WO2021233018A1

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

Info

Publication number: WO2021233018A1
Application number: PCT/CN2021/086963
Authority: WO
Inventors: 赵帅; 杨斌; 任慧超; 李玥
Original assignee: 华为技术有限公司
Priority date: 2020-05-20
Filing date: 2021-04-13
Publication date: 2021-11-25
Also published as: CN113693556A

Abstract

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

Description

Method and device for detecting muscle fatigue after exercise, and electronic equipment

This application claims the priority of a Chinese patent application filed with the Chinese Patent Office on May 20, 2020, the application number is 202010430716.0, and the application name is "Methods and devices for detecting muscle fatigue after exercise, and electronic equipment". The entire content of the application is approved. The reference is incorporated in this application.

Technical field

This application relates to the technical field of intelligent detection equipment, and in particular to methods and devices for detecting muscle fatigue after exercise, and electronic equipment.

Background technique

Muscle fatigue after exercise is an important indicator for evaluating exercise intensity and body recovery function after exercise. The current main measurement methods are blood lactic acid method and surface electromyography method. Among them, the blood lactic acid method uses blood lactic acid to detect muscle fatigue with high accuracy, but it needs to collect blood, which cannot be accepted by most groups; the surface electromyography method uses surface electromyogram (Emg, Electroyogram) for evaluation, which can be measured by muscle mass. Muscle fatigue, but electrodes need to be attached to different muscle masses, which is cumbersome to operate and is not suitable for ordinary users to measure by themselves. In order to detect the degree of muscle fatigue after exercise, so as to evaluate the exercise intensity for users, it is urgent to provide a muscle fatigue detection solution that is convenient for detection and acceptable to users.

Summary of the invention

The embodiments of the present application provide a method, device, electronic equipment, weight measuring equipment, computer storage medium and computer program product for detecting muscle fatigue after exercise, which can detect the muscle fatigue of the user after exercise, and facilitate the user to check after exercise. Muscle fatigue is self-measured and user acceptance is high.

In the first aspect, an embodiment of the present application provides a method for detecting muscle fatigue after exercise, which is applied to a device for detecting muscle fatigue after exercise, and the method includes:

Acquiring the body state parameters of the user before exercise and the body state parameters after exercise, where the body state parameters include body stability, heart rate, breathing frequency, and breathing intensity;

The muscle fatigue degree of the user after exercise is calculated according to the body state parameter of the user before exercise and the body state parameter of the user after exercise.

With reference to the first aspect, in a feasible implementation manner, the acquiring the physical state parameters of the user before exercise includes:

Receive a first pressure signal generated by a weight measurement device, and determine the physical state parameter of the user before exercise according to the first pressure signal, wherein the first pressure signal is used by the weight measurement device before the user exercises Said weight measurement device is generated when the weight measurement is performed;

The acquiring the physical state parameters of the user after exercise includes:

Receive a second pressure signal generated by the weight measurement device, and determine the body state parameter of the user after exercise according to the second pressure signal, wherein the second pressure signal is used by the weight measurement device after the user exercises The weight measurement device is generated when the weight measurement is performed.

Before the user exercises, measure the weight of the user, generate a first pressure signal according to the pressure applied by the user, and determine the physical state parameters of the user before the exercise according to the first pressure signal;

After the user exercises, measure the weight of the user, generate a second pressure signal according to the pressure applied by the user, and determine the body state parameter of the user after the exercise according to the second pressure signal.

With reference to the first aspect, in a feasible implementation manner, the method further includes:

Acquiring the motion parameter corresponding to the user motion;

The heart recovery index and the lung recovery index of the user are calculated according to the body state parameters of the user before exercise, the body state parameters of the user after exercise, and the exercise parameters of the user.

With reference to the first aspect, in a feasible implementation manner, the calculation of the muscle fatigue of the user after exercise according to the physical state parameters of the user before exercise and the physical state parameters of the user after exercise includes:

The muscle fatigue degree of the user after exercise is calculated according to the physical state parameters of the user before exercise, the physical state parameters of the user after exercise, and the exercise parameters of the user.

With reference to the first aspect, in a feasible implementation manner, the exercise parameter includes one or more of exercise type, exercise intensity, exercise duration, post-exercise duration, and exercise parameter credibility.

With reference to the first aspect, in a feasible implementation manner, the determining the physical state parameters of the user before exercise according to the first pressure signal includes:

The first pressure signal is sequentially subjected to high-pass amplification processing and low-pass filtering processing to obtain a first signal, and the first signal is subjected to high-pass filtering processing to obtain a pre-exercise cardiac shock signal, according to the pre-exercise cardiac shock The signal determines the heart rate before exercise, the waveform profile of the cardiac shock signal before exercise is determined according to the waveform change of the cardiac shock signal before exercise, and the exercise is calculated according to the peak feature points of the waveform contour of the cardiac shock signal before exercise The pre-exercise respiration frequency and the pre-exercise respiration intensity calculated according to the peak-to-peak value of the waveform profile of the pre-exercise cardiac shock signal;

Perform low-pass filtering on the first signal to obtain a second signal, calculate the dominant frequency, peak-to-peak value and standard deviation of the second signal, and calculate based on the dominant frequency, peak-to-peak value and standard deviation of the second signal Body stability before exercise.

With reference to the first aspect, in a feasible implementation manner, the determining the body state parameter of the user after exercise according to the second pressure signal includes:

The second pressure signal is sequentially subjected to high-pass amplification processing and low-pass filtering processing to obtain a third signal, and the third signal is subjected to high-pass filtering processing to obtain a post-exercise cardiac shock signal, according to the post-exercise cardiac shock The signal determines the heart rate after exercise, the waveform contour of the post-exercise cardiac shock signal is determined according to the waveform change of the post-exercise cardiac shock signal, and the exercise is calculated according to the peak feature points of the waveform contour of the post-exercise cardiac shock signal The post-exercise respiration frequency and the post-exercise respiration intensity calculated according to the peak-to-peak value of the waveform profile of the post-exercise cardiac shock signal;

Perform low-pass filtering on the third signal to obtain a fourth signal, calculate the dominant frequency, peak-to-peak value and standard deviation of the fourth signal, and calculate based on the dominant frequency, peak-to-peak value and standard deviation of the fourth signal Body stability after exercise.

Calculate the body stability difference between the body stability of the user before exercise and the body stability of the user after exercise, and calculate the heart rate between the heart rate of the user before exercise and the heart rate of the user after exercise Difference, and calculating the difference between the breathing frequency of the user before exercise and the breathing frequency of the user after exercise, and calculating the difference between the breathing intensity of the user before exercise and the breathing intensity of the user after exercise Difference in respiratory intensity between;

According to the difference in body stability, the difference in heart rate, the difference in respiratory rate, and the difference in respiratory intensity, a weighted average algorithm is used to calculate the user's muscle fatigue after exercise.

With reference to the first aspect, in a feasible implementation manner, the user’s heart after exercise is calculated according to the user’s body state parameters before exercise, the user’s body state parameters after exercise, and the user’s exercise parameters. Recovery index and lung recovery index, including:

According to the body stability difference, the heart rate difference, the respiratory frequency difference, and the respiratory intensity difference, combined with the user's exercise parameters, a weighted average algorithm is used to calculate the user's heart after exercise Recovery index and lung recovery index.

With reference to the first aspect, in a feasible implementation manner, the calculation of the user’s physical state parameters according to the user’s physical state parameters before exercise, the user’s physical state parameter calculations after exercise, and the user’s exercise parameters Muscle fatigue, heart recovery index and lung recovery index, including:

According to the body stability difference, the heart rate difference, the breathing rate difference, and the breathing intensity difference, combined with the user's exercise parameters, a neural network is used to calculate the user's muscle fatigue and heart rate. Recovery index and lung recovery index.

Display any one or any of the calculated muscle fatigue, heart recovery index, and lung recovery index of the user, and one or any of the user’s muscle fatigue, heart recovery index, and lung recovery index Multiple generations and displays one or more of exercise volume evaluation, physical function evaluation, exercise suggestion, and physical recovery suggestion.

In a second aspect, an embodiment of the present application provides a device for detecting muscle fatigue after exercise, and the device includes:

The first acquisition module is used to acquire the body state parameters of the user before exercise and the body state parameters after exercise, wherein the body state parameters include body stability, heart rate, breathing rate, and breathing intensity; and

The first calculation module is configured to calculate the muscle fatigue of the user after exercise according to the physical state parameters of the user before exercise and the physical state parameters of the user after exercise.

It can be understood that the embodiment of the present invention obtains the body state parameters of the user before exercise and the body state parameters after exercise, and then calculates the user's body state parameters based on the body state parameters of the user before exercise and the body state parameters of the user after exercise. The muscle fatigue after exercise does not need to collect the user's blood, the operation is simple, and the user acceptability is high. At the same time, the muscle fatigue detection can be realized by relying on the weight measurement equipment, without the need for special testing equipment.

With reference to the second aspect, in a feasible implementation manner, the first acquisition module may include:

The first acquisition unit is configured to receive the first pressure signal generated by the weight measurement device, and determine the physical state parameter of the user before exercise according to the first pressure signal, wherein the first pressure signal is used by the weight measurement device Generated when the user uses the weight measurement device to perform weight measurement before the user exercises; and

The second acquisition unit is configured to receive a second pressure signal generated by the weight measurement device, and determine the body state parameter of the user after exercise according to the second pressure signal, wherein the second pressure signal is generated by the weight measurement device It is generated when the user uses the weight measurement device to perform weight measurement after the user exercises.

The first determining unit is configured to measure the weight of the user before the user exercises, generate a first pressure signal according to the pressure applied by the user, and determine according to the first pressure signal that the user is before the exercise Physical state parameters; and

The second determining unit is configured to measure the weight of the user after the user exercises, generate a second pressure signal according to the pressure applied by the user, and determine that the user is after exercise according to the second pressure signal Physical state parameters.

With reference to the second aspect, in a feasible implementation manner, the device may further include:

The second acquisition module is used to acquire the motion parameters corresponding to the user motion; and

The second calculation module is configured to calculate the heart recovery index and lung recovery index of the user according to the physical state parameters of the user before exercise, the physical state parameters of the user after exercise, and the exercise parameters of the user.

With reference to the second aspect, in a feasible implementation manner, the first calculation module may include:

The first calculation unit is configured to calculate the muscle fatigue of the user after exercise according to the physical state parameters of the user before exercise, the physical state parameters of the user after exercise, and the exercise parameters of the user.

With reference to the second aspect, in a feasible implementation manner, the exercise parameter includes one or any more of exercise type, exercise intensity, exercise duration, post-exercise duration, and exercise parameter credibility.

With reference to the second aspect, in a feasible implementation manner, the first acquiring module or the first determining module may include:

The first processing unit is configured to sequentially perform high-pass amplification processing and low-pass filtering processing on the first pressure signal to obtain a first signal, perform high-pass filtering processing on the first signal to obtain a pre-exercise cardiac shock signal, according to The cardiac shock signal before exercise determines the heart rate before exercise, the waveform profile of the cardiac shock signal before exercise is determined according to the waveform change of the cardiac shock signal before exercise, and the waveform of the cardiac shock signal before exercise is determined. Calculating the pre-exercise respiration frequency with the peak feature points of the contour and calculating the pre-exercise respiration intensity according to the peak-to-peak value of the waveform contour of the pre-exercise cardiac shock signal; and

The second processing unit is configured to perform low-pass filter processing on the first signal to obtain a second signal, calculate the dominant frequency, peak-to-peak value, and standard deviation of the second signal, according to the dominant frequency of the second signal , Peak-to-peak value and standard deviation to calculate the body stability before exercise.

With reference to the second aspect, in a feasible implementation manner, the second acquiring module or the second determining module may include:

The third processing unit is configured to sequentially perform high-pass amplification processing and low-pass filter processing on the second pressure signal to obtain a third signal, perform high-pass filter processing on the third signal to obtain a post-exercise cardiac shock signal, according to The post-exercise cardiac shock signal determines the post-exercise heart rate, the waveform profile of the post-exercise cardiac shock signal is determined according to the waveform change of the post-exercise cardiac shock signal, and the waveform profile of the post-exercise cardiac shock signal is determined according to the waveform of the post-exercise cardiac shock signal The peak feature points of the contour calculate the post-exercise respiration frequency, and the post-exercise respiration intensity is calculated according to the peak-to-peak value of the waveform contour of the post-exercise cardiac shock signal; and

The fourth processing unit is configured to perform low-pass filtering processing on the third signal to obtain a fourth signal, calculate the main frequency, peak-to-peak value and standard deviation of the fourth signal, and calculate the main frequency of the fourth signal according to the main frequency of the fourth signal. , Peak-to-peak value and standard deviation to calculate the body stability after exercise.

