muscles by corresponding electrode devices; analyzing the myoelectric signal and the pressure signal to generate muscle information, wherein the muscle information comprises at least one of a real-time myoelectric value, a real-time myotension value, a real-time myoelectric signal continuous active time or a real-time muscle pressure value.

In an embodiment, the step of adjusting the training parameters of the first training regimen based on the plurality of self-assessment information and the muscle information to generate the second training regimen, as performed by the processor, further comprises the step of: constructing a muscle training scheme library, wherein a plurality of muscle training schemes are stored in the muscle training scheme library, and the muscle training schemes are formed by combining a plurality of training parameters; setting a muscle training scheme generation rule, wherein the muscle training scheme generation rule is used for specifying parameter values of training parameters according to the plurality of self-evaluation information and the muscle information.

In one embodiment, the step of adjusting the training parameters of the first training regimen based on the plurality of self-assessment information and the muscle information to generate the second training regimen, as performed by the processor of the computer program 301, comprises: scoring the self-assessment information and the muscle information according to a preset quantitative scoring standard to generate a scoring result; according to the scoring result and the muscle training scheme, generating a parameter value of a rule designated training parameter; and selecting a second training scheme from the muscle training scheme library according to the parameter values of the training parameters.

In an embodiment, the step of generating the second training regimen from the plurality of self-assessment information and muscle information, when performed by the processor, further comprises: pushing the second training scheme and the personal information of the trainer to a background workstation; receiving confirmation information of the background workstation, and updating the second training scheme to the training instrument so that the training instrument executes the second training scheme; or receiving the modification information of the background workstation and the modified second training scheme, and updating the modified second training scheme to the training instrument so that the training instrument executes the modified second training scheme.

It should be noted that the apparatus of the present embodiment can perform the steps of the method, and the detailed description of the related contents refers to the above method section, which is not repeated herein.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by the computer program 301 instructing the relevant hardware to complete, and the computer program 301 can be stored in a non-volatile computer readable storage medium, and when executed, the computer program 301 can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

The above embodiments are merely examples, and not intended to limit the scope of the present application, and all modifications, equivalents, and flow charts using the contents of the specification and drawings of the present application, or those directly or indirectly applied to other related arts, are included in the scope of the present application.

Claims

1. A method for optimizing parameters of a training apparatus, the method comprising the steps of:

collecting a plurality of self-evaluation information of a trainer;

collecting muscle information of the trainer during a first training scheme;

adjusting training parameters of the first training regimen based on a plurality of the self-assessment information and the muscle information to generate a second training regimen.

2. The method of claim 1, wherein the step of collecting a plurality of self-assessment information of the trainer comprises:

acquiring personal information of the trainer;

generating a self-evaluation questionnaire according to the personal information of the trainer;

and acquiring the self-evaluation questionnaire filled by the trainer so as to acquire a plurality of self-evaluation information in the self-evaluation questionnaire.

3. The method of claim 1, wherein the step of collecting muscle information of the trainer while performing the first training regimen comprises:

acquiring myoelectric signals and pressure signals of the pelvic floor muscles by corresponding electrode devices;

analyzing the myoelectric signal and the pressure signal to generate the muscle information, wherein the muscle information comprises at least one of a real-time myoelectric value, a real-time myotension value, a real-time myoelectric signal continuous active time or a real-time muscle pressure value.

4. The method of claim 1, wherein prior to the step of adjusting the training parameters of the first training regimen to generate a second training regimen based on the plurality of self-assessment information and the muscle information, the method further comprises the steps of:

constructing a muscle training scheme library, wherein a plurality of muscle training schemes are stored in the muscle training scheme library, and the muscle training schemes are formed by combining a plurality of training parameters;

setting a muscle training scenario generation rule for specifying a parameter value of the training parameter from a plurality of the self-assessment information and the muscle information.

5. The method of claim 4, wherein the training parameters include at least one of muscle contraction patterns, number of training sets, number of times per set, range of motion, training speed, and inter-set interval rest time.

