CN113693556A

CN113693556A - Method and device for detecting muscle fatigue degree after exercise and electronic equipment

Info

Publication number: CN113693556A
Application number: CN202010430716.0A
Authority: CN
Inventors: 赵帅; 杨斌; 任慧超; 李玥
Original assignee: Huawei Technologies Co Ltd
Current assignee: Huawei Technologies Co Ltd
Priority date: 2020-05-20
Filing date: 2020-05-20
Publication date: 2021-11-26
Also published as: WO2021233018A1

Abstract

The application provides a method and a device for detecting muscle fatigue after exercise and electronic equipment, wherein the method for detecting muscle fatigue after exercise comprises the following steps: acquiring body state parameters of a user before movement and body state parameters of the user after movement, wherein the body state parameters comprise body smoothness, heart rate, respiratory rate and respiratory intensity; and calculating the muscle fatigue degree of the user after the movement according to the body state parameters of the user before the movement and the body state parameters of the user after the movement. The detection method can facilitate the user to measure by himself and has high user acceptance.

Description

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

Technical Field

The application relates to the technical field of intelligent detection equipment, in particular to a method and a device for detecting muscle fatigue after exercise and electronic equipment.

Background

The muscle fatigue after exercise is an important index for evaluating exercise intensity and body recovery function after human body exercise, and the current main measuring methods comprise a blood lactic acid method and a surface electromyography. The blood lactic acid method is used for detecting muscle fatigue by using blood lactic acid, has high accuracy, but needs to collect blood and cannot be accepted by most groups; the surface electromyography utilizes surface electromyogram signals (Emg, Electroyogram) for evaluation, muscle fatigue can be measured according to muscle mass, but electrodes are required to be attached to different muscle masses, the operation is complicated, and the surface electromyography is not suitable for self measurement of common users. In order to detect the muscle fatigue degree after exercise and thus evaluate the exercise intensity for the user, it is highly desirable to provide a muscle fatigue degree detection scheme which is convenient to detect and acceptable to the user.

Disclosure of Invention

The embodiment of the application provides a method and a device for detecting muscle fatigue after exercise, an electronic device, a weight measuring device, a computer storage medium and a computer program product, which can detect the muscle fatigue of a user after exercise, can facilitate the user to measure the muscle fatigue after exercise by himself, and have high user acceptance.

In a 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 body state parameters of a user before movement and body state parameters of the user after movement, wherein the body state parameters comprise body smoothness, heart rate, respiratory rate and respiratory intensity;

and calculating the muscle fatigue degree of the user after the movement according to the body state parameters of the user before the movement and the body state parameters of the user after the movement.

With reference to the first aspect, in one possible implementation manner, the acquiring physical state parameters of the user before movement includes:

receiving a first pressure signal generated by a weight measuring device, and determining a physical state parameter of the user before exercise according to the first pressure signal, wherein the first pressure signal is generated when the weight measuring device is used by the weight measuring device to measure the weight of the user before exercise;

the acquiring of the body state parameters of the user after movement comprises:

and receiving a second pressure signal generated by the weight measuring device, and determining 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 measuring device when the weight measuring device is used for measuring the weight of the user after exercise.

before the user moves, measuring the weight of the user, generating a first pressure signal according to the pressure applied by the user, and determining the physical state parameter of the user before the movement according to the first pressure signal;

after the user moves, measuring the weight of the user, generating a second pressure signal according to the pressure applied by the user, and determining the physical state parameter of the user after the user moves according to the second pressure signal.

With reference to the first aspect, in one possible implementation manner, the method further includes:

acquiring a motion parameter corresponding to the user motion;

and calculating the heart recovery index and the lung recovery index of the user according to the physical state parameter of the user before the movement, the physical state parameter of the user after the movement and the movement parameter of the user.

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

and calculating the muscle fatigue degree of the user after the movement according to the body state parameters of the user before the movement, the body state parameters of the user after the movement and the movement parameters of the user.

With reference to the first aspect, in a possible implementation manner, the motion parameter includes one or any multiple of a motion type, a motion intensity, a motion duration, a duration after motion, and a motion parameter reliability.

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

sequentially carrying out high-pass amplification processing and low-pass filtering processing on the first pressure signal to obtain a first signal, carrying out high-pass filtering processing on the first signal to obtain a pre-movement impact signal, determining a pre-movement heart rate according to the pre-movement impact signal, determining a waveform profile of the pre-movement impact signal according to the waveform change of the pre-movement impact signal, calculating a pre-movement respiratory frequency according to a peak characteristic point of the waveform profile of the pre-movement impact signal and calculating pre-movement respiratory intensity according to a peak value of the waveform profile of the pre-movement impact signal;

and carrying out low-pass filtering processing on the first signal to obtain a second signal, calculating the main frequency, the peak-to-peak value and the standard deviation of the second signal, and calculating the body smoothness before movement according to the main frequency, the peak-to-peak value and the standard deviation of the second signal.

With reference to the first aspect, in one possible implementation manner, the determining the physical state parameter of the user after the movement according to the second pressure signal includes:

sequentially carrying out high-pass amplification processing and low-pass filtering processing on the second pressure signal to obtain a third signal, carrying out high-pass filtering processing on the third signal to obtain a moved cardioblast signal, determining a heart rate after movement according to the moved cardioblast signal, determining a waveform profile of the moved cardioblast signal according to the waveform change of the moved cardioblast signal, calculating a breathing frequency after movement according to the peak characteristic point of the waveform profile of the moved cardioblast signal and calculating breathing intensity after movement according to the peak value of the waveform profile of the moved cardioblast signal;

and performing low-pass filtering processing on the third signal to obtain a fourth signal, calculating the main frequency, the peak-to-peak value and the standard deviation of the fourth signal, and calculating the body smoothness after movement according to the main frequency, the peak-to-peak value and the standard deviation of the fourth signal.

calculating a body smoothness difference value between the body smoothness of the user before the user moves and the body smoothness of the user after the user moves, calculating a heart rate difference value between the heart rate of the user before the user moves and the heart rate of the user after the user moves, calculating a respiratory rate difference value between the respiratory rate of the user before the user moves and the respiratory rate of the user after the user moves, and calculating a respiratory intensity difference value between the respiratory intensity of the user before the user moves and the respiratory intensity of the user after the user moves;

and calculating the muscle fatigue of the user after exercise by adopting a weighted average algorithm according to the body smoothness difference value, the heart rate difference value, the respiratory rate difference value and the respiratory intensity difference value.

With reference to the first aspect, in a possible implementation manner, calculating a cardiac recovery index and a pulmonary recovery index of the user after exercise according to the physical state parameter of the user before exercise, the physical state parameter of the user after exercise, and the exercise parameter of the user includes:

and calculating the heart recovery index and the lung recovery index of the user after movement by adopting a weighted average algorithm according to the body smoothness difference value, the heart rate difference value, the respiratory rate difference value and the respiratory intensity difference value and combining the movement parameters of the user.

With reference to the first aspect, in a possible implementation manner, the calculating muscle fatigue, cardiac recovery index, and lung recovery index of the user according to the physical state parameter of the user before exercise, the calculation of the physical state parameter of the user after exercise, and the exercise parameter of the user includes:

and calculating muscle fatigue, heart recovery index and lung recovery index of the user by using a neural network according to the body smoothness difference, the heart rate difference, the respiratory rate difference and the respiratory intensity difference and by combining the motion parameters of the user.

displaying any one or more of the calculated muscle fatigue, heart recovery index and lung recovery index of the user, and generating and displaying one or more of an amount of exercise evaluation, a body function evaluation, an exercise suggestion and a body recovery suggestion according to the muscle fatigue, heart recovery index and lung recovery index of the user.

In a second aspect, the present application provides an apparatus for detecting muscle fatigue after exercise, the apparatus including:

the first acquisition module is used for acquiring body state parameters of a user before movement and body state parameters of the user after movement, wherein the body state parameters comprise body smoothness, heart rate, respiratory rate and respiratory intensity; and

the first calculation module is used for calculating the muscle fatigue degree of the user after the user moves according to the body state parameters of the user before the user moves and the body state parameters of the user after the user moves.

