CN116859479A - Intelligent detection method and device for wearing state of massage equipment - Google Patents
Intelligent detection method and device for wearing state of massage equipment Download PDFInfo
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Abstract
The application provides an intelligent detection method and device for wearing state of massage equipment, wherein the method comprises the following steps: collecting piezoelectric signals of a piezoelectric sensor on the massage equipment according to the detection period; generating an autocorrelation curve using the piezoelectric signal; extracting autocorrelation characteristics of the autocorrelation curve; detecting a motion state of the massage device according to the autocorrelation characteristics, wherein the motion state comprises: the method solves the problems that signals acquired by a sensor arranged on the massage equipment are easily influenced by the wearing tightness of the equipment, the threshold value needs to be adjusted, the suitability is poor and the like, improves the robustness of a detection algorithm of the wearing state of the equipment, and improves the use suitability of the massage equipment.
Description
Technical Field
The application relates to the technical field of massagers, in particular to an intelligent detection method and device for the wearing state of massage equipment.
Background
In the related art, accurately judging the state of the equipment is a precondition for automatic control of the equipment. The schemes for judging the state of the device are an optical sensor, a skin capacitance sensor, a gyroscope, an accelerometer and the like. In the case of an optical sensor, an opening is required in the housing. In the case of eye/head massagers, since the face of the massager contacting the skin is typically made of cloth or leather, in such a scenario, the openings may result in poor dust-proof performance of the device and poor wearing comfort. In addition, the cost of the optical sensor is also slightly higher than that of the piezoelectric sensor. In the scheme of the gyroscope and the accelerometer, the sensor senses the motion state of a person through acceleration, and although the state of the equipment can be judged, physiological signals of the person wearing the gyroscope and the accelerometer cannot be further acquired for deeper analysis.
In the related art, signals collected by a sensor placed on massage equipment are easy to be influenced by wearing tightness of the equipment, the threshold value is required to be adjusted under the condition of wearing different tightness of different people or the same person, and the suitability is poor.
In view of the above problems in the related art, an efficient and accurate solution has not been found.
Disclosure of Invention
The application provides an intelligent detection method and device for the wearing state of massage equipment, which are used for solving the technical problems in the related art.
According to an embodiment of the present application, there is provided an intelligent detection method for a wearing state of a massage apparatus, including: collecting piezoelectric signals of a piezoelectric sensor on the massage equipment according to the detection period; generating an autocorrelation curve using the piezoelectric signal; extracting autocorrelation characteristics of the autocorrelation curve; detecting a motion state of the massage device according to the autocorrelation characteristics, wherein the motion state comprises: a placing state, a picking state and a wearing state.
Optionally, extracting the autocorrelation characteristic of the autocorrelation curve includes: counting the number of peaks of the autocorrelation curve, calculating the peak value of each peak, calculating the peak width of each peak, and calculating the peak relative height of each peak; the number of peaks, the peak value, the peak width, and the peak relative height are determined as auto-correlation features of the piezoelectric signal.
Optionally, detecting the motion state of the massage device according to the autocorrelation feature includes: judging whether the number of wave peaks is within a preset range, wherein the autocorrelation characteristic comprises: the number of peaks, the size of the peaks; if the wave crest number is in a preset range, determining that the motion state of the massage equipment is a placement state; and if the number of the wave peaks is not in the preset range, detecting the motion state of the massage equipment according to the wave peak size.
Optionally, detecting the motion state of the massage device according to the peak size includes: determining the maximum peak value of all peak values, calculating a first average value of all peak widths, and calculating a second average value of all peak relative heights, wherein the peak sizes comprise: peak value, peak width, and peak relative height; judging whether the maximum peak value is larger than a first threshold value or not; if the maximum peak value is greater than a first threshold value, judging whether the first average value is greater than a second threshold value, and judging whether the second average value is greater than a third threshold value; and if the first average value is larger than a second threshold value and the second average value is larger than a third threshold value, determining that the motion state of the massage equipment is a wearing state.
Optionally, after determining whether the maximum peak is greater than a first threshold, the method further comprises: if the maximum peak value is smaller than or equal to a first threshold value, calculating the root mean square error of the piezoelectric signal; judging whether the root mean square error is in a preset range or not; if the root mean square error is in a preset range, determining that the motion state of the massage equipment is a wearing state; if the root mean square error is not in the preset range, calculating the duration of the current state; if the duration time is longer than the preset duration time, determining that the motion state of the massage equipment is a picking state; and if the duration is less than or equal to the preset duration, determining that the motion state of the massage equipment is a wearing body motion state, wherein the wearing state comprises a wearing body motion state and a wearing static state.