The second calculation unit is used to calculate the body stability difference between the body stability of the user before exercise and the body stability of the user after exercise, and to calculate the heart rate of the user before exercise and the user exercise The heart rate difference between the heart rates after the exercise, and the respiration rate difference between the respiration rate of the user before exercise and the respiration rate of the user after exercise, and the calculation of the respiration intensity of the user before exercise and the The difference in breathing intensity between the breathing intensity of the user after exercise; and

The third calculation unit is configured to calculate the muscle fatigue degree of the user after exercise by using a weighted average algorithm according to the difference in body stability, the difference in heart rate, the difference in respiratory frequency, and the difference in respiratory intensity.

In a feasible implementation manner, the second calculation unit may further include:

The first calculation subunit is configured to adopt a weighted average algorithm according to the difference in body stability, the difference in heart rate, the difference in respiratory frequency, and the difference in respiratory intensity, in combination with the exercise parameters of the user Calculate the heart recovery index and lung recovery index of the user after exercise.

With reference to the second aspect, in a feasible implementation manner, the first calculation unit may include:

The second calculation subunit is used to calculate the difference in body stability between the body stability of the user before exercise and the body stability of the user after exercise, and to calculate the difference between the user’s heart rate before exercise and the user’s The heart rate difference between the heart rates after exercise, and the respiration rate difference between the respiration rate of the user before exercise and the respiration rate of the user after exercise, and the calculation of the respiration intensity of the user before exercise and the total The breathing intensity difference between the breathing intensity of the user after exercise; and

The third calculation subunit is used to calculate the difference in body stability, the difference in heart rate, the difference in respiration rate, and the difference in respiration intensity, in combination with the exercise parameters of the user, using a neural network to calculate Describes the user’s muscle fatigue, heart recovery index and lung recovery index.

The first display module is used to display any one or more of the calculated muscle fatigue, heart recovery index, and lung recovery index of the user, and according to the user’s muscle fatigue, heart recovery index, and lung recovery index. One or any more of the recovery index generates and displays one or any more of exercise volume evaluation, physical function evaluation, exercise suggestion, and physical recovery suggestion.

In a third aspect, an embodiment of the present application provides an electronic device that includes a memory, a processor, a touch sensor, and a display screen. The memory stores a computer program, the processor is connected to the memory, and the The processor executes a computer program to implement the above-mentioned method for detecting muscle fatigue after exercise.

In a fourth aspect, an embodiment of the present application provides a weight measurement device, the weight measurement device includes a memory, a processor, a touch sensor, and a display screen, the memory stores a computer program, and the processor is connected to the memory The processor executes the computer program to realize the above-mentioned method for detecting muscle fatigue after exercise.

In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, including computer instructions, which, when the computer instructions run on an electronic device, cause the electronic device to perform the first aspect or any one of the first aspect. The instruction of the method in the implementation mode.

In a sixth aspect, the embodiments of the present application provide a computer program product, which when the computer program product runs on a computer, causes the computer to execute the method in the first aspect or any possible implementation of the first aspect instruction.

It can be understood that the embodiment of the present invention obtains the body state parameters of the user before exercise and the body state parameters after exercise, and then calculates the user’s post-exercise based on the user’s body state parameters before exercise and the user’s body state parameters after exercise. The muscle fatigue is not required to collect the user’s blood, the operation is simple, and the user’s acceptance is high. At the same time, the muscle fatigue detection can be achieved by relying on the weight measurement device without special detection equipment. At the same time, the detection method provided by the embodiment of the present invention is based on pressure Detection without changing the form of hardware products.

Description of the drawings

FIG. 1 is a schematic structural diagram of an electronic device provided by an embodiment of the application;

2 is a block diagram of the software structure of an electronic device according to an embodiment of the present invention;

3 is a flowchart of a method for detecting muscle fatigue after exercise provided by an embodiment of the present invention;

4 is an exemplary application framework diagram of a method for detecting muscle fatigue after exercise provided by an embodiment of the present invention;

FIG. 5 is an exemplary detection flow chart of a user's body state parameter before exercise according to an embodiment of the present invention;

FIG. 6 is an exemplary flow chart for detecting muscle fatigue, heart recovery index, and lung recovery index after exercise of a user according to an embodiment of the present invention;

FIG. 7 is an exemplary schematic diagram of measuring a user's body state parameters before/after exercise according to an embodiment of the present invention;

8 is a schematic diagram of calculating muscle fatigue, heart recovery index, and lung recovery index through neural network according to an embodiment of the present invention;

9 is a state selection interface before and after exercise of an exemplary device for detecting muscle fatigue after exercise provided by an embodiment of the present invention;

Fig. 10 is an exemplary setting interface for automatic acquisition of sports parameters provided by an embodiment of the present invention;

Fig. 11 is an exemplary manual input selection interface for sports parameters provided by an embodiment of the present invention;

Fig. 12 is an exemplary motion parameter input interface provided by an embodiment of the present invention;

Fig. 13 is yet another exemplary motion parameter input interface provided by the embodiment of the present invention;

FIG. 14 is an exemplary measurement result display interface provided by an embodiment of the present invention;

FIG. 15 is a schematic diagram of a device for detecting muscle fatigue after exercise provided by an embodiment of the application.

Detailed ways

In order to better understand the technical solutions of the present invention, the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

It should be clear that the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

In this application, "at least one" refers to one or more, and "multiple" refers to two or more. "And/or" describes the association relationship of the associated objects, indicating that there can be three relationships, for example, A and/or B, which can mean: A alone exists, A and B exist at the same time, and B exists alone, where A, B can be singular or plural. The character "/" generally indicates that the associated objects before and after are in an "or" relationship. "The following at least one item (a)" or similar expressions refers to any combination of these items, including any combination of a single item (a) or a plurality of items (a). For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple .

The method for detecting muscle fatigue of a user after exercise provided by the embodiments of the present application can be applied to the electronic device 100 shown in FIG. 1, and the electronic device 100 shown in FIG. Portable electronic devices with player functions, such as mobile phones, tablet computers, wearable devices with wireless communication functions (such as smart watches), etc. The above-mentioned portable electronic devices are, for example, laptop computers with touch panels.

In some embodiments, the electronic device 100 includes a straight-face display screen, a curved display screen, or a foldable display screen. The electronic device 100 collects the touch points in a preset area when the user holds the electronic device 100, and uploads the touch points to the cloud server 200. The cloud server 200 determines the user's holding posture according to the touch points, and feeds back the holding posture to the electronic device. Equipment 100. In other embodiments, the electronic terminal 100 can also determine the user's holding posture based on the touch point, which is not limited herein.

As shown in FIG. 1, the electronic device 100 is taken as an example to describe the embodiments in detail.

The electronic device 100 may include a processor 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, and an antenna 2. , Mobile communication module 150, wireless communication module 160, audio module 170, speaker 170A, receiver 170B, microphone 170C, earphone jack 170D, sensor module 180, buttons 190, motor 191, indicator 192, camera 193, display screen 194, and Subscriber identification module (subscriber identification module, SIM) card interface 195, etc. The sensor module 180 may include a pressure sensor 180A, a gyroscope sensor 180B, an air pressure sensor 180C, a magnetic sensor 180D, an acceleration sensor 180E, a distance sensor 180F, a proximity sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, and ambient light Sensor 180L, bone conduction sensor 180M, etc.

It can be understood that the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the electronic device 100. In other embodiments of the present application, the electronic device 100 may include more or fewer components than those shown in the figure, or combine certain components, or split certain components, or arrange different components. The illustrated components can be implemented in hardware, software, or a combination of software and hardware.

The processor 110 may include one or more processing units. For example, the processor 110 may include an application processor (AP), a modem processor, a graphics processing unit (GPU), and an image signal processor. (image signal processor, ISP), controller, video codec, digital signal processor (digital signal processor, DSP), baseband processor, and/or neural-network processing unit (NPU), etc. Among them, the different processing units may be independent devices or integrated in one or more processors.

The controller can generate operation control signals according to the instruction operation code and timing signals to complete the control of fetching instructions and executing instructions.

A memory may also be provided in the processor 110 for storing computer programs and data. In some embodiments, the memory in the processor 110 is a cache memory. The memory can store instructions or data that have just been used or recycled by the processor 110. If the processor 110 needs to use the instruction or data again, it can be directly called from the memory. Repeated accesses are avoided, the waiting time of the processor 110 is reduced, and the efficiency of the system is improved.

In some embodiments, the processor 110 may include one or more interfaces. The interface can include an integrated circuit (inter-integrated circuit, I2C) interface, an integrated circuit built-in audio (inter-integrated circuit sound, I2S) interface, a pulse code modulation (pulse code modulation, PCM) interface, and a universal asynchronous transmitter (universal asyncHRonous) interface. receiver/transmitter, UART) interface, mobile industry processor interface (MIPI), general-purpose input/output (GPIO) interface, subscriber identity module (SIM) interface, and / Or Universal Serial Bus (USB) interface, etc.

The I2C interface is a bidirectional synchronous serial bus, including a serial data line (SDA) and a serial clock line (SCL). In some embodiments, the processor 110 may include multiple sets of I2C buses. The processor 110 may be coupled to the touch sensor 180K, charger, flash, camera 193, etc., respectively through different I2C bus interfaces. For example, the processor 110 may couple the touch sensor 180K through an I2C interface, so that the processor 110 and the touch sensor 180K communicate through the I2C bus interface to implement the touch function of the electronic device 100.

The I2S interface can be used for audio communication. In some embodiments, the processor 110 may include multiple sets of I2S buses. The processor 110 may be coupled with the audio module 170 through an I2S bus to implement communication between the processor 110 and the audio module 170. In some embodiments, the audio module 170 may transmit audio signals to the wireless communication module 160 through an I2S interface, so as to realize the function of answering calls through a Bluetooth headset.

The PCM interface can also be used for audio communication to sample, quantize and encode analog signals. In some embodiments, the audio module 170 and the wireless communication module 160 may be coupled through a PCM bus interface. In some embodiments, the audio module 170 may also transmit audio signals to the wireless communication module 160 through the PCM interface, so as to realize the function of answering calls through the Bluetooth headset. Both the I2S interface and the PCM interface can be used for audio communication.

The UART interface is a universal serial data bus used for asynchronous communication. The bus can be a two-way communication bus. It converts the data to be transmitted between serial communication and parallel communication. In some embodiments, the UART interface is generally used to connect the processor 110 and the wireless communication module 160. For example, the processor 110 communicates with the Bluetooth module in the wireless communication module 160 through the UART interface to realize the Bluetooth function. In some embodiments, the audio module 170 may transmit audio signals to the wireless communication module 160 through a UART interface, so as to realize the function of playing music through a Bluetooth headset.

The MIPI interface can be used to connect the processor 110 with the display screen 194, the camera 193 and other peripheral devices. The MIPI interface includes a camera serial interface (camera serial interface, CSI), a display serial interface (display serial interface, DSI), and so on. In some embodiments, the processor 110 and the camera 193 communicate through a CSI interface to implement the shooting function of the electronic device 100. The processor 110 and the display screen 194 communicate through a DSI interface to realize the display function of the electronic device 100.

The GPIO interface can be configured through software. The GPIO interface can be configured as a control signal or as a data signal. In some embodiments, the GPIO interface can be used to connect the processor 110 with the camera 193, the display screen 194, the wireless communication module 160, the audio module 170, the sensor module 180, and so on. The GPIO interface can also be configured as an I2C interface, I2S interface, UART interface, MIPI interface, etc.

The USB interface 130 is an interface that complies with the USB standard specification, and specifically may be a Mini USB interface, a Micro USB interface, a USB Type C interface, and so on. The USB interface 130 can be used to connect a charger to charge the electronic device 100, and can also be used to transfer data between the electronic device 100 and peripheral devices. It can also be used to connect earphones and play audio through earphones. This interface can also be used to connect other electronic devices, such as AR devices.

It can be understood that the interface connection relationship between the modules illustrated in the embodiment of the present application is merely a schematic description, and does not constitute a structural limitation of the electronic device 100. In other embodiments of the present application, the electronic device 100 may also adopt different interface connection modes in the foregoing embodiments, or a combination of multiple interface connection modes.