6. The method of claim 4, wherein the step of adjusting the training parameters of the first training regimen based on the plurality of self-assessment information and the muscle information to generate a second training regimen comprises:

scoring the self-assessment information and the muscle information according to a preset quantitative scoring standard to generate a scoring result;

appointing parameter values of the training parameters according to the grading result and the muscle training scheme generation rule;

and selecting a second training scheme from the muscle training scheme library according to the parameter value of the training parameter.

7. The method of claim 1, wherein after the step of generating a second training regimen from the plurality of self-assessment information and the muscle information, the method further comprises:

pushing the second training scheme and the personal information of the trainer to a background workstation;

receiving confirmation information of the background workstation, and updating the second training scheme to the training instrument so that the training instrument executes the second training scheme; alternatively, the first and second electrodes may be,

and receiving modification information of the background workstation and the modified second training scheme, and updating the modified second training scheme to the training instrument so that the training instrument executes the modified second training scheme.

8. A muscle training system, wherein the system comprises a training apparatus;

the training instrument comprises a human-computer interaction interface, an electrode device and a processor, wherein the processor is connected with the human-computer interaction interface and the electrode device;

the human-computer interaction interface is used for collecting a plurality of self-evaluation information of a trainer;

the electrode device is used for collecting muscle information of the trainer during a first training scheme;

the processor is configured to receive a plurality of the self-assessment information and the muscle information, and adjust a training parameter of the first training regimen according to the plurality of the self-assessment information and the muscle information to generate a second training regimen.

9. The system of claim 8, wherein the human-computer interface is configured to obtain personal information of the trainer;

the processor is used for receiving the personal information of the trainer and generating a self-evaluation questionnaire according to the personal information of the trainer;

the human-computer interaction interface is used for outputting the self-evaluation questionnaire and acquiring the self-evaluation questionnaire filled by the trainer so as to acquire a plurality of self-evaluation information in the self-evaluation questionnaire.

10. The system of claim 8,

the electrode device is used for collecting myoelectric signals and pressure signals of pelvic floor muscles;

the processor is configured to analyze the electromyographic signal and the pressure signal to generate the muscle information, wherein the muscle information includes at least one of a real-time myoelectric value, a real-time myotension value, a real-time electromyographic signal continuous active time, or a real-time muscle pressure value.

11. The system of claim 8,

the processor is used for constructing a muscle training scheme library, wherein a plurality of muscle training schemes are stored in the muscle training scheme library, and the muscle training schemes are formed by combining a plurality of training parameters;

the processor is further configured to set a muscle training regimen generation rule, wherein the muscle training regimen generation rule is configured to specify a parameter value of the training parameter according to a plurality of the self-assessment information and the muscle information;

wherein the training parameters comprise at least one of muscle contraction pattern, number of training groups, number of times per group, range of motion, training speed and inter-group interval rest time.

12. The system of claim 11,

the processor is used for scoring the self-assessment information and the muscle information according to a preset quantitative scoring standard so as to generate a scoring result; appointing parameter values of the training parameters according to the grading result and the muscle training scheme generation rule; and selecting a second training scheme from the muscle training scheme library according to the parameter value of the training parameter.

13. The system of claim 8, further comprising: the communication equipment is connected with the background workstation and the training instrument so as to realize data transmission between the background workstation and the training instrument;

the trainer pushes the second training scheme and the personal information of the trainer to the background workstation through the communication equipment;

the training instrument receives confirmation information of the background workstation through the communication equipment, and the training instrument is used for executing the second training scheme; alternatively, the first and second electrodes may be,

and the training instrument receives the modified information of the background workstation and the modified second training scheme through the communication equipment, and is used for executing the modified second training scheme.

14. A muscle training device, characterized in that it comprises: a first acquisition unit, a second acquisition unit and a processing unit, wherein the processing unit is connected with the first acquisition unit and the second acquisition unit, and the first acquisition unit, the second acquisition unit and the processing unit are matched to realize the steps of the parameter optimization method of the training instrument according to any one of claims 1 to 7.

15. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method for optimizing parameters of a training apparatus according to any one of claims 1 to 7.