Optionally, after determining whether the maximum peak is greater than a first threshold, the method further comprises: if the maximum peak value is smaller than or equal to a first threshold value, judging whether the first average value is larger than a fourth threshold value, and judging whether the second average value is larger than a fifth threshold value, wherein the fourth threshold value is larger than the second threshold value, and the fifth threshold value is smaller than the third threshold value; and if the first average value is larger than the fourth threshold value and the second average value is larger than the fifth threshold value, determining that the motion state of the massage equipment is a picking state.
Optionally, after detecting the motion state of the massage device according to the autocorrelation characteristics, the method further comprises: if the motion state is a wearing state, collecting a heart rate value of a wearing medium of the massage equipment; extracting Heart Rate Variability (HRV) characteristics of the heart rate value; detecting a sleep state of the wearing medium according to the HRV characteristics, the heart rate value, and the motion state value of the wearing medium.
According to another embodiment of the present application, there is provided an intelligent massage apparatus of a state of wearing the massage apparatus, including: the acquisition module is used for acquiring piezoelectric signals of the piezoelectric sensor on the massage equipment according to the detection period; the generating module is used for generating an autocorrelation curve by adopting the piezoelectric signal; the extraction module is used for extracting the autocorrelation characteristics of the autocorrelation curve; a detection module, configured to detect a motion state of the massage device according to the autocorrelation feature, where the motion state includes: a placing state, a picking state and a wearing state.
Optionally, the extracting module includes: a calculation unit for counting the number of peaks of the autocorrelation curve, calculating a peak value of each peak, calculating a peak width of each peak, and calculating a peak relative height of each peak; a determining unit for determining the number of peaks, the peak value, the peak width, and the peak relative height as autocorrelation characteristics of the piezoelectric signal.
Optionally, the detection module includes: the first judging unit is configured to judge whether the number of peaks is within a preset range, where the autocorrelation feature includes: the number of peaks, the size of the peaks; a first determining unit, configured to determine that a motion state of the massage device is a placement state if the number of peaks is within a preset range; and the second determining unit is used for detecting the motion state of the massage equipment according to the wave crest size if the wave crest number is not in the preset range.
Optionally, the second determining unit includes: a calculating subunit, configured to determine a maximum peak value among all peak values, calculate a first average value of all peak widths, and calculate a second average value of all peak relative heights, where the peak sizes include: peak value, peak width, and peak relative height; a first judging subunit, configured to judge whether the maximum peak value is greater than a first threshold value; a second judging subunit, configured to judge whether the first average value is greater than a second threshold value and whether the second average value is greater than a third threshold value if the maximum peak value is greater than the first threshold value; and the determining subunit is used for determining that the motion state of the massage equipment is a wearing state if the first average value is larger than a second threshold value and the second average value is larger than a third threshold value.
Optionally, the detection module further comprises: the first calculating unit is used for calculating the root mean square error of the piezoelectric signal if the maximum peak value is smaller than or equal to a first threshold value; the second judging unit is used for judging whether the root mean square error is in a preset range or not; a third determining unit, configured to determine that the motion state of the massage device is a wearing state if the root mean square error is within a preset range; the second calculating unit is used for calculating the duration time of the current state if the root mean square error is not in a preset range; a fourth determining unit, configured to determine that the motion state of the massage device is a pickup state if the duration is longer than a preset duration; and a fifth determining unit, configured to determine that the motion state of the massage device is a wearing body motion state if the duration is less than or equal to a preset duration, where the wearing state includes a wearing body motion state and a wearing stationary state.
Optionally, the apparatus further comprises: the judging module is used for judging whether the first average value is larger than a fourth threshold value and judging whether the second average value is larger than a fifth threshold value if the maximum peak value is smaller than or equal to the first threshold value, wherein the fourth threshold value is larger than the second threshold value, and the fifth threshold value is smaller than the third threshold value; and the determining module is used for determining that the motion state of the massage equipment is a picking state if the first average value is larger than a fourth threshold value and the second average value is larger than a fifth threshold value.