The charging management module 140 is used to receive charging input from the charger. Among them, the charger can be a wireless charger or a wired charger. In some wired charging embodiments, the charging management module 140 may receive the charging input of the wired charger through the USB interface 130. In some embodiments of wireless charging, the charging management module 140 may receive the wireless charging input through the wireless charging coil of the electronic device 100. While the charging management module 140 charges the battery 142, it can also supply power to the electronic device 100 through the power management module 141.

The power management module 141 is used to connect the battery 142, the charging management module 140 and the processor 110. The power management module 141 receives input from the battery 142 and/or the charge management module 140, and supplies power to the processor 110, the internal memory 121, the display screen 194, the camera 193, and the wireless communication module 160. The power management module 141 can also be used to monitor parameters such as battery capacity, battery cycle times, and battery health status (leakage, impedance). In some other embodiments, the power management module 141 may also be provided in the processor 110. In other embodiments, the power management module 141 and the charging management module 140 may also be provided in the same device.

The wireless communication function of the electronic device 100 can be implemented by the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, the modem processor, and the baseband processor.

The antenna 1 and the antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in the electronic device 100 can be used to cover a single or multiple communication frequency bands. Different antennas can also be reused to improve antenna utilization. For example: Antenna 1 can be multiplexed as a diversity antenna of a wireless local area network. In other embodiments, the antenna can be used in combination with a tuning switch.

The mobile communication module 150 can provide a wireless communication solution including 2G/3G/4G/5G and the like applied to the electronic device 100. The mobile communication module 150 may include at least one filter, a switch, a power amplifier, a low noise amplifier (LNA), and the like. The mobile communication module 150 can receive electromagnetic waves by the antenna 1, and perform processing such as filtering, amplifying and transmitting the received electromagnetic waves to the modem processor for demodulation. The mobile communication module 150 can also amplify the signal modulated by the modem processor, and convert it into electromagnetic waves for radiation via the antenna 1. In some embodiments, at least part of the functional modules of the mobile communication module 150 may be provided in the processor 110. In some embodiments, at least part of the functional modules of the mobile communication module 150 and at least part of the modules of the processor 110 may be provided in the same device.

The modem processor may include a modulator and a demodulator. Among them, the modulator is used to modulate the low frequency baseband signal to be sent into a medium and high frequency signal. The demodulator is used to demodulate the received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then transmits the demodulated low-frequency baseband signal to the baseband processor for processing. After the low-frequency baseband signal is processed by the baseband processor, it is passed to the application processor. The application processor outputs a sound signal through an audio device (not limited to the speaker 170A, the receiver 170B, etc.), or displays an image or video through the display screen 194. In some embodiments, the modem processor may be an independent device. In other embodiments, the modem processor may be independent of the processor 110 and be provided in the same device as the mobile communication module 150 or other functional modules.

The wireless communication module 160 can provide applications on the electronic device 100 including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), bluetooth (BT), and global navigation satellites. System (gloBAl navigation satellite system, GNSS), frequency modulation (frequency modulation, FM), near field communication technology (near field communication, NFC), infrared technology (infrared, IR) and other wireless communication solutions. The wireless communication module 160 may be one or more devices integrating at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via the antenna 2, frequency modulates and filters the electromagnetic wave signals, and sends the processed signals to the processor 110. The wireless communication module 160 may also receive a signal to be sent from the processor 110, perform frequency modulation, amplify, and convert it into electromagnetic waves to radiate through the antenna 2.

In some embodiments, the antenna 1 of the electronic device 100 is coupled with the mobile communication module 150, and the antenna 2 is coupled with the wireless communication module 160, so that the electronic device 100 can communicate with the network and other devices through wireless communication technology. The wireless communication technology may include global system for mobile communications (GSM), general packet radio service (GPRS), code division multiple access (CDMA), broadband Code division multiple access (wideBAnd code division multiple access, WCDMA), time-division code division multiple access (TD-SCDMA), long term evolution (LTE), BT, GNSS, WLAN, NFC , FM, and/or IR technology, etc. The GNSS may include global satellite positioning system (gloBAl positioning system, GPS), global navigation satellite system (gloBAl navigation satellite system, GLONASS), Beidou navigation satellite system (BDS), quasi-zenith satellite system (quasi -zenith satellite system, QZSS) and/or satellite BAsed augmentation systems (SBAS).

The electronic device 100 implements a display function through a GPU, a display screen 194, an application processor, and the like. The GPU is an image processing microprocessor, which is connected to the display screen 194 and the application processor. The GPU is used to perform mathematical and geometric calculations and is used for graphics rendering. The processor 110 may include one or more GPUs that execute program instructions to generate or change display information.

The display screen 194 is used to display images, videos, and the like. The display screen 194 includes a display panel. The display panel can use liquid crystal display (LCD), organic light-emitting diode (OLED), active matrix organic light-emitting diode or active-matrix organic light-emitting diode (active-matrix organic light-emitting diode). AMOLED, flexible light-emitting diode (FLED), Miniled, MicroLed, Micro-oLed, quantum dot light-emitting diode (QLED), etc. In some embodiments, the electronic device 100 may include one or N display screens 194, and N is a positive integer greater than one. In this embodiment, the display screen 194 is a curved display screen or a foldable display screen.

The electronic device 100 can implement a shooting function through an ISP, a camera 193, a video codec, a GPU, a display screen 194, and an application processor.

The ISP is used to process the data fed back from the camera 193. For example, when taking a picture, the shutter is opened, the light is transmitted to the photosensitive element of the camera through the lens, the light signal is converted into an electrical signal, and the photosensitive element of the camera transmits the electrical signal to the ISP for processing and is converted into an image visible to the naked eye. ISP can also optimize the image noise, brightness, and skin color. ISP can also optimize the exposure, color temperature and other parameters of the shooting scene. In some embodiments, the ISP may be provided in the camera 193.

The camera 193 is used to capture still images or videos. The object generates an optical image through the lens and is projected to the photosensitive element. The photosensitive element may be a charge coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the optical signal into an electrical signal, and then transfers the electrical signal to the ISP to convert it into a digital image signal. ISP outputs digital image signals to DSP for processing. DSP converts digital image signals into standard RGB, YUV and other formats of image signals. In some embodiments, the electronic device 100 may include one or N cameras 193, and N is a positive integer greater than one.

Digital signal processors are used to process digital signals. In addition to digital image signals, they can also process other digital signals. For example, when the electronic device 100 selects the frequency point, the digital signal processor is used to perform Fourier transform on the energy of the frequency point.

Video codecs are used to compress or decompress digital video. The electronic device 100 may support one or more video codecs. In this way, the electronic device 100 can play or record videos in multiple encoding formats, such as: moving picture experts group (MPEG) 1, MPEG2, MPEG3, MPEG4, and so on.

NPU is a neural-network (NN) computing processor. By drawing on the structure of biological neural networks, for example, the transfer mode between human brain neurons, it can quickly process input information, and it can also continuously self-learn. Through the NPU, applications such as intelligent cognition of the electronic device 100 can be realized, such as image recognition, face recognition, voice recognition, text understanding, and so on.

The external memory interface 120 may be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the electronic device 100. The external memory card communicates with the processor 110 through the external memory interface 120 to realize the data storage function. For example, save music, video and other files in an external memory card.

The internal memory 121 may be used to store computer executable program code, where the executable program code includes instructions. The internal memory 121 may include a storage program area and a storage data area. Among them, the storage program area can store an operating system, an application program (such as a sound playback function, an image playback function, etc.) required by at least one function, and the like. The data storage area can store data (such as audio data, phone book, etc.) created during the use of the electronic device 100. In addition, the internal memory 121 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, a universal flash storage (UFS), and the like. The processor 110 executes various functional applications and data processing of the electronic device 100 by running instructions stored in the internal memory 121 and/or instructions stored in a memory provided in the processor.

The electronic device 100 can implement audio functions through the audio module 170, the speaker 170A, the receiver 170B, the microphone 170C, the earphone interface 170D, and the application processor. For example, music playback, recording, etc.

The audio module 170 is used to convert digital audio information into an analog audio signal for output, and is also used to convert an analog audio input into a digital audio signal. The audio module 170 can also be used to encode and decode audio signals. In some embodiments, the audio module 170 may be provided in the processor 110, or part of the functional modules of the audio module 170 may be provided in the processor 110.

The speaker 170A, also called "speaker", is used to convert audio electrical signals into sound signals. The electronic device 100 can listen to music through the speaker 170A, or listen to a hands-free call.

The receiver 170B, also called "earpiece", is used to convert audio electrical signals into sound signals. When the electronic device 100 answers a call or voice message, it can receive the voice by bringing the receiver 170B close to the human ear.

The microphone 170C, also called "microphone", "microphone", is used to convert sound signals into electrical signals. When making a call or sending a voice message, the user can make a sound by approaching the microphone 170C through the human mouth, and input the sound signal into the microphone 170C. The electronic device 100 may be provided with at least one microphone 170C. In other embodiments, the electronic device 100 may be provided with two microphones 170C, which can implement noise reduction functions in addition to collecting sound signals. In other embodiments, the electronic device 100 may also be provided with three, four or more microphones 170C to collect sound signals, reduce noise, identify sound sources, and realize directional recording functions.

The earphone interface 170D is used to connect wired earphones. The earphone interface 170D may be a USB interface 130, or a 3.5mm open mobile terminal platform (OMTP) standard interface, and a cellular telecommunications industry association of the USA (CTIA) standard interface.

The pressure sensor 180A is used to sense the pressure signal and can convert the pressure signal into an electrical signal. In some embodiments, the pressure sensor 180A may be provided on the display screen 194. There are many types of pressure sensors 180A, such as resistive pressure sensors, inductive pressure sensors, capacitive pressure sensors and so on. The capacitive pressure sensor may include at least two parallel plates with conductive materials. When a force is applied to the pressure sensor 180A, the capacitance between the electrodes changes. The electronic device 100 determines the intensity of the pressure according to the change in capacitance. When a touch operation acts on the display screen 194, the electronic device 100 detects the intensity of the touch operation according to the pressure sensor 180A. The electronic device 100 may also calculate the touched position according to the detection signal of the pressure sensor 180A. In some embodiments, touch operations that act on the same touch position but have different touch operation strengths may correspond to different operation instructions. For example: when a touch operation whose intensity of the touch operation is less than the first pressure threshold is applied to the short message application icon, an instruction to view the short message is executed. When a touch operation with a touch operation intensity greater than or equal to the first pressure threshold acts on the short message application icon, an instruction to create a new short message is executed.

The gyro sensor 180B may be used to determine the movement posture of the electronic device 100. In some embodiments, the angular velocity of the electronic device 100 around three axes (ie, x, y, and z axes) can be determined by the gyro sensor 180B. The gyro sensor 180B can be used for image stabilization. Exemplarily, when the shutter is pressed, the gyro sensor 180B detects the shake angle of the electronic device 100, calculates the distance that the lens module needs to compensate according to the angle, and allows the lens to counteract the shake of the electronic device 100 through reverse movement to achieve anti-shake. The gyro sensor 180B can also be used for navigation and somatosensory game scenes.

The air pressure sensor 180C is used to measure air pressure. In some embodiments, the electronic device 100 calculates the altitude based on the air pressure value measured by the air pressure sensor 180C to assist positioning and navigation.

The magnetic sensor 180D includes a Hall sensor. The electronic device 100 may use the magnetic sensor 180D to detect the opening and closing of the flip holster. In some embodiments, when the electronic device 100 is a flip machine, the electronic device 100 can detect the opening and closing of the flip according to the magnetic sensor 180D. Furthermore, according to the detected opening and closing state of the leather case or the opening and closing state of the flip cover, features such as automatic unlocking of the flip cover are set.

The acceleration sensor 180E can detect the magnitude of the acceleration of the electronic device 100 in various directions (generally three axes). When the electronic device 100 is stationary, the magnitude and direction of gravity can be detected. It can also be used to identify the posture of the electronic device 100, and be used in applications such as horizontal and vertical screen switching, pedometers, and the like.

Distance sensor 180F, used to measure distance. The electronic device 100 can measure the distance by infrared or laser. In some embodiments, when shooting a scene, the electronic device 100 may use the distance sensor 180F to measure the distance to achieve fast focusing.