It can be understood that, in the embodiment of the present invention, by acquiring the body state parameters of the user before exercise and the body state parameters of the user after exercise, and then calculating the muscle fatigue of the user after exercise according to the body state parameters of the user before exercise and the body state parameters of the user after exercise, blood of the user does not need to be collected, the operation is simple, the user acceptance is high, and meanwhile, the muscle fatigue detection can be realized by means of the weight measurement device without using a special detection device.

With reference to the second aspect, in one possible implementation manner, the first obtaining module may include:

the first acquisition unit is used for receiving a first pressure signal generated by a weight measuring device and determining a body state parameter of the user before exercise according to the first pressure signal, wherein the first pressure signal is generated when the weight measuring device is used by the user to measure the weight before exercise; and

and the second acquisition unit is used for receiving a second pressure signal generated by the weight measuring equipment and determining 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 measuring equipment when the user uses the weight measuring equipment to measure the weight after exercise.

the first determination unit is used for measuring the weight of the user before the user exercises, generating a first pressure signal according to the pressure applied by the user, and determining the body state parameter of the user before the user exercises according to the first pressure signal; and

the second determining unit is used for measuring the weight of the user after the user exercises, generating a second pressure signal according to the pressure applied by the user, and determining the body state parameter of the user after the user exercises according to the second pressure signal.

With reference to the second aspect, in one possible implementation manner, the apparatus may further include:

the second acquisition module is used for acquiring motion parameters corresponding to the user motion; and

and the second calculation module is used for calculating the heart recovery index and the lung recovery index of the user according to the physical state parameter of the user before the movement, the physical state parameter of the user after the movement and the movement parameter of the user.

With reference to the second aspect, in one possible implementation manner, the first computing module may include:

the first calculation unit is used for calculating the muscle fatigue degree of the user after the movement according to the body state parameters of the user before the movement, the body state parameters of the user after the movement and the movement parameters of the user.

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

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

the first processing unit is used for sequentially carrying out high-pass amplification processing and low-pass filtering processing on the first pressure signal to obtain a first signal, carrying out high-pass filtering processing on the first signal to obtain a pre-exercise heart impact signal, determining a pre-exercise heart rate according to the pre-exercise heart impact signal, determining a pre-exercise heart impact signal waveform contour according to the pre-exercise heart impact signal waveform change, calculating a pre-exercise respiratory frequency according to a peak characteristic point of the pre-exercise heart impact signal waveform contour, and calculating pre-exercise respiratory intensity according to a peak value of the pre-exercise heart impact signal waveform contour; and

and the second processing unit is used for carrying out low-pass filtering processing on the first signal to obtain a second signal, calculating the main frequency, the peak-to-peak value and the standard deviation of the second signal, and calculating the body stability before movement according to the main frequency, the peak-to-peak value and the standard deviation of the second signal.

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

the third processing unit is used for sequentially carrying out high-pass amplification processing and low-pass filtering processing on the second pressure signal to obtain a third signal, carrying out high-pass filtering processing on the third signal to obtain a moved impact cardiac signal, determining a heart rate after movement according to the moved impact cardiac signal, determining a waveform profile of the moved impact cardiac signal according to the waveform change of the moved impact cardiac signal, calculating a breathing frequency after movement according to the peak characteristic point of the waveform profile of the moved impact cardiac signal and calculating breathing intensity after movement according to the peak value of the waveform profile of the moved impact cardiac signal; and

and the fourth processing unit is used for performing low-pass filtering processing on the third signal to obtain a fourth signal, calculating the main frequency, the peak-to-peak value and the standard deviation of the fourth signal, and calculating the body smoothness after movement according to the main frequency, the peak-to-peak value and the standard deviation of the fourth signal.

a second calculating unit, configured to calculate a body smoothness difference between the body smoothness of the user before exercise and the body smoothness of the user after exercise, calculate a heart rate difference between the heart rate of the user before exercise and the heart rate of the user after exercise, calculate a respiratory rate difference between the respiratory rate of the user before exercise and the respiratory rate of the user after exercise, and calculate a respiratory intensity difference between the respiratory intensity of the user before exercise and the respiratory intensity of the user after exercise; and

and the third calculating unit is used for calculating the muscle fatigue degree of the user after movement by adopting a weighted average algorithm according to the body smoothness difference value, the heart rate difference value, the respiratory rate difference value and the respiratory intensity difference value.

In one possible implementation manner, the second computing unit may further include:

and the first calculating subunit is used for calculating the heart recovery index and the lung recovery index of the user after movement by adopting a weighted average algorithm according to the body smoothness difference value, the heart rate difference value, the respiratory rate difference value and the respiratory intensity difference value and by combining the movement parameters of the user.

With reference to the second aspect, in one possible implementation manner, the first computing unit may include:

a second calculating subunit, configured to calculate a body smoothness difference between the body smoothness of the user before exercise and the body smoothness of the user after exercise, calculate a heart rate difference between the heart rate of the user before exercise and the heart rate of the user after exercise, calculate a respiratory rate difference between the respiratory rate of the user before exercise and the respiratory rate of the user after exercise, and calculate a respiratory intensity difference between the respiratory intensity of the user before exercise and the respiratory intensity of the user after exercise; and

and the third calculation subunit is used for calculating the muscle fatigue degree, the heart recovery index and the lung recovery index of the user by using a neural network according to the body smoothness difference value, the heart rate difference value, the respiratory rate difference value and the respiratory intensity difference value and by combining the motion parameters of the user.

the first display module is used for displaying any one or more of the calculated muscle fatigue, heart recovery index and lung recovery index of the user, and generating and displaying one or more of motion quantity evaluation, physical function evaluation, exercise suggestion and physical recovery suggestion according to the muscle fatigue, heart recovery index and lung recovery index of the user.

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

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

In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, which includes computer instructions that, when executed on an electronic device, cause the electronic device to perform the instructions of the first aspect or the method in any possible implementation manner of the first aspect.

In a sixth aspect, embodiments of the present application provide a computer program product, which when run on a computer causes the computer to execute the instructions of the first aspect or any possible implementation manner of the first aspect.

It can be understood that, in the embodiment of the present invention, the body state parameters of the user before exercise and the body state parameters of the user after exercise are obtained, and then the muscle fatigue of the user after exercise is calculated according to the body state parameters of the user before exercise and the body state parameters of the user after exercise, blood of the user does not need to be collected, the operation is simple, the user acceptance is high, meanwhile, the muscle fatigue detection can be realized by means of the weight measurement device, no special detection device is needed, and meanwhile, the detection method provided by the embodiment of the present invention is based on pressure detection, and no hardware product form needs to be changed.

Drawings

Fig. 1 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure;

FIG. 2 is a block diagram of a software architecture of an electronic device according to an embodiment of the invention;

FIG. 3 is a flowchart of a method for detecting muscle fatigue after exercise according to an embodiment of the present invention;

FIG. 4 is a diagram of an exemplary application framework of a method for detecting muscle fatigue after exercise according to an embodiment of the present invention;

FIG. 5 is a flowchart illustrating exemplary detection of a physical state parameter before a user moves according to an embodiment of the present invention;

FIG. 6 is a flowchart illustrating exemplary user detection of muscle fatigue, cardiac recovery index, and pulmonary recovery index after exercise according to an embodiment of the present invention;

fig. 7 is a schematic diagram of an exemplary measurement of a physical state parameter of a user before/after exercise according to an embodiment of the present invention;

FIG. 8 is a diagram illustrating calculation of muscle fatigue, cardiac recovery index, and lung recovery index via a neural network according to an embodiment of the present invention;

FIG. 9 is a diagram of an exemplary pre-exercise and post-exercise state selection interface for a post-exercise muscle fatigue detection device according to an embodiment of the present invention;

FIG. 10 illustrates an exemplary setup interface for automatic acquisition of athletic parameters provided by embodiments of the present invention;

FIG. 11 illustrates an exemplary athletic parameter manual entry selection interface provided by embodiments of the present invention;

FIG. 12 illustrates an exemplary athletic parameter input interface provided by embodiments of the present invention;

FIG. 13 illustrates yet another exemplary athletic parameter input interface provided by an embodiment of the present invention;

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

fig. 15 is a schematic view of a device for detecting muscle fatigue after exercise according to an embodiment of the present application.