Optionally, the apparatus further comprises: the acquisition module is used for acquiring the heart rate value of the wearing medium of the massage equipment if the motion state is the wearing state; the extraction module is used for extracting Heart Rate Variability (HRV) characteristics of the heart rate value; and the detection module is used for detecting the sleep state of the wearing medium according to the HRV characteristic, the heart rate value and the motion state value of the wearing medium.
According to a further embodiment of the application, there is also provided a storage medium, characterized in that the storage medium has stored therein a computer program, wherein the computer program is arranged to execute the steps of any of the above-mentioned apparatus embodiments when run.
According to a further embodiment of the application there is also provided an electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to run the computer program to perform the steps of any of the device embodiments described above.
According to the embodiment of the application, the piezoelectric signals of the piezoelectric sensor on the massage equipment are collected according to the detection period; generating an autocorrelation curve using the piezoelectric signal; extracting autocorrelation characteristics of the autocorrelation curve; detecting a motion state of the massage device according to the autocorrelation characteristics, wherein the motion state comprises: a placement state, a picking state, and a wearing state; by judging the motion state of the massage equipment by utilizing the peak characteristics of the autocorrelation curve, the problems that signals acquired by a sensor placed in the massage equipment are easily influenced by wearing tightness of the equipment, threshold values need to be adjusted, suitability is poor and the like are solved, the robustness of an equipment state detection algorithm is improved, and the suitability of the massage equipment in use is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
fig. 1 is a block diagram of a hardware configuration of a massage apparatus according to an embodiment of the present application;
fig. 2 is a flowchart of an intelligent detection method of a wearing state of a massage apparatus according to an embodiment of the present application;
FIG. 3 is a placement state autocorrelation curve in accordance with an embodiment of the present application;
FIG. 4 is a picking state autocorrelation curve in accordance with an embodiment of the present application;
fig. 5 is a wearing state autocorrelation curve according to an embodiment of the present application.
Fig. 6 is an overall flowchart of an intelligent detection method of the wearing state of the massage apparatus according to the embodiment of the present application;
fig. 7 is a block diagram of a massage apparatus according to an embodiment of the present application.
Detailed Description
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application. It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
The method embodiment provided by the first embodiment of the application can be implemented in a massage device of a massage device, a helmet, a massage device, a mobile phone or similar apparatus. Taking the operation on the massage device as an example, fig. 1 is a block diagram of the hardware structure of a massage device according to an embodiment of the present application. As shown in fig. 1, the massage device may include one or more processors 102 (only one is shown in fig. 1) (the processor 102 may include, but is not limited to, a microprocessor MCU or a processing means such as a programmable logic device FPGA) and a memory 104 for storing data, and optionally, a transmission device 106 for communication functions and an input-output device 108. It will be appreciated by those skilled in the art that the configuration shown in fig. 1 is merely illustrative and is not intended to limit the configuration of the massage device described above. For example, the massage device may also include more or fewer components than shown in fig. 1, or have a different configuration than shown in fig. 1.
The memory 104 may be used to store programs of the massage device, such as software programs and modules of application software, such as a program-based one of the embodiments of the present application, and the processor 102 performs various functional applications and data processing, i.e., implements the methods described above, by running the massage device programs stored in the memory 104. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory remotely located with respect to the processor 102, which may be connected to a server via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
In this embodiment, an intelligent detection method for a wearing state of a massage device is provided, fig. 2 is a flowchart of an intelligent detection method for a wearing state of a massage device according to an embodiment of the present application, as shown in fig. 2, where the flowchart includes the following steps:
step S202, collecting piezoelectric signals of a piezoelectric sensor on the massage equipment according to a detection period;
the massage device of the present embodiment may be a health device worn on the head, eyes, such as a head massager, an eye massager, or the like.
Alternatively, the detection period is a time interval for each data acquisition, and the piezoelectric signals of the piezoelectric sensor may be acquired by selecting a suitable microcontroller and an analog-to-digital converter (ADC), and storing the acquired piezoelectric signals in an internal memory or an external memory card of the computer.
Step S204, generating an autocorrelation curve by adopting the piezoelectric signal;
optionally, generating the autocorrelation curve includes the steps of calculating an autocorrelation function, determining a delay range, calculating an autocorrelation value, plotting the autocorrelation curve, fitting the autocorrelation curve, and the like, wherein fitting the autocorrelation curve requires fitting using an appropriate mathematical function, and a gaussian function or an exponential function may be used for fitting.
Step S206, extracting the autocorrelation characteristic of the autocorrelation curve;
optionally, the peak characteristics of the autocorrelation curve include the size of the peak, the width of the peak and the height of the peak.