The proximity light sensor 180G may include, for example, a light emitting diode (LED) and a light detector such as a photodiode. The light emitting diode may be an infrared light emitting diode. The electronic device 100 emits infrared light to the outside through the light emitting diode. The electronic device 100 uses a photodiode to detect infrared reflected light from nearby objects. When sufficient reflected light is detected, it can be determined that there is an object near the electronic device 100. When insufficient reflected light is detected, the electronic device 100 can determine that there is no object near the electronic device 100. The electronic device 100 can use the proximity light sensor 180G to detect that the user holds the electronic device 100 close to the ear to talk, so as to automatically turn off the screen to save power. The proximity light sensor 180G can also be used in leather case mode, and the pocket mode will automatically unlock and lock the screen.

The ambient light sensor 180L is used to sense the brightness of the ambient light. The electronic device 100 can adaptively adjust the brightness of the display screen 194 according to the perceived brightness of the ambient light. The ambient light sensor 180L can also be used to automatically adjust the white balance when taking pictures. The ambient light sensor 180L can also cooperate with the proximity light sensor 180G to detect whether the electronic device 100 is in the pocket to prevent accidental touch.

The fingerprint sensor 180H is used to collect fingerprints. The electronic device 100 can use the collected fingerprint characteristics to implement fingerprint unlocking, access application locks, fingerprint photographs, fingerprint answering calls, and so on.

The temperature sensor 180J is used to detect temperature. In some embodiments, the electronic device 100 uses the temperature detected by the temperature sensor 180J to execute a temperature processing strategy. For example, when the temperature reported by the temperature sensor 180J exceeds a threshold value, the electronic device 100 executes to reduce the performance of the processor located near the temperature sensor 180J, so as to reduce power consumption and implement thermal protection. In other embodiments, when the temperature is lower than another threshold, the electronic device 100 heats the battery 142 to avoid abnormal shutdown of the electronic device 100 due to low temperature. In some other embodiments, when the temperature is lower than another threshold, the electronic device 100 boosts the output voltage of the battery 142 to avoid abnormal shutdown caused by low temperature.

Touch sensor 180K, also called "touch panel". The touch sensor 180K may be disposed on the display screen 194, and the touch screen is composed of the touch sensor 180K and the display screen 194, which is also called a “touch screen”. The touch sensor 180K is used to detect touch operations acting on or near it. The touch sensor can pass the detected touch operation to the application processor to determine the type of touch event. The visual output related to the touch operation can be provided through the display screen 194. In other embodiments, the touch sensor 180K may also be disposed on the surface of the electronic device 100, which is different from the position of the display screen 194.

The bone conduction sensor 180M can acquire vibration signals. In some embodiments, the bone conduction sensor 180M can obtain the vibration signal of the vibrating bone mass of the human voice. The bone conduction sensor 180M can also contact the human pulse and receive the blood pressure pulse signal. In some embodiments, the bone conduction sensor 180M may also be provided in the earphone, combined with the bone conduction earphone. The audio module 170 can parse the voice signal based on the vibration signal of the vibrating bone block of the voice obtained by the bone conduction sensor 180M, and realize the voice function. The application processor can analyze the heart rate information based on the blood pressure beating signal obtained by the bone conduction sensor 180M, and realize the heart rate detection function.

The button 190 includes a power-on button, a volume button, and so on. The button 190 may be a mechanical button. It can also be a touch button. The electronic device 100 may receive key input, and generate key signal input related to user settings and function control of the electronic device 100.

The motor 191 can generate vibration prompts. The motor 191 can be used for incoming call vibration notification, and can also be used for touch vibration feedback. For example, touch operations applied to different applications (such as photographing, audio playback, etc.) can correspond to different vibration feedback effects. Acting on touch operations in different areas of the display screen 194, the motor 191 can also correspond to different vibration feedback effects. Different application scenarios (for example: time reminding, receiving information, alarm clock, games, etc.) can also correspond to different vibration feedback effects. The touch vibration feedback effect can also support customization.

The indicator 192 may be an indicator light, which may be used to indicate the charging status, power change, or to indicate messages, missed calls, notifications, and so on.

The SIM card interface 195 is used to connect to the SIM card. The SIM card can be inserted into the SIM card interface 195 or pulled out from the SIM card interface 195 to achieve contact and separation with the electronic device 100. The electronic device 100 may support 1 or N SIM card interfaces, and N is a positive integer greater than 1. The SIM card interface 195 can support Nano SIM cards, Micro SIM cards, SIM cards, etc. The same SIM card interface 195 can insert multiple cards at the same time. The types of the multiple cards can be the same or different. The SIM card interface 195 can also be compatible with different types of SIM cards. The SIM card interface 195 may also be compatible with external memory cards. The electronic device 100 interacts with the network through the SIM card to implement functions such as call and data communication. In some embodiments, the electronic device 100 adopts an eSIM, that is, an embedded SIM card. The eSIM card can be embedded in the electronic device 100 and cannot be separated from the electronic device 100.

The software system of the electronic device 100 may adopt a layered architecture, an event-driven architecture, a microkernel architecture, a microservice architecture, or a cloud architecture. The embodiment of the present invention takes an Android system with a layered architecture as an example to illustrate the software structure of the electronic device 100.

FIG. 2 is a block diagram of the software structure of the electronic device 100 according to an embodiment of the present invention.

The layered architecture divides the software into several layers, and each layer has a clear role and division of labor. Communication between layers through software interface. In some embodiments, the Android system is divided into four layers, from top to bottom, the application layer, the application framework layer, the Android runtime and system library, and the kernel layer.

The application layer can include a series of application packages.

As shown in Figure 2, the application package may include applications such as camera, gallery, calendar, call, map, navigation, WLAN, Bluetooth, music, video, short message, etc.

The application framework layer provides an application programming interface (application programming interface, API) and a programming framework for applications in the application layer. The application framework layer includes some predefined functions.

As shown in Figure 2, the application framework layer can include a window manager, a content provider, a view system, a phone manager, a resource manager, and a notification manager.

The window manager is used to manage window programs. The window manager can obtain the size of the display screen, determine whether there is a status bar, lock the screen, take a screenshot, etc.

The content provider is used to store and retrieve data and make these data accessible to applications. The data may include videos, images, audios, phone calls made and received, browsing history and bookmarks, phone book, etc.

The view system includes visual controls, such as controls that display text, controls that display pictures, and so on. The view system can be used to build applications. The display interface can be composed of one or more views. For example, a display interface that includes a short message notification icon may include a view that displays text and a view that displays pictures.

The phone manager is used to provide the communication function of the electronic device 100. For example, the management of the call status (including connecting, hanging up, etc.).

The resource manager provides various resources for the application, such as localized strings, icons, pictures, layout files, video files, and so on.

The notification manager enables the application to display notification information in the status bar, which can be used to convey notification-type messages, and it can automatically disappear after a short stay without user interaction. For example, the notification manager is used to notify download completion, message reminders, and so on. The notification manager can also be a notification that appears in the status bar at the top of the system in the form of a chart or a scroll bar text, such as a notification of an application running in the background, or a notification that appears on the screen in the form of a dialog window. For example, text messages are prompted in the status bar, prompt sounds, electronic devices vibrate, and indicator lights flash.

Android Runtime includes core libraries and virtual machines. Android runtime is responsible for the scheduling and management of the Android system.

The core library consists of two parts: one part is the function functions that the java language needs to call, and the other part is the core library of Android.

The application layer and application framework layer run in a virtual machine. The virtual machine executes the java files of the application layer and the application framework layer as binary files. The virtual machine is used to perform functions such as object life cycle management, stack management, thread management, security and exception management, and garbage collection.

The system library can include multiple functional modules. For example: surface manager (surface manager), media library (Media LiBraries), 3D graphics processing library (for example: OpenGL ES), 2D graphics engine (for example: SGL), etc.

The surface manager is used to manage the display subsystem and provides a combination of 2D and 3D layers for multiple applications.

The media library supports playback and recording of a variety of commonly used audio and video formats, as well as still image files. The media library can support multiple audio and video encoding formats, such as: MPEG4, H.264, MP3, AAC, AMR, JPG, PNG, etc.

The 3D graphics processing library is used to implement 3D graphics drawing, image rendering, synthesis, and layer processing.

The 2D graphics engine is a drawing engine for 2D drawing.

The kernel layer is the layer between hardware and software. The kernel layer contains at least display driver, camera driver, audio driver, and sensor driver.

In the following, the workflow of the software and hardware of the electronic device 100 will be exemplified in conjunction with capturing a photo scene.

When the touch sensor 180K receives a touch operation, the corresponding hardware interrupt is sent to the kernel layer. The kernel layer processes touch operations into original input events (including touch coordinates, time stamps of touch operations, etc.). The original input events are stored in the kernel layer. The application framework layer obtains the original input event from the kernel layer and identifies the control corresponding to the input event. Taking the touch operation as a touch click operation, and the control corresponding to the click operation is the control of the camera application icon as an example, the camera application calls the interface of the application framework layer to start the camera application, and then starts the camera driver by calling the kernel layer. The camera 193 captures still images or videos.

Exercise muscle fatigue refers to the inability of the body's physiological processes to continue its function at a certain level or the inability to maintain a predetermined exercise intensity. In the prior art, the detection methods of muscle fatigue after exercise mainly include: blood lactate detection method and surface electromyography (EMG) detection method.

Among them, the blood lactic acid detection method requires blood collection and cannot be accepted by most user groups, while the surface electromyography measurement method requires multiple electrodes to be attached, which is cumbersome to operate and is not suitable for users to measure by themselves. Based on the prior art, the present invention provides a A method for detecting muscle fatigue after exercise is convenient for users to measure by themselves and has high user acceptance, and does not require special testing equipment.

A method for detecting muscle fatigue after exercise. The method can be applied to a device for detecting muscle fatigue after exercise. The detecting device may be the above-mentioned electronic device 100 (such as a mobile phone) or a weight measuring device (such as a weight scale or Body fat scale or other equipment with weight measurement function), of course, can also be other electronic equipment, as shown in Figure 3, the method includes:

Step S11: Obtain the body state parameters of the user before exercise and the body state parameters after exercise.

Step S12: Calculate the user's muscle fatigue after exercise according to the user's body state parameters before exercise and the user's body state parameters after exercise.

It can be understood that the embodiment of the present invention obtains the user's body state parameters before exercise and post-exercise body state parameters, and then calculates the user's muscle fatigue after exercise according to the user's body state parameters before exercise and the user's body state parameters after exercise. , No need to collect the user's blood, simple operation, high user acceptance, and at the same time, the muscle fatigue detection can be achieved by relying on the weight measurement equipment, without the need for special testing equipment.

In specific implementation, step S11 and step S12 may be executed by a terminal device (for example, the electronic device 100), or may be executed by a weight measurement device (for example, a weight scale).

Among them, for the scenario executed by the terminal device (for example, the electronic device 100), it may specifically be: the terminal device is in communication connection with the weight measurement device, and the weight measurement device measures the pressure signal applied by the user before and after the exercise based on the stress test, The pressure signal is sent to the terminal device, and the terminal device separately determines the user's body state parameters before and after the exercise according to the pressure signal, thereby calculating the muscle fatigue and displaying it to the user.

Of course, in other implementations of this scenario, the weight measurement device can also process the pressure signal measured by itself to obtain the user's body state parameters before and after exercise, and then calculate the user's body state parameters before and after exercise. The body state parameters are sent to the terminal device, and the terminal device calculates the degree of muscle fatigue based on the body state parameters before and after exercise.

It should be noted that, in order to distinguish pre-exercise and post-exercise pressure signals, the embodiment of the present invention refers to the pre-exercise pressure signal as the "first pressure signal" and the post-exercise pressure signal as the "second pressure signal". ".

It should also be noted that the user's state is divided into pre-exercise, in-exercise and post-exercise. Among them, if the user performs weight measurement before exercise, if there are historical body state parameters before exercise (at rest), then The body state parameters obtained in this measurement and the body state parameters before exercise (at rest) can be weighted and averaged to obtain the new body state parameters before exercise (at rest). Of course, in some embodiments, the weighted average calculation may not be performed. When the user performs multiple measurements before exercise, the latest pre-exercise (resting state) body state parameter is directly used as the pre-exercise (resting state). Below) the physical state parameters. In one or more embodiments of the present invention, the state of the user can be determined as pre-exercise or post-exercise according to the state selected by the user during weight measurement (before exercise/after exercise).