Detailed Description

For better understanding of the technical solutions of the present invention, the following detailed descriptions of the embodiments of the present application are provided with reference to the accompanying drawings.

It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

In the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone, wherein A and B can be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of the singular or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or multiple.

The method for detecting muscle fatigue of a user after exercise provided by the embodiment of the application can be applied to the electronic device 100 shown in fig. 1, where the electronic device 100 shown in fig. 1 can be a portable electronic device that further includes other functions such as a personal digital assistant and/or a music player function, such as a mobile phone, a tablet computer, a wearable device (e.g., a smart watch) with a wireless communication function, and the like. Such as a laptop computer with a touch panel.

In some embodiments, the electronic device 100 includes a rectilinear display screen, a curved display screen, or a foldable display screen. The electronic device 100 collects touch points in a preset area when the user holds the electronic device 100, and uploads the touch points to the cloud server 200, and the cloud server 200 judges the holding posture of the user according to the touch points and feeds the holding posture back to the electronic device 100. In other embodiments, the electronic terminal 100 may also determine the holding gesture of the user according to the touch point, which is not limited herein.

As shown in fig. 1, the following describes an embodiment specifically by taking an electronic device 100 as an example.

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, an antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, an earphone interface 170D, a sensor module 180, a key 190, a motor 191, an indicator 192, a camera 193, a display screen 194, a Subscriber Identification Module (SIM) card interface 195, and the like. 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 light sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, an ambient light sensor 180L, a bone conduction sensor 180M, and the like.

It is to be understood that the illustrated structure of the embodiment of the present application does not specifically limit the electronic device 100. In other embodiments of the present application, electronic device 100 may include more or fewer components than shown, or some components may be combined, some components may be split, or a different arrangement of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

Processor 110 may include one or more processing units, such as: the processor 110 may include an Application Processor (AP), a modem processor, a Graphics Processing Unit (GPU), an Image Signal Processor (ISP), a controller, a video codec, a Digital Signal Processor (DSP), a baseband processor, and/or a neural-Network Processing Unit (NPU), etc. The different processing units may be separate devices or may be integrated into one or more processors.

The controller can generate an operation control signal according to the instruction operation code and the timing signal to complete the control of instruction fetching and instruction execution.

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 may hold instructions or data that have just been used or recycled by the processor 110. If the processor 110 needs to reuse the instruction or data, it can be called directly from the memory. Avoiding repeated accesses reduces the latency of the processor 110, thereby increasing the efficiency of the system.

In some embodiments, processor 110 may include one or more interfaces. The interface may include an integrated circuit (I2C) interface, an integrated circuit built-in audio (I2S) interface, a Pulse Code Modulation (PCM) interface, a universal asynchronous receiver/transmitter (universal asynchronous receiver/transmitter, universal serial bus (usb, universal serial bus (universal serial bus, or universal serial bus, or universal serial bus, or universal serial bus, or universal serial bus, or universal_RA single receiver/transmitter, UART interface, a Mobile industry processor interface (moB)_IA Mobile Industry Processor Interface (MIPI), a general-purpose input/output (GPIO) interface, a Subscriber Identity Module (SIM) interface, and/or a Universal Serial Bus (USB) interface, etc.

The I2C interface is a bi-directional synchronous serial bus that includes a serial data line (SDA) and a Serial Clock Line (SCL). In some embodiments, processor 110 may include multiple sets of I2C buses. The processor 110 may be coupled to the touch sensor 180K, the charger, the flash, the camera 193, etc. through different I2C bus interfaces, respectively. For example: the processor 110 may be coupled to the touch sensor 180K via an I2C interface, such that the processor 110 and the touch sensor 180K communicate via an I2C bus interface to implement the touch functionality of the electronic device 100.

The I2S interface may be used for audio communication. In some embodiments, processor 110 may include multiple sets of I2S buses. The processor 110 may be coupled to the audio module 170 via an I2S bus to enable communication between the processor 110 and the audio module 170. In some embodiments, the audio module 170 may communicate audio signals to the wireless communication module 160 via the I2S interface, enabling answering of calls via a bluetooth headset.

The PCM interface may also be used for audio communication, sampling, quantizing and encoding analog signals. In some embodiments, the audio module 170 and the wireless communication module 160 may be coupled by 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 implement a function of answering a call through a bluetooth headset. Both the I2S interface and the PCM interface may be used for audio communication.

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

MIPI interfaces may be used to connect processor 110 with peripheral devices such as display screen 194, camera 193, and the like. The MIPI interface includes a Camera Serial Interface (CSI), a Display Serial Interface (DSI), and the like. In some embodiments, processor 110 and camera 193 communicate through a CSI interface to implement the capture functionality of electronic device 100. The processor 110 and the display screen 194 communicate through the DSI interface to implement the display function of the electronic device 100.

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

The USB interface 130 is an interface conforming to the USB standard specification, and may specifically be a Mini USB interface, a Micro USB interface, a USB Type C interface, or the like. The USB interface 130 may be used to connect a charger to charge the electronic device 100, and may also be used to transmit data between the electronic device 100 and a peripheral device. And the earphone can also be used for connecting an earphone and playing audio through the earphone. The interface may also be used to connect other electronic devices, such as AR devices and the like.

It should be understood that the interface connection relationship between the modules illustrated in the embodiments of the present application is only an illustration, and does not limit the structure of the electronic device 100. In other embodiments of the present application, the electronic device 100 may also adopt different interface connection manners or a combination of multiple interface connection manners in the above embodiments.

The charging management module 140 is configured to receive charging input from a charger. The charger may be a wireless charger or a wired charger. In some wired charging embodiments, the charging management module 140 may receive charging input from a wired charger via the USB interface 130. In some wireless charging embodiments, the charging management module 140 may receive a wireless charging input through a wireless charging coil of the electronic device 100. The charging management module 140 may also supply power to the electronic device 100 through the power management module 141 while charging the battery 142.

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 194, the camera 193, the wireless communication module 160, and the like. The power management module 141 may also be used to monitor parameters such as battery capacity, battery cycle count, battery state of health (leakage, impedance), etc. In some other embodiments, the power management module 141 may also be disposed in the processor 110. In other embodiments, the power management module 141 and the charging management module 140 may be disposed in the same device.

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

The

antennas

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

The mobile communication module 150 may provide a solution including 2G/3G/4G/5G wireless communication 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 may receive the electromagnetic wave from the antenna 1, filter, amplify, etc. the received electromagnetic wave, and transmit the electromagnetic wave to the modem processor for demodulation. The mobile communication module 150 may also amplify the signal modulated by the modem processor, and convert the signal into electromagnetic wave through the antenna 1 to radiate the electromagnetic wave. In some embodiments, at least some of the functional modules of the mobile communication module 150 may be disposed in the processor 110. In some embodiments, at least some of the functional modules of the mobile communication module 150 may be disposed in the same device as at least some of the modules of the processor 110.

The modem processor may include a modulator and a demodulator. The modulator is used for modulating a low-frequency baseband signal to be transmitted into a medium-high frequency signal. The demodulator is used for demodulating the received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then passes the demodulated low frequency baseband signal to a baseband processor for processing. The low frequency baseband signal is processed by the baseband processor and then transferred 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 a stand-alone device. In other embodiments, the modem processor may be provided in the same device as the mobile communication module 150 or other functional modules, independent of the processor 110.

The wireless communication module 160 may provide Wireless Local Area Networks (WLANs) (e.g., wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (gloB) and the like for the electronic device 100_AGNSS (Global navigation satellite System), frequency modulation (F)_M) Near Field Communication (NFC), Infrared (IR), and the like. 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, performs frequency modulation and filtering processing on electromagnetic wave signals, and transmits the processed signals to the processor 110. The wireless communication module 160 may also receive a signal to be transmitted from the processor 110, perform frequency modulation and amplification on the signal, and convert the signal into electromagnetic waves through the antenna 2 to radiate the electromagnetic waves.