Step S208, detecting a motion state of the massage device according to the autocorrelation feature, wherein the motion state includes: a placing state, a picking state and a wearing state.
The lifting state is a state of only moving the massage equipment and not wearing, the placing state is a resting state of the equipment which is not moving and not wearing, and the wearing state is a state of the equipment wearing on the head or eyes of a user.
Alternatively, the method for detecting the motion state of the massage device according to the autocorrelation characteristics is that the size of the peak, the width of the peak and the height of the peak are set as thresholds, and then different motion states of the massage device can be obtained through judgment.
According to the embodiment of the application, the piezoelectric signals of the piezoelectric sensor on the massage equipment are collected according to the detection period; generating an autocorrelation curve using the piezoelectric signal; extracting autocorrelation characteristics of the autocorrelation curve; detecting a motion state of the massage device according to the autocorrelation characteristics, wherein the motion state comprises: a placement state, a picking state, and a wearing state; by judging the motion state of the massage equipment by utilizing the peak characteristics of the autocorrelation curve, the influence of the wearing tightness of the equipment on the signals collected by the sensor placed on the massage equipment is avoided, threshold adjustment is not needed, the technical problem that the motion state error of the identification massage equipment is large in the related technology is solved, the robustness of the equipment state detection algorithm is improved, the accuracy of the identification of the motion state of the massage equipment is improved, and the use suitability of the massage equipment is improved.
In this embodiment, extracting the autocorrelation characteristic of the autocorrelation curve includes: counting the number of peaks of the autocorrelation curve, calculating the peak value of each peak, calculating the peak width of each peak, and calculating the peak relative height of each peak; the number of peaks, the peak value, the peak width, and the peak relative height are determined as auto-correlation features of the piezoelectric signal.
Alternatively, peak dimensions include peak value (absolute peak height), peak width, and peak relative height.
In this embodiment, detecting the motion state of the massage device from the autocorrelation characteristics includes: judging whether the number of wave peaks is within a preset range, wherein the autocorrelation characteristic comprises: the number of peaks, the size of the peaks; if the wave crest number is in a preset range, determining that the motion state of the massage equipment is a placement state; and if the number of the wave peaks is not in the preset range, detecting the motion state of the massage equipment according to the wave peak size.
Alternatively, the preset range is a range of 10 or more.
In this embodiment, piezoelectric signals of a piezoelectric sensor on the massage device are acquired according to a detection period, and an autocorrelation curve is generated by using the piezoelectric signals, fig. 3 is a placement state autocorrelation curve according to an embodiment of the present application, and fig. 4 is a picking state autocorrelation curve according to an embodiment of the present application; fig. 5 is a wearing state autocorrelation curve according to an embodiment of the present application. In fig. 3 and 4, the horizontal axis coordinate of fig. 5 is a delay value (proportional to time, delay value/piezoelectric signal sampling frequency=time), the vertical axis coordinate is an autocorrelation value, that is, amplitude, the peak characteristics of the autocorrelation in different states are different, the difference of the peaks on the curve is large, the delay value is the product of the piezoelectric signal sampling frequency and time, and the horizontal axis coordinate value corresponding to the peak is the product of the piezoelectric signal sampling frequency and the signal period.
In this embodiment, detecting the motion state of the massage device according to the peak size includes:
s11, determining the maximum peak value in all peak values, calculating a first average value of all peak widths, and calculating a second average value of all peak relative heights, wherein the peak sizes comprise: peak value, peak width, and peak relative height;
s12, judging whether the maximum peak value is larger than a first threshold value or not;
optionally, the first threshold is 0.5.
S13, if the maximum peak value is larger than a first threshold value, judging whether the first average value is larger than a second threshold value, and judging whether the second average value is larger than a third threshold value;
optionally, the second threshold is 8 and the third threshold is 0.8.
S14, if the first average value is larger than a second threshold value and the second average value is larger than a third threshold value, determining that the motion state of the massage equipment is a wearing state.
In the placement state shown in fig. 3, there are a plurality of peaks (more than 10) in the autocorrelation chart, there are peaks with larger peaks (more than 0.5), the width of the peaks is smaller (less than 5), and the relative height of the peaks is smaller (less than 0.1); in the lifted state shown in fig. 4, the number of peaks in the autocorrelation chart is small (2), the peak value is generally small (less than 0.3), but the width of the peak value is large (more than 15), and the relative height of the peak is high (more than 0.5); in the wearing state shown in fig. 5, the number of peaks in the autocorrelation chart is small (3), there are peaks with large peaks (greater than 0.5), the width of the peak is large (greater than 8), and the relative height of the peak is large (greater than 0.8).