Among them, for a scenario executed by a weight measurement device (such as a weight scale), it may specifically be: after the weight measurement device measures the first pressure signal applied by the user before exercise based on the stress test, it determines that the user is before exercise according to the first pressure signal The body state parameter of the body; the weight measurement device measures the second pressure signal applied by the user before exercise based on the stress test, and then determines the body state parameter of the user after exercise according to the second pressure signal, that is, the post-exercise muscle provided by the embodiment of the present invention Each step of the fatigue detection method is executed by the weight measurement equipment;

It can be understood that the detection method provided by the embodiment of the present invention is based on pressure detection, and there is no need to change the form of the hardware product.

Based on the foregoing, in an optional implementation manner, acquiring the user's physical state parameters before exercise includes:

Receiving the first pressure signal generated by the weight measuring device, and determining the user's body state parameter before exercise according to the first pressure signal, wherein the first pressure signal is generated by the weight measuring device before the user uses the weight measuring device to perform weight measurement;

Obtain the physical state parameters of the user after exercise, including:

The second pressure signal generated by the weight measurement device is received, and the body state parameter of the user after exercise is determined according to the second pressure signal, wherein the second pressure signal is generated by the weight measurement device when the user uses the weight measurement device to perform weight measurement after exercise.

Based on the foregoing, in another optional implementation manner, acquiring the user's physical state parameters before exercise includes:

Before the user exercises, measure the user's weight, generate a first pressure signal according to the pressure applied by the user, and determine the user's body state parameters before the exercise according to the first pressure signal;

Obtain the physical state parameters of the user after exercise, including:

After the user exercises, the weight of the user is measured, the first pressure signal is generated according to the pressure applied by the user, and the physical state parameters of the user after the exercise are determined according to the first pressure signal.

Based on the foregoing, in another optional implementation manner, the body state parameters of the user before exercise and the body state parameters after exercise generated by the weight measurement device are received, wherein the body state parameters of the user before exercise are determined by the weight measurement device. The measured first pressure signal is generated, and the body state parameters of the user after exercise are generated by the weight measurement device according to the measured second pressure signal.

In an optional implementation manner, the physical state parameters of the user before exercise include, but are not limited to: one or any more of body stability BA1, heart rate HR1, respiratory rate BR1, and respiratory intensity BI1.

In an optional implementation manner, the physical state parameters of the user after exercise include, but are not limited to, one or any more of body stability BA2, heart rate HR2, breathing rate BR2, and breathing intensity BI2.

In an optional implementation manner, receiving the first pressure signal generated by the weight measurement device further includes:

Obtain the motion parameters corresponding to the user's motion;

The user's heart recovery index RH and lung recovery index RL are calculated according to the user's body state parameters before exercise, the user's body state parameters after exercise, and the user's exercise parameters.

In an optional implementation manner, calculating the user's muscle fatigue after exercise according to the user's body state parameters before exercise and the user's body state parameters after exercise may include:

The user's muscle fatigue degree FM after exercise is calculated according to the user's body state parameters before exercise, the user's body state parameters after exercise, and the user's exercise parameters.

It can be understood that the embodiment of the present invention can calculate the user's muscle fatigue FM in combination with the user's exercise parameters. Of course, it is also possible to calculate the user's muscle fatigue FM without combining the user's exercise parameters. The embodiment of the present invention can also be combined with the user's exercise parameters. Calculate the user's heart recovery index RH and/or lung recovery index RL with the user's body state parameters before exercise and the user's body state parameters after exercise.

In an optional embodiment, the exercise parameter includes one or any more of exercise type s, exercise intensity m, exercise duration t, post-exercise time T, and exercise parameter credibility w.

Among them, in the embodiment of the present invention, the method for acquiring the exercise parameter may include: the exercise health App (Application, application) of the device that executes the method for detecting muscle fatigue after exercise in FIG. 3 or the exercise worn by the user Obtained from smart devices such as watches and sports bracelets.

In a specific implementation, when the user finishes an exercise (for example, running), the sports and health app of the corresponding device (for example, a mobile phone) can learn the related exercise parameters of the user's exercise, such as exercise type s, exercise intensity m, exercise duration t, One or more of the post-exercise time T, etc. When the user carries the device while exercising, the device can automatically obtain the user’s exercise type, exercise intensity, exercise duration, post-exercise duration, etc. according to the user’s actions, Of course, it can also be input by the user into a sports health app or a sports watch or a sports bracelet, and then the device that executes the method for detecting muscle fatigue after exercise in Figure 3 above obtains the relevant exercise parameters from the related sports health app. Among them, the specific implementation process of the sports health app that can learn the user's related sports parameters. The prior art has been widely used, and the embodiments of the present invention will not repeat them one by one.

Of course, in another specific implementation, the relevant exercise parameters can also be obtained through manual input by the user, or some exercise parameters are obtained from the sports health app, and some exercise parameters are obtained through manual input by the user. Of course, all exercise parameters or Part of the motion parameters may also be unnecessary. For example, all the motion parameters or some of the motion parameters may be empty, which is not specifically limited in the present invention.

Wherein, the motion parameter may also include the reliability w of the motion parameter. In the embodiment of the present invention, the sports parameter credibility w may be associated with the source and/or completeness and/or quantity of other sports parameters other than the sports parameter credibility w, for example, when the sports parameter is credible When other sports parameters other than the degree w are automatically obtained from sports health apps or watch bracelets, the sports parameters have the highest credibility and can be set to w=1; when the user manually enters the sports parameters, the credibility of the sports parameters is the second, yes. Set to w=0.5; when the other motion parameters except for the motion parameter credibility w are all empty, the motion parameter credibility is the lowest, which can be set to w=0. The determination method of the motion parameter credibility w can be based on actual conditions. Flexible settings are required.

In the embodiment of the present invention, the user’s muscle fatigue FM, heart recovery index RH, and lung recovery index after exercise are calculated according to the user’s body state parameters before exercise, the user’s body state parameter calculations after exercise, and/or the user’s exercise parameters. RL, methods for calculating the user's muscle fatigue FM, heart recovery index RH, and lung recovery index RL after exercise include but are not limited to methods such as weighted average algorithm and neural network.

In the embodiment of the present invention, after calculating the user’s muscle fatigue FM, and/or heart recovery index RH, lung recovery index RL, the present invention may further include: displaying the calculated user’s muscle fatigue FM, heart Any one or more of the recovery index RH and the lung recovery index RL, and any one or more of the user’s muscle fatigue, heart recovery index, and lung recovery index generate exercise volume assessment and/or physical function assessment and / Or exercise suggestion and/or body recovery suggestion and display the above exercise volume assessment and/or physical function assessment and/or exercise suggestion and/or body recovery suggestion to the user. The above specific implementation process will be described in detail below.

It can be understood that the embodiment of the present invention displays the detection result to the user, and provides exercise volume assessment and/or physical function assessment and/or exercise suggestion and/or physical recovery suggestion, which facilitates the user to understand the current physical state through human-computer interaction. At the same time, it provides users with guidance on exercise and physical recovery, which can prevent users from performing exercise training with unreasonable intensity and time and guide users to quickly restore physical functions.

As shown in Figure 4, the user's calculation of muscle fatigue, heart recovery index, and lung recovery index may specifically include:

Step (1): Before the user exercises (at rest), the weight measurement device (such as a weight scale, or a device with a weight detection function such as a body fat scale) detects the user’s pressure on the weight scale and generates a pressure signal (ie The above-mentioned first pressure signal), the weight measuring device or the terminal device calculates the body stability BA1, the heart rate HR1, the breathing rate BR1, and the breathing intensity BI1 according to the first pressure signal as the body state parameters of the user in the resting state;

Step (2): After the user exercises, the weight measurement device (for example, a weight scale, or a device with a weight detection function such as a body fat scale) detects the pressure of the user on the weight scale, and generates the above-mentioned pressure signal (that is, the above-mentioned second Pressure signal), the weight measuring device or terminal device calculates the body stability BA2, the heart rate HR2, the breathing rate BR2, and the breathing intensity BI2 according to the second pressure signal as the physical state parameters of the user after exercise;

Step (3): After the user exercises, the weight measuring device or terminal device obtains the user's exercise parameters, including exercise type s, exercise intensity m, exercise duration t, post-exercise time T, and exercise parameter credibility w. The acquisition method is preferably It can be automatically obtained from sports and health apps or corresponding watches, bracelets and other devices. It can also be entered manually by the user, or it can be empty. When the exercise parameters are automatically obtained from sports and health apps or watches, bracelets and other devices, the exercise parameters have the highest credibility, which can be set to w=1; when the user manually enters the exercise parameters, the credibility of the exercise parameters is second, which can be set w=0.5; when the motion parameter is empty, the reliability of the motion parameter is the lowest, which can be set to w=0.

Among them, step (3) can also be performed simultaneously with step (2), or step (3) is performed first, and then step (2) is performed.

Step (4): Calculate muscle fatigue FM, heart recovery index RH, and lung recovery index RL according to the user’s resting state and post-exercise physical state parameters, as well as exercise parameters. The calculation methods include but are not limited to weighted average algorithm or Neural network, etc.

Step (5): Display the calculated muscle fatigue, heart recovery index, lung recovery index, body stability, heart rate, breathing rate, and breathing intensity on the interface, and give an assessment of exercise volume and physical function, as well as exercise suggestions and Recovery suggestions, etc.

As shown in Figure 5, the process of detecting body state parameters of the user before exercise (at rest), the above process can be implemented by a weight measurement device or a terminal device, and the process includes:

Step S21: Collect the first pressure signal, which can be collected by the body weight measurement device;

Step S22: sequentially perform high-pass amplification and low-pass filtering processing on the first pressure signal;

Step S23: Perform low-pass filter processing on the first pressure signal after the high-pass amplification and low-pass filter processing, to obtain the body stability BA0 before exercise/resting state, and perform the high-pass amplification and low-pass filter processing on the The first pressure signal is subjected to high-pass processing to obtain the user's pre-exercise/rest heart rate HR0, respiratory rate BR0, and respiratory intensity BI0;

Step S24: Determine whether the body state parameters in the historical resting state are stored (including the body stability BA3 in the historical resting state, the heart rate HR3 in the historical resting state, the respiratory rate BR3 in the historical resting state, and the historical resting state). Any one or more of the breathing intensity BI3 in the rest state), if there is, the body state parameter obtained in step S23 and the corresponding body state parameter in the historical resting state are subjected to a weighted average operation to obtain a new The body state parameters of the user before exercise/resting state. If not, the body state parameters obtained in step S23 are used as the body state parameters of the user before exercise (resting state).

Specifically, when the historical resting state of body stability BA3 is stored, the user's body stability before exercise/resting state BA1=Wnew1*BA0+Wold1*BA3, where Wnew1 and Wold1 are weighted coefficients, and Wnew1 is equal to The sum of Wold1 is 1; when there is no historical resting state of body stability BA3 stored, BA1=BA0.

Specifically, when the historical resting state heart rate HR3 is stored, the user's heart rate before exercise/resting state HR1=Wnew2*HR0+Wold2*HR3, where Wnew2 and Wold2 are weighting coefficients, and the sum of Wnew2 and Wold2 It is 1; when there is no historical resting heart rate HR3 stored, HR1=HR0.

Specifically, when the historical resting state breathing rate BR3 is stored, the user's pre-exercise/resting state breathing rate BR1=Wnew3*BR0+Wold3*BR3, where Wnew3 and Wold3 are weighting coefficients, Wnew3 and Wold3 The sum is 1; when the body stability BR3 of the historical resting state is not stored, HR1=HR0.

Specifically, when the historical resting state breathing intensity BI3 is stored, the user's pre-exercise/rest breathing intensity BI1=Wnew4*BI0+Wold2*BI3, where Wnew4 and Wold4 are weighted coefficients, Wnew4 and Wold4 The sum is 1; when no historical resting state breathing intensity BI3 is stored, BI1=BI0.

Step S24: Store and display the body state parameters before exercise (at rest): body stability BA1, heart rate HR1, respiration rate BR1, respiration intensity BI1.

As shown in Figure 6, the process of detecting muscle fatigue FM, heart recovery index RH, and lung recovery index RL after the user exercises can be implemented by a weight measurement device or a terminal device, and the process includes:

Step S31: Obtain the user's exercise parameters, where the exercise parameters include one or more of exercise type s, exercise intensity m, exercise duration t, post-exercise duration T, and exercise parameter credibility w; as shown in FIG. 6 Before acquiring the user's motion parameters, the user's motion parameter acquisition method can be set.