In some embodiments, antenna 1 of electronic device 100 is coupled to mobile communication module 150 and antenna 2 is coupled to wireless communication module 160 so that electronic device 100 can communicate with networks and other devices through wireless communication techniques. The wireless communication technology may include Global System for Mobile communications (gloB)_Al system for moB_IE communications, GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), wideband code division multiple access (wideband CDMA)_And code division multiple access, WCDMA), time-division code division multiple access (TD-SCDMA), Long Term Evolution (LTE), BT, GNSS, WLAN, NFC, F_MAnd/or IR techniques, etc. The GNSS may comprise a global positioning satellite system (gloB)_APositioning system, GPS), Global navigation satellite System (gloB)_AA navigation satellite system, GLONASS), the Beidou satellite navigation System (BDS), and the quasi-zenith satellite System (quasi-z)Inith satellite system, QZSS) and/or satellite based augmentation system (satellite B)_Ased augmentation systems，SB_AS)。

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

The display screen 194 is used to display images, video, and the like. The display screen 194 includes a display panel. The display panel may adopt a Liquid Crystal Display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode (active-matrix organic light-emitting diode, AMOLED), a flexible light-emitting diode (FLED), a miniature, a Micro-oeld, a quantum dot light-emitting diode (QLED), and the like. In some embodiments, the electronic device 100 may include 1 or N display screens 194, with N being a positive integer greater than 1. In this embodiment, the display 194 is a curved display or a foldable display.

The electronic device 100 may implement a shooting function through the ISP, the camera 193, the video codec, the GPU, the display 194, the application processor, and the like.

The ISP is used to process the data fed back by the camera 193. For example, when a photo is taken, the shutter is opened, light is transmitted to the camera photosensitive element through the lens, the optical signal is converted into an electrical signal, and the camera photosensitive element transmits the electrical signal to the ISP for processing and converting into an image visible to naked eyes. The ISP can also carry out algorithm optimization on the noise, brightness and skin color of the image. The ISP can also optimize parameters such as exposure, color temperature and the like of a shooting scene. In some embodiments, the ISP may be provided in camera 193.

The camera 193 is used to capture still images or video. The object generates an optical image through the lens and projects the optical image to the photosensitive element. The photosensitive element may be a Charge Coupled Device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The light sensing element converts the optical signal into an electrical signal, which is then passed to the ISP where it is converted into a digital image signal. And the ISP outputs the digital image signal to the DSP for processing. The DSP converts the digital image signal into image signal in standard RGB, YUV and other formats. In some embodiments, the electronic device 100 may include 1 or N cameras 193, N being a positive integer greater than 1.

The digital signal processor is used for processing digital signals, and can process digital image signals and other digital signals. For example, when the electronic device 100 selects a frequency bin, the digital signal processor is used to perform fourier transform or the like on the frequency bin energy.

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 may play or record video in a variety of encoding formats, such as: moving Picture Experts Group (MPEG) 1, MPEG2, MPEG3, MPEG4, and the like.

The NPU is a neural-network (NN) computing processor that processes input information quickly by using a biological neural network structure, for example, by using a transfer mode between neurons of a human brain, and can also learn by itself continuously. Applications such as intelligent recognition of the electronic device 100 can be realized through the NPU, for example: image recognition, face recognition, speech recognition, text understanding, and the like.

The external memory interface 120 may be used to connect an external memory card, such as a Micro SD card, to extend the memory capability of the electronic device 100. The external memory card communicates with the processor 110 through the external memory interface 120 to implement a data storage function. For example, files such as music, video, etc. are saved in an external memory card.

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

The electronic device 100 may implement audio functions via the audio module 170, the speaker 170A, the receiver 170B, the microphone 170C, the headphone interface 170D, and the application processor. Such as music playing, recording, etc.

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

The speaker 170A, also called a "horn", is used to convert the audio electrical signal into an acoustic signal. The electronic apparatus 100 can listen to music through the speaker 170A or listen to a handsfree call.

The receiver 170B, also called "earpiece", is used to convert the electrical audio signal into an acoustic signal. When the electronic apparatus 100 receives a call or voice information, it can receive voice by placing the receiver 170B close to the ear of the person.

The microphone 170C, also referred to as a "microphone," is used to convert sound signals into electrical signals. When making a call or transmitting voice information, the user can input a voice signal to the microphone 170C by speaking the user's mouth near 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 to achieve a noise reduction function in addition to collecting sound signals. In other embodiments, the electronic device 100 may further include three, four or more microphones 170C to collect sound signals, reduce noise, identify sound sources, perform directional recording, and so on.

The headphone interface 170D is used to connect a wired headphone. The earphone interface 170D may be the USB interface 130, or may be an open mobile electronic device platform (open moB) of 3.5mm_IAn OMTP) standard interface, a Cellular Telecommunications Industry Association (CTIA) standard interface.

The pressure sensor 180A is used for sensing a pressure signal, and converting the pressure signal into an electrical signal. In some embodiments, the pressure sensor 180A may be disposed on the display screen 194. The pressure sensor 180A can be of a wide variety, such as a resistive pressure sensor, an inductive pressure sensor, a capacitive pressure sensor, and the like. The capacitive pressure sensor may be a sensor comprising at least two parallel plates having an electrically conductive material. When a force acts on the pressure sensor 180A, the capacitance between the electrodes changes. The electronic device 100 determines the strength of the pressure from the change in capacitance. When a touch operation is applied to the display screen 194, the electronic apparatus 100 detects the intensity of the touch operation according to the pressure sensor 180A. The electronic apparatus 100 may also calculate the touched position from the detection signal of the pressure sensor 180A. In some embodiments, the touch operations that are applied to the same touch position but different touch operation intensities may correspond to different operation instructions. For example: and when the touch operation with the touch operation intensity smaller than the first pressure threshold value acts on the short message application icon, executing an instruction for viewing the short message. And when the touch operation with the touch operation intensity larger than or equal to the first pressure threshold value acts on the short message application icon, executing an instruction of newly building the short message.

The gyro sensor 180B may be used to determine the motion attitude of the electronic device 100. In some embodiments, the angular velocity of electronic device 100 about three axes (i.e., the x, y, and z axes) may be determined by gyroscope sensor 180B. The gyro sensor 180B may be used for photographing anti-shake. For example, when the shutter is pressed, the gyro sensor 180B detects a shake angle of the electronic device 100, calculates a distance to be compensated for by the lens module according to the shake angle, and allows the lens to counteract the shake of the electronic device 100 through a reverse movement, thereby achieving anti-shake. The gyroscope sensor 180B may also be used for navigation, somatosensory gaming scenes.

The air pressure sensor 180C is used to measure air pressure. In some embodiments, electronic device 100 calculates altitude, aiding in positioning and navigation, from barometric pressure values measured by barometric pressure sensor 180C.

The magnetic sensor 180D includes a hall sensor. The electronic device 100 may detect the opening and closing of the flip holster using the magnetic sensor 180D. In some embodiments, when the electronic device 100 is a flip phone, the electronic device 100 may detect the opening and closing of the flip according to the magnetic sensor 180D. And then according to the opening and closing state of the leather sheath or the opening and closing state of the flip cover, the automatic unlocking of the flip cover is set.

The acceleration sensor 180E may detect the magnitude of acceleration of the electronic device 100 in various directions (typically three axes). The magnitude and direction of gravity can be detected when the electronic device 100 is stationary. The method can also be used for recognizing the posture of the electronic equipment 100, and is applied to horizontal and vertical screen switching, pedometers and other applications.

A distance sensor 180F for measuring a distance. The electronic device 100 may measure the distance by infrared or laser. In some embodiments, taking a picture of a scene, electronic device 100 may utilize range sensor 180F to range for fast focus.

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 detects infrared reflected light from nearby objects using a photodiode. 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 may determine that there are no objects near the electronic device 100. The electronic device 100 can utilize the proximity light sensor 180G to detect that the user holds the electronic device 100 close to the ear for talking, so as to automatically turn off the screen to achieve the purpose of saving power. The proximity light sensor 180G may also be used in a holster mode, a pocket mode automatically unlocks and locks the screen.

The ambient light sensor 180L is used to sense the ambient light level. Electronic device 100 may adaptively adjust the brightness of display screen 194 based on the perceived ambient light level. The ambient light sensor 180L may also be used to automatically adjust the white balance when taking a picture. The ambient light sensor 180L may also cooperate with the proximity light sensor 180G to detect whether the electronic device 100 is in a pocket to prevent accidental touches.