In this embodiment, after determining whether the maximum peak value is greater than a first threshold value, the method further includes:
s21, if the maximum peak value is smaller than or equal to a first threshold value, calculating the root mean square error of the piezoelectric signal;
optionally, the method for calculating the root mean square error includes comparing the actual value and the predicted value of the piezoelectric signal, calculating the difference between the actual value and the predicted value for each data point, squaring each difference to obtain a square error, adding all the square errors, dividing by the number of data points, and performing square root operation on the result, wherein the formula can be usedThe representation, where RMSE is root mean square error, n is the number of data points acquired for the piezoelectric signal, +.>Is the actual value of a single point, +.>Is a predicted value for a single point.
S22, judging whether the root mean square error is in a preset range or not;
s23, if the root mean square error is in a preset range, determining that the motion state of the massage equipment is a wearing state; if the root mean square error is not in the preset range, calculating the duration of the current state;
s24, if the duration time is longer than a preset duration time, determining that the motion state of the massage equipment is a picking state; and if the duration is less than or equal to the preset duration, determining that the motion state of the massage equipment is a wearing body motion state, wherein the wearing state comprises a wearing body motion state and a wearing static state.
Wherein, the wearing body movement state also belongs to a wearing state.
Optionally, the preset duration is 3 seconds, and the duration of the body movement signal is short, so that the device can be judged whether to be in a picking state or a body movement state by counting the duration of the body movement signal.
In this embodiment, after determining whether the maximum peak value is greater than the first threshold value, the method further includes: if the maximum peak value is smaller than or equal to a first threshold value, judging whether the first average value is larger than a fourth threshold value, and judging whether the second average value is larger than a fifth threshold value, wherein the fourth threshold value is larger than the second threshold value, and the fifth threshold value is smaller than the third threshold value; and if the first average value is larger than the fourth threshold value and the second average value is larger than the fifth threshold value, determining that the motion state of the massage equipment is a picking state.
Optionally, the fourth threshold is 15 and the fifth threshold is 0.5.
In this embodiment, after detecting the motion state of the massage device according to the autocorrelation characteristics, it further includes: if the motion state is a wearing state, collecting a heart rate value of a wearing medium of the massage equipment; extracting Heart Rate Variability (HRV) characteristics of the heart rate value; detecting a sleep state of the wearing medium according to the HRV characteristics, the heart rate value, and the motion state value of the wearing medium.
Wherein the wearing medium is an object wearing the massage device, such as human, animal, etc.
Optionally, the method for collecting heart rate of the wearing medium of the massage device comprises calculating time interval (JJ-interval) between two J peaks of the BCG signal as cycle of heart beat calculation, and using formulaHeart rate is calculated.
Optionally, HRV (Heart rate variability) is a method for measuring a degree of change in a continuous heart rate, wherein the HRV features include time domain features and frequency domain features, and wherein the time domain features include: NN (mean of JJ-intervals within 5 min), SDNN (standard deviation of JJ-intervals within 5 min), RMSSD (root mean square of differences between adjacent JJ-intervals), NN50 (all pairs of adjacent normal heartbeat time intervals in an electrocardiogram, the difference exceeds 50 milliseconds); the frequency domain features include: ultra-low frequency range (0.0033 Hz-0.04 Hz), low frequency range (0.04 Hz-0.15 Hz), high frequency range (0.15 Hz-0.4 Hz), and ratio of low and high frequency power.
Optionally, a decision tree algorithm is used to determine the sleep state of the wearer based on the HRV characteristics, the heart rate value, and the state of motion value of the wearing medium.
In this embodiment, after detecting the motion state of the massage device according to the autocorrelation characteristics, the method further includes: and if the motion state is the body motion state of the wearing state, compensating the heart rate value by using the heart rate average value for a period of time.
Alternatively, the heart rate average over the period of time may calculate an average heart rate value of 5 seconds before the onset of the body movement state and take this value as the heart rate value over the period of time of the body movement state.