In an optional implementation manner, the manner of obtaining the user's motion parameters may include:

Automatically obtain exercise parameters through sports health apps or corresponding watches, bracelets and other devices (exercise parameters can include exercise type s1, exercise intensity m1, exercise duration t1, post-exercise time T1, exercise parameter credibility w1);

Alternatively, the user can input the exercise parameter (the exercise parameter may include exercise type s2, exercise intensity m2, exercise duration t2, post-exercise time T2, exercise parameter credibility w2);

Or, some sports parameters are obtained from sports health apps or corresponding watches, bracelets and other devices, and some sports parameters are input by the user;

Of course, the exercise parameters may be unnecessary. When the exercise parameters cannot be obtained from the sports health app or corresponding watches, bracelets and other devices, and the user does not input the exercise parameters, the exercise parameters can be empty.

In specific implementation, it is preferable to automatically obtain sports parameters through sports and health apps or corresponding watches, bracelets and other devices. When the sports parameters cannot be obtained from sports and health apps or corresponding watches, bracelets and other devices, pass The user enters the motion parameter, and the user does not enter the motion parameter, the motion parameter is empty.

The user's input of the motion parameter may specifically be an input interface for the user to input the motion parameter displayed to the user, and the user performs input and/or selection operations on the input interface to realize the input of the motion parameter.

Step S32: Collect the second pressure signal, which can be collected by the body weight measurement device;

Step S33: sequentially perform high-pass amplification processing and low-pass filtering processing on the first pressure signal;

Step S34: Perform low-pass filter processing on the second pressure signal after the high-pass amplification processing and low-pass filter processing, to obtain the body stability BA2 before exercise (at rest), and after the high-pass amplification and low-pass filter processing Perform high-pass processing on the second pressure signal of the user to obtain the user's pre-exercise/rest heart rate HR2, respiratory rate BR2, and respiratory intensity BI2;

Step S35: Calculate the user's muscle fatigue FM, heart recovery index RH, and lung recovery index RL in combination with the body state parameters and exercise parameters of the user before exercise (at rest).

Step S36: Display the calculated muscle fatigue FM, heart recovery index RH, and lung recovery index RL. According to the calculated muscle fatigue FM, heart recovery index RH, and lung recovery index RL, provide exercise volume evaluation, physical function evaluation, and exercise Suggestions and suggestions for body recovery.

It should be noted that the above steps S31 to S36 are an exemplary implementation process, and some steps may be unnecessary or alternative. For example, the muscle fatigue degree FM, the heart recovery index RH and the heart recovery index RH obtained from the calculation in step S36 The lung recovery index RL gives exercise volume assessment, physical function assessment, exercise advice, and physical recovery advice. It may be unnecessary, or it can only give exercise volume assessment, physical function assessment, exercise advice, and body recovery advice. For example, only Exercise volume assessment and physical function assessment.

FIG. 7 is an exemplary schematic diagram of measuring a user's body state parameters before/after exercise according to an embodiment of the present invention.

As shown in Figure 7, taking signal 0 as the first pressure signal as an example, the determination of the user's body state parameters before exercise according to the first pressure signal includes:

Step S41: the first pressure signal (signal 0) is sequentially subjected to high-pass amplification processing and low-pass filtering processing to obtain the first signal (that is, signal 2 shown in FIG. 7);

Wherein, step S41 may further include: performing low-pass filtering processing on the first pressure signal (signal 0) to obtain weight information (that is, signal 1 shown in FIG. 7).

Step S42(a): The first signal (signal 2) is subjected to high-pass filtering to obtain the post-exercise cardiac shock signal BCG (signal 3), and the user’s heart rate HR1 before exercise is determined according to the post-exercise cardiac shock signal (signal 3) , Determine the waveform contour of the pre-exercise cardiac shock signal (signal 3) according to the waveform change of the pre-exercise cardiac shock signal (signal 3), and calculate the exercise based on the peak feature points of the pre-exercise cardiac shock signal (signal 3) waveform contour The pre-exercise respiration frequency BR1 and the pre-exercise respiration intensity BI1 are calculated based on the peak-to-peak value of the waveform profile of the pre-exercise cardiac shock signal (signal 3).

Step S42(b): Perform low-pass filtering on the first signal (ie signal 2 shown in FIG. 7) to obtain a second signal (ie signal 4 shown in FIG. 7), and calculate the second signal (signal 4) The main frequency, peak-to-peak value and standard deviation of the second signal (signal 4) are used to calculate the pre-exercise body stability BA1 according to the main frequency, peak-to-peak value and standard deviation of the second signal (signal 4). Among them, the calculation method of the body flat temperature BA1 includes but is not limited to multiple Linear regression.

Among them, step S42(a) and step S42(b) can be performed simultaneously or in a sequence. The embodiment of the present invention does not limit the sequence.

Continuing as shown in Figure 7, taking signal 0 as the second pressure signal as an example, the user’s body state parameters after exercise are determined according to the second pressure signal, including:

Step S51: perform high-pass amplification and low-pass filtering on the second pressure signal (signal 0) in order to obtain a third signal (that is, signal 2 shown in FIG. 7);

Wherein, step S51 may further include: performing low-pass filtering processing on the second pressure signal (signal 0) to obtain weight information (that is, signal 1 shown in FIG. 7).

Step S52(a): Perform high-pass filtering on the third signal (signal 2) to obtain the post-exercise cardiac shock signal BCG (signal 3), and determine the user’s post-exercise heart rate HR2 according to the post-exercise cardiac shock signal (signal 3) , Determine the waveform contour of the cardiac shock signal (signal 3) after exercise according to the waveform change of the cardiac shock signal (signal 3) after exercise, and calculate the movement based on the peak feature points of the waveform contour of the cardiac shock signal (signal 3) after exercise The post-exercise respiration frequency BR2 and the post-exercise respiration intensity BI2 are calculated based on the peak-to-peak value of the waveform profile of the post-exercise cardiac shock signal (signal 3).

Step S52(b): Perform low-pass filtering on the third signal (ie signal 2 shown in FIG. 7) to obtain a fourth signal (ie signal 4 shown in FIG. 7), and calculate the fourth signal (signal 4) The main frequency, peak-to-peak value and standard deviation of the fourth signal (signal 4) are used to calculate the post-exercise body stability BA2 according to the main frequency, peak-to-peak value and standard deviation of the fourth signal (signal 4). Among them, the calculation method of body flat temperature BA2 includes but is not limited to multiple Linear regression.

Among them, step S52(a) and step S52(b) can be performed simultaneously or in a sequence. The embodiment of the present invention does not limit the sequence.

It can be understood that the embodiment shown in FIG. 7 measures the pressure of the human body against it by a weight measuring device, generates a pressure signal, and processes the pressure signal through different high-pass, low-pass, and amplifying processes to analyze the weight, heart rate, respiratory frequency, and respiratory intensity. , Body stability and other signals, without additional sensors, only pressure signals can be used to obtain a variety of human physiological signals (that is, body state parameters).

In the embodiment of the present invention, the weight measurement device or the terminal device may use the following methods to calculate the user's muscle fatigue, heart recovery index, and lung recovery index.

Method 1: Through the weighted average algorithm.

In specific implementation, using a weighting algorithm to calculate the user's muscle fatigue FM may include:

Step S61: Calculate the physiological parameters before and after exercise by using the body stability before exercise BA1, the heart rate HR1, the respiratory frequency BR1, the respiratory intensity BI1, and the post exercise body stability BA2, the heart rate HR2, the respiratory frequency BR2, and the respiratory intensity BI2. Difference (body stability difference BA, heart rate difference HR, respiratory rate difference BR, and respiratory intensity difference BI).

Among them, the body stability difference BA=BA2-BA1, the heart rate difference HR=HR2-HR1, the respiratory rate difference BR=BR2-BR1, and the respiratory intensity difference BI=BI2-BI1.

Step S62: Using a weighted average algorithm, calculate the muscle fatigue FM based on the body stability difference BA, the heart rate difference HR, the respiratory rate difference BR, and the respiratory intensity difference BI.

Specifically, FM=a1*BA+a2*HR+a3*BR+a4*BI;

Among them, a1, a2, a3, and a4 are coefficients, which can be flexibly set according to actual needs.

In specific implementation, using a weighting algorithm to calculate the user's heart recovery index RH may include:

Step S71: Calculate the physiological parameters before and after exercise by using body stability BA1, heart rate HR1, breathing rate BR1, breathing intensity BI1, and body stability after exercise BA2, heart rate HR2, breathing frequency BR2, and breathing intensity BI2 Difference (body stability difference BA, heart rate difference HR, respiratory rate difference BR, and respiratory intensity difference BI).

Step S72: Using weighted average algorithm, according to body stability difference BA, heart rate difference HR, respiratory rate difference BR and respiratory intensity difference BI, combined with exercise type s1, exercise intensity m1, exercise duration t1, and post-exercise time T1, exercise parameter credibility w1 to calculate the heart recovery index RH.

Specifically, RH=b1*BA+b2*HR+b3*BR+b4*BI+b5*s1+b6*m1+b7*t1+b8*T1+b9*w1;

Among them, b1, b2, b3, b4, b5, b6, b7, b8, and b9 are coefficients, which can be flexibly set according to actual needs.

In a specific implementation, calculating the user's lung recovery index RL by using a weighting algorithm may include:

Step S81: Use the body stability before exercise BA1, heart rate HR1, breathing rate BR1, breathing intensity BI1, and the body stability after exercise BA2, heart rate HR2, breathing frequency BR2, and breathing intensity BI2 to calculate the physiological parameters before and after exercise. Difference (body stability difference BA, heart rate difference HR, respiratory rate difference BR, and respiratory intensity difference BI).

Step S82: Using weighted average algorithm, according to body stability difference BA, heart rate difference HR, respiratory rate difference BR and respiratory intensity difference BI, combined with exercise type s, exercise intensity m, exercise duration t, and post-exercise time T, exercise parameter credibility w, calculate the heart recovery index RL.

Specifically, RL=c1*BA+c2*HR+c3*BR+c4*BI+c5*s+c6*m+c7*t+c8*T+c9*w;

Among them, c1, c2, c3, c4, c5, c6, c7, c8, and c9 are coefficients, which can be flexibly set according to actual needs.

It can be understood that the embodiment of the present invention calculates the difference in physiological parameters such as body stability, heart rate, breathing frequency, and breathing intensity before and after exercise to characterize changes in physiological performance before and after exercise. Combined with exercise parameters, a weighted average algorithm is used to calculate the user’s post-exercise. The muscular fatigue, heart recovery ability and lung recovery ability are highly accurate and easy to calculate.

Method 2: Through neural network.

By constructing a neural network, and then training the constructed neural network, the trained neural network is obtained, and then the muscle fatigue FM, the heart recovery index RH and the heart recovery index RL are calculated according to the trained neural network.

Among them, the training set when training the neural network model may include the input training set and the input training set: where the input training set may include: the physiological parameter difference before and after the user exercise (body stability difference BA, heart rate difference HR , Breathing rate difference BR, breathing intensity difference BI) and exercise parameters (exercise type s, exercise intensity m, exercise duration t, post-exercise time T, exercise parameter credibility w); the output training set can include: muscle fatigue Degree FM, heart recovery capacity RH and lung recovery capacity RL, among which muscle fatigue FM can be measured by blood lactic acid method or electromyography method. The heart recovery capacity RH and the lung recovery capacity RL can be measured by an exercise testing machine.

By training the neural network, the calculation parameters of the neural network can be obtained. For new input data, the parameters in the neural network are used to calculate muscle fatigue FM, heart recovery capacity RH, and lung recovery capacity RL.

8 is a schematic diagram of calculating muscle fatigue FM, heart recovery index RH, and lung recovery index RL through neural network according to an embodiment of the present invention;

As shown in Figure 8, the input information of the neural network after training may include: the physiological parameter difference before and after the user exercise (body stability difference BA, heart rate difference HR, respiratory rate difference BR, respiratory intensity difference BI) And exercise parameters (exercise type s, exercise intensity m, exercise duration t, post-exercise time T, exercise parameter credibility w).

The trained neural network can calculate the muscle fatigue FM, the heart recovery index RH and the lung recovery index RL through the above input information and output them.