The fingerprint sensor 180H is used to collect a fingerprint. The electronic device 100 can utilize the collected fingerprint characteristics to unlock the fingerprint, access the application lock, photograph the fingerprint, answer an incoming call with the fingerprint, and so on.

The temperature sensor 180J is used to detect temperature. In some embodiments, electronic device 100 implements a temperature processing strategy using the temperature detected by temperature sensor 180J. For example, when the temperature reported by the temperature sensor 180J exceeds a threshold, the electronic device 100 performs a reduction in performance of a processor located near the temperature sensor 180J, so as to reduce power consumption and implement thermal protection. In other embodiments, the electronic device 100 heats the battery 142 when the temperature is below another threshold to avoid the low temperature causing the electronic device 100 to shut down abnormally. In other embodiments, when the temperature is lower than a further threshold, the electronic device 100 performs boosting on the output voltage of the battery 142 to avoid abnormal shutdown due to low temperature.

The touch sensor 180K is also referred to as a "touch panel". The touch sensor 180K may be disposed on the display screen 194, and the touch sensor 180K and the display screen 194 form a touch screen, which is also called a "touch screen". The touch sensor 180K is used to detect a touch operation applied thereto or nearby. The touch sensor can communicate the detected touch operation to the application processor to determine the touch event type. Visual output associated with the touch operation may be provided through the display screen 194. In other embodiments, the touch sensor 180K may be disposed on a surface of the electronic device 100, different from the position of the display screen 194.

The bone conduction sensor 180M may acquire a vibration signal. In some embodiments, the bone conduction sensor 180M may acquire a vibration signal of the human vocal part vibrating the bone mass. The bone conduction sensor 180M may also contact the human pulse to receive the blood pressure pulsation signal. In some embodiments, the bone conduction sensor 180M may also be disposed in a headset, integrated into a bone conduction headset. The audio module 170 may analyze a voice signal based on the vibration signal of the bone mass vibrated by the sound part acquired by the bone conduction sensor 180M, so as to implement a voice function. The application processor can analyze heart rate information based on the blood pressure beating signal acquired by the bone conduction sensor 180M, so as to realize the heart rate detection function.

The keys 190 include a power-on key, a volume key, and the like. The keys 190 may be mechanical keys. Or may be touch keys. The electronic apparatus 100 may receive a key input, and generate a key signal input related to user setting and function control of the electronic apparatus 100.

The motor 191 may generate a vibration cue. The motor 191 may be used for incoming call vibration cues, as well as for touch vibration feedback. For example, touch operations applied to different applications (e.g., photographing, audio playing, etc.) may correspond to different vibration feedback effects. The motor 191 may also respond to different vibration feedback effects for touch operations applied to different areas of the display screen 194. Different application scenes (such as time reminding, receiving information, alarm clock, game and the like) can also correspond to different vibration feedback effects. The touch vibration feedback effect may also support customization.

Indicator 192 may be an indicator light that may be used to indicate a state of charge, a change in charge, or a message, missed call, notification, etc.

The SIM card interface 195 is used to connect a SIM card. The SIM card can be brought into and out of contact with the electronic apparatus 100 by being inserted into the SIM card interface 195 or being pulled out of the SIM card interface 195. The electronic device 100 may support 1 or N SIM card interfaces, N being a positive integer greater than 1. The SIM card interface 195 may support a Nano SIM card, a Micro SIM card, a SIM card, etc. The same SIM card interface 195 can be inserted with multiple cards at the same time. The types of the plurality of cards may be the same or different. The SIM card interface 195 may 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 communication and data communication. In some embodiments, the electronic device 100 employs esims, namely: 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 employ a layered architecture, an event-driven architecture, a micro-core architecture, a micro-service architecture, or a cloud architecture. The embodiment of the present invention uses an Android system with a layered architecture as an example to exemplarily illustrate a software structure of the electronic device 100.

Fig. 2 is a block diagram of a software configuration of the electronic apparatus 100 according to the embodiment of the present invention.

The layered architecture divides the software into several layers, each layer having a clear role and division of labor. The layers communicate with each other through a software interface. In some embodiments, the Android system is divided into four layers, an application layer, an application framework layer, an Android runtime (Android runtime) and system library, and a kernel layer from top to bottom.

The application layer may include a series of application packages.

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

The application framework layer provides an Application Programming Interface (API) and a programming framework for the application program of the application layer. The application framework layer includes a number of predefined functions.

As shown in FIG. 2, the application framework layers may include a window manager, content provider, view system, phone manager, resource manager, notification manager, and the like.

The window manager is used for managing window programs. The window manager can obtain the size of the display screen, judge whether a status bar exists, lock the screen, intercept the screen and the like.

The content provider is used to store and retrieve data and make it accessible to applications. The data may include video, images, audio, calls made and received, browsing history and bookmarks, phone books, etc.

The view system includes visual controls such as controls to display text, controls to display pictures, and the like. The view system may be used to build applications. The display interface may be composed of one or more views. For example, the display interface including the short message notification icon may include a view for displaying text and a view for displaying pictures.

The phone manager is used to provide communication functions of the electronic device 100. Such as management of call status (including on, off, etc.).

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

The notification manager enables the application to display notification information in the status bar, can be used to convey notification-type messages, can disappear automatically after a short dwell, and does not require user interaction. Such as a notification manager used to inform download completion, message alerts, etc. The notification manager may also be a notification that appears in the form of a chart or scroll bar text at the top status bar of the system, such as a notification of a background running application, or a notification that appears on the screen in the form of a dialog window. For example, prompting text information in the status bar, sounding a prompt tone, vibrating the electronic device, flashing an indicator light, etc.

The Android Runtime comprises a core library and a virtual machine. The Android runtime is responsible for scheduling and managing an Android system.

The core library comprises two parts: one part is a function which needs to be called by java language, and the other part is a core library of android.

The application layer and the application framework layer run in a virtual machine. And executing java files of the application program layer and the application program framework layer into a binary file by the virtual machine. The virtual machine is used for performing the functions of object life cycle management, stack management, thread management, safety and exception management, garbage collection and the like.

The system library may include a plurality of functional modules. For example: surface managers (surface managers), Media LiBraries (Media LiBraries), three-dimensional graphics processing LiBraries (e.g., OpenGL ES), 2D graphics engines (e.g., SGL), and the like.

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

The media library supports a variety of commonly used audio, video format playback and recording, and still image files, among others. The media library may support a variety of audio-video encoding formats, such as MPEG4, h.264, MP3, AAC, AMR, JPG, PNG, and the like.

The three-dimensional graphic processing library is used for realizing three-dimensional graphic drawing, image rendering, synthesis, layer processing and the like.

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

The kernel layer is a layer between hardware and software. The inner core layer at least comprises a display driver, a camera driver, an audio driver and a sensor driver.

The following describes exemplary workflow of the software and hardware of the electronic device 100 in connection with capturing a photo scene.

When the touch sensor 180K receives a touch operation, a corresponding hardware interrupt is issued to the kernel layer. The kernel layer processes the touch operation into an original input event (including touch coordinates, a time stamp of the touch operation, and other information). The raw input events are stored at the kernel layer. And the application program framework layer acquires 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 taking a control corresponding to the click operation as a control of a camera application icon as an example, the camera application calls an interface of an application framework layer, starts the camera application, further starts a camera drive by calling a kernel layer, and captures a still image or a video through the camera 193.

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

The method for detecting the muscle fatigue degree after exercise is provided based on the prior art, can facilitate the user to measure by himself, is high in user acceptance degree, and does not need special detection equipment.

a method for detecting muscle fatigue after exercise, which can be applied to a device for detecting muscle fatigue after exercise, where the detecting device can be the electronic device 100 (e.g. a mobile phone), a weight measuring device (e.g. a weight scale or a body fat scale or other devices with a weight measuring function), or of course, other electronic devices, as shown in fig. 3, and the method includes:

step S11: and acquiring the physical state parameters of the user before movement and the physical state parameters of the user after movement.

Step S12: and calculating the muscle fatigue degree of the user after the movement according to the body state parameters of the user before the movement and the body state parameters of the user after the movement.