Fig. 6 is an overall flowchart of an intelligent detection method of a wearing state of a massage apparatus according to an embodiment of the present application, as shown in fig. 6, the flowchart including the steps of:
filtering piezoelectric signals acquired by piezoelectric sensors on the massage equipment; calculating the filtered piezoelectric signal to obtain an autocorrelation curve;
judging whether the number of the autocorrelation peaks is within a preset range, and if the number of the peaks of the autocorrelation curves is not within the preset range, placing the massage equipment; if the number of peaks of the autocorrelation curve is in a preset range, acquiring autocorrelation peak characteristics, and judging whether the autocorrelation characteristics are in the preset range, wherein the autocorrelation peak characteristics comprise peak size, peak width and peak height; optionally, the preset range is 10 or more;
if the autocorrelation characteristic is in a preset range, the massage equipment is in a wearing state, JJ-interval is calculated, and then heart rate is calculated according to the JJ-interval; if the characteristics are not in the preset range, calculating the root mean square error of the filtered piezoelectric signals, judging whether the root mean square error is in the preset range, if the root mean square error is in the preset range, putting the massage equipment in a wearing state, and if the root mean square error is not in the preset range, putting the massage equipment in a body movement state or a moving state of the wearing state;
optionally, JJ-interval is a period for calculating the heartbeat, the BCG ballistocardiogram has one J peak in one period, and JJ-interval is a time interval between two peaks;
judging whether the continuous duration of the state is greater than a threshold value T1, if the continuous duration of the state is greater than the threshold value T1, the massage equipment is in a lifting movement state, and if the continuous duration of the state is less than or equal to the threshold value T1, the massage equipment is in a short-time body movement state, and using the heart rate average value in the T2 period to compensate the heart rate;
optionally, the threshold T1 is 3 seconds, and the duration of the body movement signal is short, typically less than 3 seconds, so that it can be determined whether the device is in the picking movement state by counting the duration of the body movement signal; the T2 period is 5 seconds, and the average heart rate value in 5 seconds before the short-time body movement starts is calculated and taken as the heart rate value of the short-time body movement period.
And extracting HRV time-frequency domain characteristics according to the calculated heart rate value and the compensated heart rate value, and detecting the sleep state by combining the heart rate value and the state value.
Optionally, HRV heart rate variability is a method for measuring the degree of continuous heart rate variation, and the sleep state of the wearer is judged by using a decision tree algorithm on HRV time-frequency domain characteristics and heart rate values.
According to the embodiment of the application, the motion state of the massage equipment is judged by utilizing the peak characteristics of the autocorrelation curve, so that the problems that signals acquired by a sensor arranged on the massage equipment are easy to be influenced by the wearing tightness of the equipment, the threshold value needs to be adjusted, the suitability is poor and the like are solved, the robustness of an equipment state detection algorithm is improved, the suitability of the use of the massage equipment is improved, the heart rate value of a wearing medium of the massage equipment is acquired, the heart rate variability HRV characteristic of the heart rate value is extracted, the sleeping state of a wearer can be detected, and the function of a wearing device is increased.
From the description of the above embodiments, it will be clear to a person skilled in the art that the method according to the above embodiments may be implemented by means of software plus the necessary general hardware platform, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present application.
Example 2
In this embodiment, an intelligent massaging device for massaging a wearing state of the device is further provided, and the device is used for implementing the foregoing embodiments and preferred embodiments, and will not be described again. The term "module" as used below may implement a combination of software and hardware for subscription functions. While the means described in the following embodiments are preferably implemented in software, implementations in hardware, or a combination of software and hardware, are also contemplated.
Fig. 7 is a block diagram of a massage apparatus according to an embodiment of the present application, which is a massage apparatus for intelligent massage in a wearing state, as shown in fig. 7, and includes:
the acquisition module 70 is used for acquiring piezoelectric signals of the piezoelectric sensor on the massage equipment according to the detection period;
a generating module 72 for generating an autocorrelation curve using the piezoelectric signal;
an extraction module 74 for extracting autocorrelation characteristics of the autocorrelation curve;
a detection module 76 for detecting a motion state of the massage device based on the autocorrelation characteristics, wherein the motion state comprises: a placing state, a picking state and a wearing state.
Optionally, the extracting module includes: a calculation unit for counting the number of peaks of the autocorrelation curve, calculating a peak value of each peak, calculating a peak width of each peak, and calculating a peak relative height of each peak; a determining unit for determining the number of peaks, the peak value, the peak width, and the peak relative height as autocorrelation characteristics of the piezoelectric signal.