It can be understood that the embodiment of the present invention uses a neural network to calculate muscle fatigue FM, heart recovery index RH, and lung recovery index RL. With the development of sports health and wearable devices, the measurable physiological parameters of the human body will gradually increase. The network can effectively perform multi-parameter fusion to obtain deeper physiological parameters.

The interface interaction process of the device for detecting muscle fatigue after exercise provided by the embodiments of the present invention will be described below with reference to the accompanying drawings.

The user can select the state of the user through the state selection interface, whether it is the resting state before exercise or the state after exercise. If the user is in the resting state before exercise, they can select [An static state] in Figure 9, if the user is For the state after exercise, you can select [After exercise] in Figure 9.

The setting interface for automatic acquisition of exercise parameters is used to show the user whether to automatically acquire exercise parameters from the Sports Health App. The exercise parameters include exercise type s, exercise intensity m, exercise duration t, post-exercise time T, and exercise parameter credibility w. One or any number of.

If the user selects [Yes] in Fig. 10, the device for detecting muscle fatigue after exercise will be set to obtain exercise parameters from the Sports Health App.

If the muscle fatigue detection device does not detect the exercise data after exercise, the manual input selection interface as shown in Figure 11 can be shown to the user. If the user selects [No] in Figure 10 or [Yes] in Figure 11, Then the user can be shown the exercise parameter input interface shown in Figure 12, through which the user can manually input or set the exercise parameters, etc., specifically allowing the user to select or input the exercise type s, exercise intensity m and/or exercise duration t, Post-exercise time (exercise end time) T.

Specifically, a drop-down menu can be used to allow the user to select the type of exercise and the time after exercise (exercise end time) T, such as running, swimming, squatting, basketball, etc. When the user selects the type of exercise, according to the user’s The selected exercise type allows the user to select or input the exercise intensity/duration of the exercise type selected by the user. For example, if the type of exercise selected by the user is running/swimming, expand the menu for the user to select or set the length of running time. If the exercise type selected by the user is squat, the menu for the user to select or set the number of squats is expanded. For details, please refer to Figure 12 and Figure 13. Figure 12 and Figure 13 provide schematic diagrams of two exercise parameter input interfaces.

As shown in Figure 14, the measurement result display interface shows the user: the user’s weight, heart rate HR2, heart recovery index RH, breathing rate BR2, breathing intensity BI2, lung recovery index RH, body stability BA2, muscle fatigue FM, and Physical recovery and exercise recommendations.

Of course, FIG. 14 is only an exemplary embodiment. In other embodiments, it may only display the user's weight, heart rate HR2, heart recovery index RH, respiratory rate BR2, respiratory intensity BI2, lung recovery index RH, and body stability. The degree of BA2, the degree of muscle fatigue FM, and some of the information in the body recovery and exercise recommendations or other information may also be shown to the user.

It can be understood that the embodiment of the present invention displays the detection results to the user, facilitates the user to understand the current physical state through human-computer interaction, and provides the user with guidance on exercise and physical recovery, which can prevent the user from exercising with unreasonable intensity and time. Train and guide users to quickly restore physical functions.

The embodiment of the application also discloses a device for detecting muscle fatigue after exercise. The device for detecting muscle fatigue after exercise may be the above body weight measurement device or the above terminal device. It should be understood that the device 400 can Perform each step in the method for detecting muscle fatigue after exercise, in order to avoid repetition, it will not be described in detail here. As shown in FIG. 14, the device 400 includes: a first acquisition module 410 and a first calculation module 420.

The first acquiring module 410 is used to acquire the body state parameters of the user before exercise and the body state parameters after exercise, where the body state parameters include body stability, heart rate, breathing rate, and breathing intensity; and

The first calculation module 420 is configured to calculate the user's muscle fatigue after exercise according to the user's body state parameters before exercise and the user's body state parameters after exercise.

In a feasible implementation manner, the first obtaining module 410 may include:

The first acquisition unit is configured to receive the first pressure signal generated by the weight measurement device, and determine the user's body state parameter before exercise according to the first pressure signal, wherein the first pressure signal is used by the weight measurement device to measure the weight of the user before the exercise Generated when the device is taking weight measurements; and

The second acquisition unit is configured to receive a second pressure signal generated by the weight measurement device, and determine the body state parameter of the user after exercise according to the second pressure signal, wherein the second pressure signal is used by the weight measurement device after the user exercises the weight measurement device Generated when taking a weight measurement.

The first determining unit is configured to measure the weight of the user before the user exercises, generate a first pressure signal according to the pressure applied by the user, and determine the physical state parameters of the user before the exercise according to the first pressure signal; and

The second determining unit is configured to measure the weight of the user after the user exercises, generate a second pressure signal according to the pressure applied by the user, and determine the body state parameter of the user after the exercise according to the second pressure signal.

In a feasible implementation manner, the apparatus 400 may further include:

The second acquiring module is used to acquire the motion parameters corresponding to the user's motion; and

The second calculation module is used to calculate the user's heart recovery index and lung recovery index according to the user's body state parameters before exercise, the user's body state parameters after exercise, and the user's exercise parameters.

In a feasible implementation manner, the first calculation module 410 may include:

The first calculation unit is used to calculate the user's muscle fatigue after exercise according to the user's body state parameters before exercise, the user's body state parameters after exercise, and the user's exercise parameters.

In a feasible implementation manner, the exercise parameter includes one or more of exercise type, exercise intensity, exercise duration, post-exercise duration, and exercise parameter credibility.

In a feasible implementation manner, the first acquiring module or the first determining module may include:

The first processing unit is used to sequentially perform high-pass amplification and low-pass filtering on the first pressure signal to obtain the first signal, perform high-pass filtering on the first signal to obtain the pre-exercise cardiac shock signal, and according to the pre-exercise cardiac The shock signal determines the heart rate before exercise, the waveform profile of the pre-exercise cardiac shock signal is determined according to the waveform change of the pre-exercise cardiac shock signal, and the pre-exercise respiration frequency is calculated according to the peak feature points of the waveform profile of the pre-exercise cardiac shock signal and Calculate the pre-exercise respiration intensity based on the peak-to-peak value of the waveform profile of the pre-exercise cardiac shock signal; and

The second processing unit is used to perform low-pass filter processing on the first signal to obtain the second signal, calculate the dominant frequency, peak-to-peak value and standard deviation of the second signal, according to the dominant frequency, peak-to-peak value and standard deviation of the second signal Calculate the body stability before exercise.

In a feasible implementation manner, the second acquiring module or the second determining module may include:

The third processing unit is used to sequentially perform high-pass amplification processing and low-pass filter processing on the second pressure signal to obtain a third signal, perform high-pass filter processing on the third signal to obtain a post-exercise cardiac shock signal, according to the post-exercise heart The shock signal determines the heart rate after exercise, the waveform profile of the cardiac shock signal after exercise is determined according to the waveform change of the cardiac shock signal after exercise, and the respiration frequency after exercise is calculated according to the peak feature points of the waveform contour of the cardiac shock signal after exercise. Calculate the post-exercise respiration intensity based on the peak-to-peak value of the waveform profile of the post-exercise cardiac shock signal; and

The fourth processing unit is used to perform low-pass filter processing on the third signal to obtain the fourth signal, calculate the dominant frequency, peak-to-peak value and standard deviation of the fourth signal, according to the dominant frequency, peak-to-peak value and standard deviation of the fourth signal Calculate the body stability after exercise.

In a feasible implementation manner, the first calculation module 420 may include:

The second calculation unit is used to calculate the body stability difference between the user's body stability before exercise and the user's body stability after exercise, and to calculate the heart rate difference between the user's heart rate before exercise and the user's heart rate after exercise Calculate the difference between the user's breathing rate before exercise and the user's breathing rate after exercise, and calculate the difference between the user's breathing intensity before exercise and the user's breathing intensity after exercise; and

The third calculation unit is used to calculate the user's muscle fatigue after exercise by using a weighted average algorithm based on the difference in body stability, the difference in heart rate, the difference in respiratory frequency, and the difference in respiratory intensity.

In a feasible implementation manner, the second calculation unit may include:

The first calculation subunit is used to calculate the heart recovery index and lungs of the user after exercise by using the weighted average algorithm according to the difference in body stability, heart rate, respiratory rate, and respiratory intensity, and combined with the user's exercise parameters. Recovery index.

In a feasible implementation manner, the first calculation unit may include:

The second calculation subunit is used to calculate the body stability difference between the user's body stability before exercise and the user's body stability after exercise, and to calculate the heart rate between the user's heart rate before exercise and the user's heart rate after exercise Difference, and calculating the difference between the user's respiration rate before exercise and the user’s respiration rate after exercise, and calculating the difference between the user’s respiration intensity before exercise and the user’s respiration intensity after exercise; and

The third calculation subunit is used to calculate the user’s muscle fatigue, heart recovery index, and lungs based on the difference in body stability, heart rate, respiratory rate, and respiratory intensity, combined with the user’s exercise parameters, and the neural network. Recovery index.

In a feasible implementation manner, the apparatus 400 may further include:

The first display module is used to display any one or more of the calculated user’s muscle fatigue, heart recovery index, and lung recovery index, and according to the user’s muscle fatigue, heart recovery index, and lung recovery index. One or any number of generating and displaying one or any number of exercise volume assessment, physical function assessment, exercise suggestion, and physical recovery suggestion.

This application also provides an electronic device. As shown in FIG. 1, the electronic device 100 includes a memory 121 and a processor 110. The memory 121 stores a computer program, the processor 110 is connected to the memory 121, and the processor 110 executes the computer program to Realize the above-mentioned method for detecting muscle fatigue after exercise.

This application also provides a body weight measurement device, which includes a memory and a processor, and a computer program is stored in the memory. The processor is connected to the memory, and the processor executes the computer program to realize the detection of muscle fatigue after exercise as described above. method.

This application also provides a computer storage medium, including computer instructions. When the computer instructions run on the electronic device 100 or the weight measurement device, the electronic device 100 or the weight measurement device executes the above-mentioned method for detecting muscle fatigue after exercise. The various steps in.

The present application also provides a computer program product. When the computer program product runs on a computer, when the computer program product runs on the computer, the computer executes the steps in the above-mentioned post-exercise muscle fatigue detection method.

Those skilled in the art can clearly understand that, for the convenience and conciseness of description, the specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiment, which will not be repeated here.

The above are only specific implementations of this application. Any person skilled in the art can easily conceive of changes or replacements within the technical scope disclosed in this application, which should be covered by the protection scope of this application. The protection scope of this application shall be subject to the protection scope of the claims.

Claims

A method for detecting muscle fatigue after exercise, characterized in that the method comprises:

Acquiring the body state parameters of the user before exercise and the body state parameters after exercise, where the body state parameters include body stability, heart rate, breathing frequency, and breathing intensity;

The muscle fatigue degree of the user after exercise is calculated according to the body state parameter of the user before exercise and the body state parameter of the user after exercise.
The method according to claim 1, wherein said obtaining the physical state parameters of the user before exercise comprises:

Receive a first pressure signal generated by a weight measurement device, and determine the physical state parameter of the user before exercise according to the first pressure signal, wherein the first pressure signal is used by the weight measurement device before the user exercises Said weight measurement device is generated when the weight measurement is performed;

The acquiring the physical state parameters of the user after exercise includes:

Receive a second pressure signal generated by the weight measurement device, and determine the body state parameter of the user after exercise according to the second pressure signal, wherein the second pressure signal is used by the weight measurement device after the user exercises The weight measurement device is generated when the weight measurement is performed.
The method according to claim 1, wherein said obtaining the physical state parameters of the user before exercise comprises:

Before the user exercises, measure the weight of the user, generate a first pressure signal according to the pressure applied by the user, and determine the physical state parameters of the user before the exercise according to the first pressure signal;

The acquiring the physical state parameters of the user after exercise includes:

After the user exercises, measure the weight of the user, generate a second pressure signal according to the pressure applied by the user, and determine the body state parameter of the user after the exercise according to the second pressure signal.
The method according to claim 1, wherein the method further comprises:

Acquiring the motion parameter corresponding to the user motion;

The heart recovery index and the lung recovery index of the user are calculated according to the body state parameters of the user before exercise, the body state parameters of the user after exercise, and the exercise parameters of the user.
The method according to claim 4, wherein the calculating the muscle fatigue of the user after exercise according to the physical state parameters of the user before exercise and the physical state parameters of the user after exercise comprises:

The muscle fatigue degree of the user after exercise is calculated according to the physical state parameters of the user before exercise, the physical state parameters of the user after exercise, and the exercise parameters of the user.
The method according to claim 4 or 5, wherein the exercise parameter includes one or any more of exercise type, exercise intensity, exercise duration, post-exercise duration, and exercise parameter credibility.
The method according to claim 2 or 3, wherein the determining the physical state parameters of the user before exercise according to the first pressure signal comprises:

The first pressure signal is sequentially subjected to high-pass amplification processing and low-pass filtering processing to obtain a first signal, and the first signal is subjected to high-pass filtering processing to obtain a pre-exercise cardiac shock signal, according to the pre-exercise cardiac shock The signal determines the heart rate before exercise, the waveform profile of the cardiac shock signal before exercise is determined according to the waveform change of the cardiac shock signal before exercise, and the exercise is calculated according to the peak feature points of the waveform contour of the cardiac shock signal before exercise The pre-exercise respiration frequency and the pre-exercise respiration intensity calculated according to the peak-to-peak value of the waveform profile of the pre-exercise cardiac shock signal;

Perform low-pass filtering on the first signal to obtain a second signal, calculate the dominant frequency, peak-to-peak value and standard deviation of the second signal, and calculate based on the dominant frequency, peak-to-peak value and standard deviation of the second signal Body stability before exercise.
The method according to claim 2 or 3, wherein the determining the physical state parameters of the user after exercise according to the second pressure signal comprises:

The second pressure signal is sequentially subjected to high-pass amplification processing and low-pass filtering processing to obtain a third signal, and the third signal is subjected to high-pass filtering processing to obtain a post-exercise cardiac shock signal, according to the post-exercise cardiac shock The signal determines the heart rate after exercise, the waveform contour of the post-exercise cardiac shock signal is determined according to the waveform change of the post-exercise cardiac shock signal, and the exercise is calculated according to the peak feature points of the waveform contour of the post-exercise cardiac shock signal The post-exercise respiration frequency and the post-exercise respiration intensity calculated according to the peak-to-peak value of the waveform profile of the post-exercise cardiac shock signal;

Perform low-pass filtering on the third signal to obtain a fourth signal, calculate the dominant frequency, peak-to-peak value and standard deviation of the fourth signal, and calculate based on the dominant frequency, peak-to-peak value and standard deviation of the fourth signal Body stability after exercise.
The method according to claim 4, wherein the calculating the muscle fatigue of the user after exercise according to the physical state parameters of the user before exercise and the physical state parameters of the user after exercise comprises:

Calculate the body stability difference between the body stability of the user before exercise and the body stability of the user after exercise, and calculate the heart rate between the heart rate of the user before exercise and the heart rate of the user after exercise Difference, and calculating the difference between the breathing frequency of the user before exercise and the breathing frequency of the user after exercise, and calculating the difference between the breathing intensity of the user before exercise and the breathing intensity of the user after exercise Difference in respiratory intensity between;

According to the difference in body stability, the difference in heart rate, the difference in respiratory frequency, and the difference in respiratory intensity, a weighted average algorithm is used to calculate the muscle fatigue of the user after exercise.
The method according to claim 9, characterized in that the heart recovery of the user after exercise is calculated according to the physical state parameters of the user before exercise, the physical state parameters of the user after exercise, and the exercise parameters of the user Index and lung recovery index, including:

According to the body stability difference, the heart rate difference, the respiratory frequency difference, and the respiratory intensity difference, combined with the user's exercise parameters, a weighted average algorithm is used to calculate the user's heart after exercise Recovery index and lung recovery index.
The method according to claim 5, wherein the calculation of the user’s muscles is performed based on the user’s physical state parameters before exercise, the user’s physical state parameter calculations after exercise, and the user’s exercise parameters. Fatigue, heart recovery index and lung recovery index, including:

Calculate the body stability difference between the body stability of the user before exercise and the body stability of the user after exercise, and calculate the heart rate between the heart rate of the user before exercise and the heart rate of the user after exercise Difference, and calculating the difference between the breathing frequency of the user before exercise and the breathing frequency of the user after exercise, and calculating the difference between the breathing intensity of the user before exercise and the breathing intensity of the user after exercise Difference in respiratory intensity between;

According to the body stability difference, the heart rate difference, the breathing rate difference, and the breathing intensity difference, combined with the user's exercise parameters, a neural network is used to calculate the user's muscle fatigue and heart rate. Recovery index and lung recovery index.
The method according to claim 4 or 5, wherein the method further comprises:

Display any one or any of the calculated muscle fatigue, heart recovery index, and lung recovery index of the user, and one or any of the user’s muscle fatigue, heart recovery index, and lung recovery index Multiple generations and displays one or more of exercise volume evaluation, physical function evaluation, exercise suggestion, and physical recovery suggestion.
A device for detecting muscle fatigue after exercise, characterized in that the device comprises:

The first acquisition module is used to acquire the body state parameters of the user before exercise and the body state parameters after exercise, wherein the body state parameters include body stability, heart rate, breathing rate, and breathing intensity; and

The first calculation module is configured to calculate the muscle fatigue of the user after exercise according to the physical state parameters of the user before exercise and the physical state parameters of the user after exercise.
The device for detecting muscle fatigue after exercise according to claim 13, wherein the first acquiring module comprises:

The first acquisition unit is configured to receive the first pressure signal generated by the weight measurement device, and determine the physical state parameter of the user before exercise according to the first pressure signal, wherein the first pressure signal is used by the weight measurement device Generated when the user uses the weight measurement device to perform weight measurement before the user exercises; and

The second acquisition unit is configured to receive a second pressure signal generated by the weight measurement device, and determine the body state parameter of the user after exercise according to the second pressure signal, wherein the second pressure signal is generated by the weight measurement device It is generated when the user uses the weight measurement device to perform weight measurement after the user exercises.
The device for detecting muscle fatigue after exercise according to claim 13, wherein the first acquiring module comprises:

The first determining unit is configured to measure the weight of the user before the user exercises, generate a first pressure signal according to the pressure applied by the user, and determine according to the first pressure signal that the user is before the exercise Physical state parameters; and

The second determining unit is configured to measure the weight of the user after the user exercises, generate a second pressure signal according to the pressure applied by the user, and determine the user after the exercise according to the second pressure signal Physical state parameters.
The device for detecting muscle fatigue after exercise according to claim 13, wherein the device further comprises:

The second acquisition module is used to acquire the motion parameters corresponding to the user motion; and

The second calculation module is configured to calculate the heart recovery index and lung recovery index of the user according to the physical state parameters of the user before exercise, the physical state parameters of the user after exercise, and the exercise parameters of the user.
The device for detecting muscle fatigue after exercise according to claim 14, wherein the first calculation module comprises:

The first calculation unit is configured to calculate the muscle fatigue of the user after exercise according to the physical state parameters of the user before exercise, the physical state parameters of the user after exercise, and the exercise parameters of the user.
The device for detecting muscle fatigue after exercise according to claim 16 or 17, wherein the exercise parameter includes one of exercise type, exercise intensity, exercise duration, post exercise duration, and exercise parameter credibility, or Any variety.
The device for detecting muscle fatigue after exercise according to claim 14 or 15, wherein the first acquiring module or the first determining module comprises:

The first processing unit is configured to sequentially perform high-pass amplification processing and low-pass filtering processing on the first pressure signal to obtain a first signal, perform high-pass filtering processing on the first signal to obtain a pre-exercise cardiac shock signal, according to The cardiac shock signal before exercise determines the heart rate before exercise, the waveform profile of the cardiac shock signal before exercise is determined according to the waveform change of the cardiac shock signal before exercise, and the waveform of the cardiac shock signal before exercise is determined. Calculating the pre-exercise respiration frequency with the peak feature points of the contour and calculating the pre-exercise respiration intensity according to the peak-to-peak value of the waveform contour of the pre-exercise cardiac shock signal; and

The second processing unit is configured to perform low-pass filter processing on the first signal to obtain a second signal, calculate the dominant frequency, peak-to-peak value, and standard deviation of the second signal, according to the dominant frequency of the second signal , Peak-to-peak value and standard deviation to calculate the body stability before exercise.
The device for detecting muscle fatigue after exercise according to claim 14 or 15, wherein the second acquiring module or the second determining module comprises:

The third processing unit is configured to sequentially perform high-pass amplification processing and low-pass filter processing on the second pressure signal to obtain a third signal, perform high-pass filter processing on the third signal to obtain a post-exercise cardiac shock signal, according to The post-exercise cardiac shock signal determines the post-exercise heart rate, the waveform profile of the post-exercise cardiac shock signal is determined according to the waveform change of the post-exercise cardiac shock signal, and the waveform profile of the post-exercise cardiac shock signal is determined according to the waveform of the post-exercise cardiac shock signal The peak feature points of the contour calculate the post-exercise respiration frequency, and the post-exercise respiration intensity is calculated according to the peak-to-peak value of the waveform contour of the post-exercise cardiac shock signal; and

The fourth processing unit is configured to perform low-pass filtering processing on the third signal to obtain a fourth signal, calculate the main frequency, peak-to-peak value and standard deviation of the fourth signal, and calculate the main frequency of the fourth signal according to the main frequency of the fourth signal. , Peak-to-peak value and standard deviation to calculate the body stability after exercise.
The device for detecting muscle fatigue after exercise according to claim 16, wherein the first calculation module comprises:

The second calculation unit is used to calculate the body stability difference between the body stability of the user before exercise and the body stability of the user after exercise, and to calculate the heart rate of the user before exercise and the user exercise The heart rate difference between the heart rates after the exercise, and the respiration rate difference between the respiration rate of the user before exercise and the respiration rate of the user after exercise, and the calculation of the respiration intensity of the user before exercise and the The difference in breathing intensity between the breathing intensity of the user after exercise; and

The third calculation unit is configured to calculate the muscle fatigue degree of the user after exercise by using a weighted average algorithm according to the difference in body stability, the difference in heart rate, the difference in respiratory frequency, and the difference in respiratory intensity.
22. The device for detecting muscle fatigue after exercise according to claim 21, wherein the second calculation unit comprises:

The first calculation subunit is configured to adopt a weighted average algorithm according to the difference in body stability, the difference in heart rate, the difference in respiratory frequency, and the difference in respiratory intensity, in combination with the exercise parameters of the user Calculate the heart recovery index and lung recovery index of the user after exercise.
The device for detecting muscle fatigue after exercise according to claim 17, wherein the first calculation unit comprises:

The second calculation subunit is used to calculate the difference in body stability between the body stability of the user before exercise and the body stability of the user after exercise, and to calculate the difference between the user’s heart rate before exercise and the user’s The heart rate difference between the heart rates after exercise, and the respiration rate difference between the respiration rate of the user before exercise and the respiration rate of the user after exercise, and the calculation of the respiration intensity of the user before exercise and the total The breathing intensity difference between the breathing intensity of the user after exercise; and

The third calculation subunit is used to calculate the difference in body stability, the difference in heart rate, the difference in respiration rate, and the difference in respiration intensity, in combination with the exercise parameters of the user, using a neural network to calculate Describes the user’s muscle fatigue, heart recovery index and lung recovery index.
The device for detecting muscle fatigue after exercise according to claim 15 or 17, wherein the device further comprises:

The first display module is used to display any one or more of the calculated muscle fatigue, heart recovery index, and lung recovery index of the user, and according to the user’s muscle fatigue, heart recovery index, and lung recovery index. One or any more of the recovery index generates and displays one or any more of exercise volume evaluation, physical function evaluation, exercise suggestion, and physical recovery suggestion.
An electronic device, characterized in that the electronic device includes a memory and a processor, a computer program is stored in the memory, the processor is connected to the memory, and the processor executes the computer program to achieve The method for detecting muscle fatigue after exercise according to any one of 1 to 12.
A weight measurement device, wherein the weight measurement device includes a memory and a processor, the memory stores a computer program, the processor is connected to the memory, and the processor executes the computer program to implement The method for detecting muscle fatigue after exercise according to any one of claims 1 to 12.
A computer storage medium, characterized by comprising computer instructions, which when the computer instructions run on an electronic device, cause the electronic device to perform the post-exercise muscle fatigue according to any one of claims 1-12 The steps of the detection method.
A computer program product, characterized in that, when the computer program product runs on a computer, the computer is caused to execute the steps of the method for detecting post-exercise muscle fatigue according to any one of claims 1-12 .