It can be understood that, in the embodiment of the invention, the body state parameters of the user before exercise and the body state parameters of the user after exercise are obtained, and then the muscle fatigue of the user after exercise is calculated according to the body state parameters of the user before exercise and the body state parameters of the user after exercise, blood of the user does not need to be collected, the operation is simple, the user acceptance is high, meanwhile, the muscle fatigue detection can be realized by depending on weight measuring equipment, and special detection equipment is not needed.

In a specific implementation, steps S11 and S12 may be executed by a terminal device (e.g., the electronic device 100) or a weight measuring device (e.g., a weight scale).

For a scenario executed by a terminal device (e.g., the electronic device 100), specifically, the scenario may be: the terminal device is in communication connection with the weight measuring device, the weight measuring device sends pressure signals to the terminal device after measuring the pressure signals applied by the user before and after exercise based on the pressure test, and the terminal device determines body state parameters of the user before and after exercise according to the pressure signals respectively, so that muscle fatigue is calculated and displayed to the user.

Of course, in other implementation manners of the scenario, the weight measurement device may also process the pressure signal measured by the weight measurement device to obtain body state parameters of the user before and after exercise, and then send the body state parameters of the user before and after exercise to the terminal device, and the terminal device calculates muscle fatigue according to the body state parameters before and after exercise.

In order to distinguish the pressure signals before and after the movement, the embodiment of the present invention refers to the pressure signal before the movement as a "first pressure signal", and refers to the pressure signal after the movement as a "second pressure signal".

It should be noted that the states of the user are divided into before-exercise, and after-exercise, wherein if the body state parameters before the historical exercise (in the rest state) exist during the weight measurement of the user before the exercise, the body state parameters obtained by the current measurement and the body state parameters before the historical exercise (in the rest state) may be subjected to weighted average operation to obtain new body state parameters before the historical exercise (in the rest state). Of course, in some embodiments, the weighted average operation may not be performed, and when the user performs multiple measurements before exercise, the latest physical state parameter before exercise (in the resting state) is directly used as the physical state parameter before exercise (in the resting state). In one or more embodiments of the present invention, the state of the user may be determined to be pre-exercise or post-exercise according to the state (pre-exercise/post-exercise) selected by the user at the time of the weight measurement.

For a scenario executed by a weight measuring device (e.g., a weight scale), specifically, the scenario may be: after a first pressure signal applied by a user before exercise is measured by the weight measuring equipment based on a pressure test, determining body state parameters of the user before exercise according to the first pressure signal; after the weight measuring device measures a second pressure signal applied by the user before exercise based on the pressure test, determining body state parameters of the user after exercise according to the second pressure signal, namely, each step of the detecting method for muscle fatigue after exercise provided by the embodiment of the invention is executed by the weight measuring device;

it can be understood that the detection method provided by the embodiment of the invention is based on pressure detection, and the hardware product form is not required to be changed.

Based on the above, in an optional embodiment, acquiring the physical state parameter of the user before movement includes:

receiving a first pressure signal generated by a weight measuring device, and determining a body state parameter of a user before exercise according to the first pressure signal, wherein the first pressure signal is generated when the weight measuring device is used for measuring the weight of the user before exercise;

acquiring physical state parameters of a user after movement, wherein the physical state parameters comprise:

Based on the above, in another optional embodiment, acquiring the physical state parameter of the user before movement includes:

before the user moves, measuring the weight of the user, generating a first pressure signal according to the pressure applied by the user, and determining the body state parameter of the user before the movement according to the first pressure signal;

after the user moves, the weight of the user is measured, a first pressure signal is generated according to the pressure applied by the user, and the physical state parameter of the user after the movement is determined according to the first pressure signal.

In another optional embodiment, the body state parameter generated by the weight measuring device before the user exercise and the body state parameter generated by the body weight measuring device after the user exercise are received, wherein the body state parameter generated by the weight measuring device before the user exercise is generated according to the measured first pressure signal, and the body state parameter generated by the weight measuring device after the user exercise is generated according to the measured second pressure signal.

In an alternative embodiment, the physical state parameters of the user prior to exercise include, but are not limited to: body smoothness B_A1Heart rate H_R1Respiratory rate B_R1And respiration intensity B_I1One or any plurality thereof.

In an alternative embodiment, the physical state parameters of the user after exercise include, but are not limited to: body smoothness B_A2Heart rate H_R2Respiratory rate B_R2And respiration intensity B_I2One or any plurality thereof.

In an alternative embodiment, receiving the first pressure signal generated by the weight measuring device further comprises:

acquiring a motion parameter corresponding to the motion of a user;

calculating the heart recovery index R of the user according to the physical state parameter of the user before movement, the physical state parameter of the user after movement and the movement parameter of the user_HAnd lung recovery index R_L。

In an alternative embodiment, calculating the muscle fatigue of the user after exercise according to the physical state parameter of the user before exercise and the physical state parameter of the user after exercise may include:

calculating muscle fatigue degree F of the user after exercise 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_M。

It can be understood that the embodiment of the invention can be used for calculating the muscle fatigue degree F of the user by combining the motion parameters of the user_MOf course, the muscle fatigue degree F of the user may be calculated without combining the exercise parameters of the user_MIn the embodiment of the invention, the heart recovery index R of the user can be calculated by combining the motion parameters of the user, the body state parameters of the user before motion and the body state parameters of the user after motion_HAnd/or lung recovery index R_L。

In an optional embodiment, the motion parameter includes one or more of a motion type s, a motion intensity m, a motion duration T, a time after motion T, and a motion parameter confidence level w.

In the embodiment of the present invention, the motion parameters may be obtained in the following manners: the exercise health class App (Application) of the device executing the method for detecting the muscle fatigue after exercise in fig. 3 or the smart device worn by the user, such as a sports watch and a sports bracelet, is obtained.

In specific implementation, when a user finishes an exercise (e.g., running), the exercise health class App of a corresponding device (e.g., a mobile phone) may obtain related exercise parameters of the user's exercise, such as one or more of an exercise type s, an exercise intensity m, an exercise duration T, a time after exercise T, and the like, and when the user carries the device while exercising, the device may automatically obtain the exercise type, the exercise intensity, the exercise duration, the time after exercise, and the like of the user according to an action of the user, or of course, the device may also be input into the exercise health class App or an exercise watch or an exercise bracelet by the user, and then the device that executes the method for detecting muscle fatigue degree after exercise in fig. 3 obtains the related exercise parameters from the related exercise health class App, where the exercise health class App may obtain specific implementation processes of the related exercise parameters of the user, which are commonly used in the prior art, the embodiments of the present invention are not described in detail herein.

Of course, in another specific implementation, the relevant exercise parameters may be obtained through manual input by the user, or a part of the exercise parameters may be obtained from the exercise health class App, and a part of the exercise parameters may be obtained through manual input by the user, of course, all the exercise parameters or a part of the exercise parameters may also be unnecessary, for example, all the exercise parameters or a part of the exercise parameters may be null, which is not specifically limited by the present invention.

The motion parameter may further include a motion parameter confidence level w. In the embodiment of the present invention, the exercise parameter reliability w may be associated with the source and/or integrity and/or number of other exercise parameters except the exercise parameter reliability w, for example, when the other exercise parameters except the exercise parameter reliability w are automatically obtained from the exercise health class App or the watch bracelet, the exercise parameter reliability is the highest, and may be set to w-1; when the user manually inputs the input, the motion parameter confidence is the second time, and w may be 0.5; when the motion parameters except the motion parameter confidence level w are all empty, the motion parameter confidence level is the lowest, and can be set to be w-0, and the determination mode of the motion parameter confidence level w can be flexibly set according to actual requirements.

In the embodiment of the invention, the muscle fatigue F after the movement of the user is calculated according to the body state parameters of the user before the movement, the body state parameters of the user after the movement and/or the movement parameters of the user_MCardiac recovery index R_HLung recovery index R_LCalculating the muscle fatigue F after the user exercise_MCardiac recovery index R_HLung recovery index R_LIncluding but not limited to by means of weighted average algorithms, neural networks, and the like.

In the embodiment of the invention, the muscle fatigue F of the user is calculated_MAnd/or a cardiac recovery index R_HLung recovery index R_LThe present invention may further comprise: displaying the calculated muscle fatigue F of the user_MCardiac recovery index R_HAnd lung recovery index R_LAnd generating and displaying the amount of exercise evaluation and/or physical function evaluation and/or exercise advice and/or physical recovery advice to the user based on any one or any plurality of muscle fatigue, cardiac recovery index, and lung recovery index of the user, as will be described in detail below.