Optionally, the detection module includes: the first judging unit is configured to judge whether the number of peaks is within a preset range, where the autocorrelation feature includes: the number of peaks, the size of the peaks; a first determining unit, configured to determine that a motion state of the massage device is a placement state if the number of peaks is within a preset range; and the second determining unit is used for detecting the motion state of the massage equipment according to the wave crest size if the wave crest number is not in the preset range.
Optionally, the second determining unit includes: a calculating subunit, configured to determine a maximum peak value among all peak values, calculate a first average value of all peak widths, and calculate a second average value of all peak relative heights, where the peak sizes include: peak value, peak width, and peak relative height; a first judging subunit, configured to judge whether the maximum peak value is greater than a first threshold value; a second judging subunit, configured to judge whether the first average value is greater than a second threshold value and whether the second average value is greater than a third threshold value if the maximum peak value is greater than the first threshold value; and the determining subunit is used for determining that the motion state of the massage equipment is a wearing state if the first average value is larger than a second threshold value and the second average value is larger than a third threshold value.
Optionally, the detection module further comprises: the first calculating unit is used for calculating the root mean square error of the piezoelectric signal if the maximum peak value is smaller than or equal to a first threshold value; the second judging unit is used for judging whether the root mean square error is in a preset range or not; a third determining unit, configured to determine that the motion state of the massage device is a wearing state if the root mean square error is within a preset range; the second calculating unit is used for calculating the duration time of the current state if the root mean square error is not in a preset range; a fourth determining unit, configured to determine that the motion state of the massage device is a pickup state if the duration is longer than a preset duration; and a fifth determining unit, configured to determine that the motion state of the massage device is a wearing body motion state if the duration is less than or equal to a preset duration, where the wearing state includes a wearing body motion state and a wearing stationary state.
Optionally, the apparatus further comprises: the judging module is used for judging whether the first average value is larger than a fourth threshold value and judging whether the second average value is larger than a fifth threshold value if the maximum peak value is smaller than or equal to the first threshold value, wherein the fourth threshold value is larger than the second threshold value, and the fifth threshold value is smaller than the third threshold value; and the determining module is used for determining that the motion state of the massage equipment is a picking state if the first average value is larger than a fourth threshold value and the second average value is larger than a fifth threshold value.
Optionally, the apparatus further comprises: the acquisition module is used for acquiring the heart rate value of the wearing medium of the massage equipment if the motion state is the wearing state; the extraction module is used for extracting Heart Rate Variability (HRV) characteristics of the heart rate value; and the detection module is used for detecting the sleep state of the wearing medium according to the HRV characteristic, the heart rate value and the motion state value of the wearing medium.
It should be noted that each of the above modules may be implemented by software or hardware, and for the latter, it may be implemented by, but not limited to: the modules are all located in the same processor; alternatively, the above modules may be located in different processors in any combination.
Example 3
An embodiment of the application also provides a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the method embodiments described above when run.
Alternatively, in the present embodiment, the above-described storage medium may be configured to store a computer program for execution:
s1, collecting piezoelectric signals of a piezoelectric sensor on massage equipment according to a detection period;
s2, generating an autocorrelation curve by adopting the piezoelectric signal;
s3, extracting autocorrelation characteristics of the autocorrelation curve;
s4, detecting the motion state of the massage equipment according to the autocorrelation characteristics, wherein the motion state comprises the following steps: a placing state, a picking state and a wearing state.
Alternatively, in the present embodiment, the storage medium may include, but is not limited to: a usb disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing a computer program.
An embodiment of the application also provides an electronic device comprising a memory having stored therein a computer program and a processor arranged to run the computer program to perform the steps of any of the method embodiments described above.
Optionally, the electronic apparatus may further include a transmission device and an input/output device, where the transmission device is connected to the processor, and the input/output device is connected to the processor.
Alternatively, in the present embodiment, the above-described processor may be configured to execute the following steps by a computer program:
s1, collecting piezoelectric signals of a piezoelectric sensor on massage equipment according to a detection period;
s2, generating an autocorrelation curve by adopting the piezoelectric signal;
s3, extracting autocorrelation characteristics of the autocorrelation curve;
s4, detecting the motion state of the massage equipment according to the autocorrelation characteristics, wherein the motion state comprises the following steps: a placing state, a picking state and a wearing state.
Alternatively, specific examples in this embodiment may refer to examples described in the foregoing embodiments and optional implementations, and this embodiment is not described herein.
The foregoing embodiment numbers of the present application are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present application, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and are merely a logical functional division, and there may be other manners of dividing the apparatus in actual implementation, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method of the various embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application, which are intended to be comprehended within the scope of the present application.
Claims (10)
1. An intelligent detection method for the wearing state of massage equipment is characterized by comprising the following steps:
collecting piezoelectric signals of a piezoelectric sensor on the massage equipment according to the detection period;
generating an autocorrelation curve using the piezoelectric signal;
extracting autocorrelation characteristics of the autocorrelation curve;
detecting a motion state of the massage device according to the autocorrelation characteristics, wherein the motion state comprises: a placing state, a picking state and a wearing state.
2. The method of claim 1, wherein extracting the autocorrelation characteristics of the autocorrelation curve comprises:
counting the number of peaks of the autocorrelation curve, calculating the peak value of each peak, calculating the peak width of each peak, and calculating the peak relative height of each peak;
the number of peaks, the peak value, the peak width, and the peak relative height are determined as auto-correlation features of the piezoelectric signal.
3. The method of claim 1, wherein detecting the motion state of the massage device from the autocorrelation characteristics comprises:
judging whether the number of wave peaks is within a preset range, wherein the autocorrelation characteristic comprises: the number of peaks, the size of the peaks;
if the wave crest number is in a preset range, determining that the motion state of the massage equipment is a placement state; and if the number of the wave peaks is not in the preset range, detecting the motion state of the massage equipment according to the wave peak size.
4. A method according to claim 3, wherein detecting the motion state of the massage device from the peak size comprises:
determining the maximum peak value of all peak values, calculating a first average value of all peak widths, and calculating a second average value of all peak relative heights, wherein the peak sizes comprise: peak value, peak width, and peak relative height;
judging whether the maximum peak value is larger than a first threshold value or not;
if the maximum peak value is greater than a first threshold value, judging whether the first average value is greater than a second threshold value, and judging whether the second average value is greater than a third threshold value;
and if the first average value is larger than a second threshold value and the second average value is larger than a third threshold value, determining that the motion state of the massage equipment is a wearing state.
5. The method of claim 4, wherein after determining whether the maximum peak is greater than a first threshold, the method further comprises:
if the maximum peak value is smaller than or equal to a first threshold value, calculating the root mean square error of the piezoelectric signal;
judging whether the root mean square error is in a preset range or not;
if the root mean square error is in a preset range, determining that the motion state of the massage equipment is a wearing state; if the root mean square error is not in the preset range, calculating the duration of the current state;
if the duration time is longer than the preset duration time, determining that the motion state of the massage equipment is a picking state; and if the duration is less than or equal to the preset duration, determining that the motion state of the massage equipment is a wearing body motion state, wherein the wearing state comprises a wearing body motion state and a wearing static state.
6. The method of claim 4, wherein after determining whether the maximum peak is greater than a first threshold, the method further comprises:
if the maximum peak value is smaller than or equal to a first threshold value, judging whether the first average value is larger than a fourth threshold value, and judging whether the second average value is larger than a fifth threshold value, wherein the fourth threshold value is larger than the second threshold value, and the fifth threshold value is smaller than the third threshold value;
and if the first average value is larger than the fourth threshold value and the second average value is larger than the fifth threshold value, determining that the motion state of the massage equipment is a picking state.
7. The method according to claim 1, wherein after detecting the motion state of the massage device from the autocorrelation characteristics, the method further comprises:
if the motion state is a wearing state, collecting a heart rate value of a wearing medium of the massage equipment;
extracting Heart Rate Variability (HRV) characteristics of the heart rate value;
detecting a sleep state of the wearing medium according to the HRV characteristics, the heart rate value, and the motion state value of the wearing medium.
8. An intelligent massage device in a state of wearing the massage device, comprising:
the acquisition module is used for acquiring piezoelectric signals of the piezoelectric sensor on the massage equipment according to the detection period;
the generating module is used for generating an autocorrelation curve by adopting the piezoelectric signal;
the extraction module is used for extracting the autocorrelation characteristics of the autocorrelation curve;
a detection module, configured to detect a motion state of the massage device according to the autocorrelation feature, where the motion state includes: a placing state, a picking state and a wearing state.
9. A storage medium having a computer program stored therein, wherein the computer program is arranged to perform the method of any of claims 1 to 7 when run.
10. An electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to run the computer program to perform the method of any of the claims 1 to 7.
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