It can be understood that, in the embodiment of the present invention, by displaying the detection result to the user and providing the motion amount evaluation and/or the body function evaluation and/or the motion advice and/or the body recovery advice, the user can know the current body state conveniently in a man-machine interaction manner, and meanwhile, the user is provided with the guidance on the motion and the body recovery, so that the user can be prevented from performing exercise training with unreasonable intensity and time and be guided to recover the body function quickly.

as shown in fig. 4, the process of calculating the muscle fatigue, the heart recovery index and the lung recovery index by the user may specifically include:

step (1): before the user moves (at rest), the weight measuring device (such as 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, generates a pressure signal (i.e. the first pressure signal), and the weight measuring device or the terminal device calculates the body stability B according to the first pressure signal_A1Heart rate H_R1Respiratory rate B_R1Respiratory intensity B_I1As a body state parameter of the user in a resting state;

step (2): after the user moves, the weight measuring device (such as 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, generates the pressure signal (i.e. the second pressure signal), and the weight measuring device or the terminal device calculates the body stability B according to the second pressure signal_A2Heart rate H_R2Respiratory rate B_R2Respiratory intensity B_I2As a physical state parameter of the user after exercise;

and (3): after the user exercises, the weight measuring device or the terminal device obtains exercise parameters of the user, including exercise type s, exercise intensity m, exercise duration T, exercise after-exercise time T and exercise parameter reliability w, and the obtaining mode is preferably automatically obtained from exercise health type apps or corresponding devices such as watches and bracelets, and can also be manually input by the user or empty. When the exercise parameters are automatically acquired from equipment such as an exercise health App, a watch, a bracelet and the like, the exercise parameters have the highest credibility, and w can be set to be 1; when the user manually inputs the input, the motion parameter confidence is the second time, and w may be 0.5; when the motion parameter is null, the motion parameter confidence is the lowest, and w may be set to 0.

Wherein, the step (3) can be carried out simultaneously with the step (2), or the step (3) is carried out first and then the step (2) is carried out.

And (4): calculating muscle fatigue F according to the resting state and physical state parameters after movement of the user and the movement parameters_MCardiac recovery index R_HLung recovery index R_LThe calculation method includes, but is not limited to, by a weighted average algorithm or a neural network, etc.

And (5): and displaying the calculated muscle fatigue, heart recovery index, lung recovery index, body smoothness, heart rate, respiratory frequency and respiratory intensity on an interface, and giving exercise amount and body function evaluation, exercise suggestion and recovery suggestion and the like.

as shown in fig. 5, the process of detecting the physical state parameter before the user moves (in a resting state), which may be implemented by a weight measuring device or a terminal device, includes:

step S21: acquiring a first pressure signal, wherein the first pressure signal can be acquired by weight measurement equipment;

step S22: sequentially carrying out high-pass amplification and low-pass filtering processing on the first pressure signal;

step S23: carrying out low-pass filtering processing on the first pressure signal subjected to the high-pass amplification and low-pass filtering processing to obtain the body stability B in the pre-movement/resting state_A0And performing high-pass processing on the first pressure signal subjected to high-pass amplification and low-pass filtering to obtain the heart rate H of the user in the state before movement/at rest_R0Respiratory rate B_R0Respiratory intensity B_I0；

Step S24: judging whether the body state parameters (including the body stability B) in the historical resting state are stored_A3Heart rate in historical resting state H_R3Respiratory rate at historical rest B_R3Respiration intensity at historical rest B_I3Any one or more of) the body state parameters obtained in step S23 and the corresponding historical resting states, if any, are obtainedThe next physical state parameter is subjected to weighted average operation to obtain a new physical state parameter of the user before exercise/at rest, and if not, the physical state parameter obtained in step S23 is used as the physical state parameter of the user before exercise (at rest).

In particular, the body smoothness B when stored with historical resting state_A3Body stability B of the user at time of exercise/rest_A1＝W_new1*B_A0+W_old1*B_A3Wherein W is_new1And W_old1As a weighting coefficient, W_new1And W_old1The sum is 1; when there is no historical resting body stability B stored_A3When, B_A1＝B_A0。

In particular, the heart rate H at historical rest is stored_R3Heart rate H of the user in pre-exercise/resting state_R1＝W_new2*H_R0+W_old2*H_R3Wherein W is_new2And W_old2As a weighting coefficient, W_new2And W_old2The sum is 1; when there is no historical resting heart rate H stored_R3When H is present_R1＝H_R0。

In particular, the breathing frequency B in the historical resting state is stored_R3The respiratory rate B of the user in the pre-exercise/resting state_R1＝W_new3*B_R0+W_old3*B_R3Wherein W is_new3And W_old3As a weighting coefficient, W_new3And W_old3The sum is 1; when there is no historical resting body stability B stored_R3When H is present_R1＝H_R0。

In particular, the respiration intensity B at the historical resting state is stored_I3The respiration intensity B of the user in the pre-exercise/resting state_I1＝W_new4*B_I0+W_old2*B_I3Wherein W is_new4And W_old4As a weighting coefficient, W_new4And W_old4The sum is 1; when there is no stored respiration intensity B of historical resting state_I3When, B_I1＝B_I0。

Step S24: storing and displaying the physical state parameters before movement (in a resting state): body smoothness B_A1Heart rate H_R1Respiratory rate B_R1Respiratory intensity B_I1。

as shown in FIG. 6, the muscle fatigue F is detected after the user exercise_MCardiac recovery index R_HAnd lung recovery index R_LThe process of (2) may be implemented by a weight measuring device or a terminal device, and the process includes:

step S31, acquiring motion parameters of a user, wherein the motion parameters comprise one or more of motion type S, motion intensity m, motion duration T, time duration after motion T and motion parameter credibility w; as shown in fig. 6, before acquiring the exercise parameters of the user, the manner of acquiring the exercise parameters of the user may be set.

In an alternative embodiment, the manner of obtaining the motion parameters of the user may include:

automatically acquiring motion parameters (the motion parameters can comprise a motion type s) through a sports health class App or corresponding devices such as a watch and a bracelet₁Exercise intensity m₁Duration of movement t₁Time after exercise T₁Reliability w of motion parameters₁)；

Alternatively, the motion parameters are input by the user (the motion parameters may include a motion type s)₂Exercise intensity m₂Duration of movement t₂Time after exercise T₂Reliability w of motion parameters₂)；

Or, part of the motion parameters are acquired from the sports health App or corresponding equipment such as a watch and a bracelet, and part of the motion parameters are input by a user;

of course, the exercise parameters may be unnecessary, and when the exercise parameters cannot be obtained from the sports health class App or the corresponding watch, bracelet, or other devices, and the user does not input the exercise parameters, the exercise parameters may be null.

In specific implementation, the exercise parameters can be preferably acquired automatically through the exercise health App or the corresponding watch, bracelet and other devices, when the exercise parameters cannot be acquired from the exercise health App or the corresponding watch, bracelet and other devices, the exercise parameters are input through the user, and when the user does not input the exercise parameters, the exercise parameters are null.

The user may specifically input the motion parameter by displaying an input interface for the user to input the motion parameter to the user, and the user performs operations such as inputting and/or selecting on the input interface to realize the input of the motion parameter.

Step S32, collecting a second pressure signal which can be collected by weight measuring equipment;

step S33: sequentially carrying out high-pass amplification processing and low-pass filtering processing on the first pressure signal;

step S34: the second pressure signal after the high-pass amplification processing and the low-pass filtering processing is subjected to low-pass filtering processing to obtain the body stability B before movement (in a resting state)_A2And carrying out high-pass processing on the second pressure signal subjected to high-pass amplification and low-pass filtering processing to obtain the heart rate H of the user in the state before movement/at rest_R2Respiratory rate B_R2Respiratory intensity B_I2；

Step S35: calculating the muscle fatigue F of the user by combining the physical state parameter before the movement (in the resting state) of the user and the movement parameter_MCardiac recovery index R_HAnd lung recovery index R_L。

Step S36: displaying the calculated muscle fatigue F_MCardiac recovery index R_HAnd lung recovery index R_LFrom the calculated muscle fatigue F_MCardiac recovery index R_HAnd lung recovery index R_LAnd (4) giving the motion quantity evaluation, the physical function evaluation, the motion advice and the physical recovery advice.

It should be noted that the above steps S31-S36 are exemplary implementations, and some steps may be unnecessary or alternative, for example, the muscle fatigue degree F obtained from calculation in step S36_MCardiac recovery index R_HAnd lung recovery index R_LIt may be unnecessary to give the motion amount evaluation, the physical function evaluation, the motion advice, and the physical restoration advice, or only a part of the motion amount evaluation, the physical function evaluation, the motion advice, and the physical restoration advice may be given, for example, only the motion amount evaluation and the physical function evaluation.

Fig. 7 is a schematic diagram of an exemplary measurement of a physical state parameter of a user before/after exercise according to an embodiment of the present invention.

As shown in fig. 7, taking signal 0 as the first pressure signal as an example, determining the physical state parameter of the user before exercise according to the first pressure signal includes:

step S41: sequentially carrying out high-pass amplification processing and low-pass filtering processing on the first pressure signal (signal 0) to obtain a first signal (namely a signal 2 shown in fig. 7);

wherein, step S41 may further include: the first pressure signal (signal 0) is low-pass filtered to obtain weight information (i.e., signal 1 shown in fig. 7).

Step S42 (a): carrying out high-pass filtering processing on the first signal (signal 2) to obtain a heart impact signal BCG (signal 3) after movement, and determining the heart rate H of the user before movement according to the heart impact signal BCG (signal 3) after movement_R1Determining the waveform profile of the pre-exercise cardioblast signal (signal 3) according to the waveform change of the pre-exercise cardioblast signal (signal 3), and calculating the respiratory frequency B before the exercise according to the peak characteristic point of the waveform profile of the pre-exercise cardioblast signal (signal 3)_R1And calculating the respiration intensity B before the exercise from the peak-to-peak value of the waveform profile of the pre-exercise cardioblast signal (signal 3)_I1。

Step S42 (b): low-pass filtering the first signal (i.e. signal 2 shown in fig. 7) to obtain a second signal (i.e. signal 4 shown in fig. 7), calculating the main frequency, peak-to-peak value and standard deviation of the second signal (signal 4), and calculating the body smoothness B before exercise according to the main frequency, peak-to-peak value and standard deviation of the second signal (signal 4)_A1Wherein the body flat temperature B_A1Including but not limited to multiple linear regression.

The step S42(a) and the step S42(b) may be performed simultaneously or sequentially, and the order is not limited in the embodiment of the present invention.

Continuing with fig. 7, taking signal 0 as the second pressure signal as an example, determining the physical state parameter of the user after exercise according to the second pressure signal includes:

step S51: sequentially carrying out high-pass amplification processing and low-pass filtering processing on the second pressure signal (signal 0) to obtain a third signal (namely a signal 2 shown in fig. 7);

wherein, step S51 may further include: the second pressure signal (signal 0) is low-pass filtered to obtain weight information (i.e., signal 1 shown in fig. 7).

Step S52 (a): carrying out high-pass filtering processing on the third signal (signal 2) to obtain a heart impact signal BCG (signal 3) after movement, and determining the heart rate H of the user after movement according to the heart impact signal (signal 3) after movement_R2Determining the waveform profile of the cardiac shock signal (signal 3) after the movement according to the waveform change of the cardiac shock signal (signal 3) after the movement, and calculating the respiratory frequency B after the movement according to the peak characteristic point of the waveform profile of the cardiac shock signal (signal 3) after the movement_R2And calculating the respiration intensity B after exercise from the peak-to-peak value of the waveform profile of the cardioblast signal (signal 3) after exercise_I2。

Step S52 (b): low-pass filtering the third signal (i.e. signal 2 shown in fig. 7) to obtain a fourth signal (i.e. signal 4 shown in fig. 7), calculating the main frequency, peak-to-peak value and standard deviation of the fourth signal (signal 4), and calculating the body smoothness B after exercise according to the main frequency, peak-to-peak value and standard deviation of the fourth signal (signal 4)_A2Wherein the body flat temperature B_A2Including but not limited to multiple linear regression.

The step S52(a) and the step S52(b) may be performed simultaneously or sequentially, and the order is not limited in the embodiment of the present invention.

It can be understood that in the embodiment shown in fig. 7, the pressure of the human body on the body is measured by the body weight measuring device to generate a pressure signal, the pressure signal is subjected to different high-pass, low-pass and amplification processes to analyze various signals such as the body weight, the heart rate, the respiratory intensity, the body stability and the like, and various human body physiological signals (i.e., body state parameters) can be obtained only by the pressure signal without an additional sensor.

In the embodiment of the present invention, the body weight measuring device or the terminal device may calculate the muscle fatigue, the heart recovery index, and the lung recovery index of the user in the following manner.

The first method is as follows: by a weighted average algorithm.

In specific implementation, the muscle fatigue degree F of the user is calculated by adopting a weighting algorithm_MThe method can comprise the following steps:

step S61: using body smoothness B before movement_A1Heart rate H_R1Respiratory rate B_R1Respiratory intensity B_I1And body smoothness after exercise B_A2Heart rate H_R2Respiratory rate B_R2Respiratory intensity B_I2Separately calculating the physiological parameter difference before and after exercise (body stability difference B)_AHeart rate difference H_RRespiratory rate difference B_RAnd the difference B of the respiratory intensity_I)。

Wherein, the difference value of the body smoothness B_A＝B_A2-B_A1Heart rate difference H_R＝H_R2-H_R1Difference in respiratory rate B_R＝B_R2-B_R1Difference in respiratory intensity B_I＝B_I2-B_I1。

Step S62: using a weighted average algorithm based on the body smoothness difference B_AHeart rate difference H_RRespiratory rate difference B_RAnd the difference B of the respiratory intensity_ICalculating muscle fatigue F_M。

Specifically, F_M＝a₁*B_A+a₂*H_R+a₃*B_R+a₄*B_I；

Wherein, a₁，a₂，a₃，a₄The coefficient can be flexibly set according to actual requirements.

In a specific implementation, a weighted algorithm is used to calculate a cardiac recovery index R for a user_HThe method can comprise the following steps:

step S71: using body smoothness B before movement_A1Heart rate H_R1Respiratory rate B_R1Respiratory intensity B_I1And body smoothness after exercise B_A2Heart rate H_R2Respiratory rate B_R2Respiratory intensity B_I2Separately calculating the physiological parameter difference before and after exercise (body stability difference B)_AHeart rate difference H_RRespiratory rate difference B_RAnd the difference B of the respiratory intensity_I)。

Step S72: using a weighted average algorithm based on the body smoothness difference B_AHeart rate difference H_RRespiratory rate difference B_RAnd the difference B of the respiratory intensity_IIn combination with the type of movement s₁Exercise intensity m₁Duration of movement t₁Time after exercise T₁Reliability w of motion parameters₁Calculating a cardiac recovery index R_H。

Specifically, R_H＝b₁*B_A+b₂*H_R+b₃*B_R+b₄*B_I+b₅*s₁+b₆*m₁+b₇*t₁+b₈*T₁+b₉*w₁；

Wherein, b₁，b₂，b₃，b₄，b₅，b₆，b₇，b₈，b₉The coefficient can be flexibly set according to actual requirements.

In a specific implementation, a weighting algorithm is adopted to calculate the lung recovery index R of a user_LThe method can comprise the following steps:

step S81: using body smoothness B before movement_A1Heart rate H_R1Respiratory rate B_R1Respiratory intensity B_I1And body smoothness after exercise B_A2Heart rate H_R2Respiratory rate B_R2Respiratory intensity B_I2Calculating before and after movement respectivelyDifference of physiological parameters (body smoothness difference B)_AHeart rate difference H_RRespiratory rate difference B_RAnd the difference B of the respiratory intensity_I)。

Step S82: using a weighted average algorithm based on the body smoothness difference B_AHeart rate difference H_RRespiratory rate difference B_RAnd the difference B of the respiratory intensity_IAnd calculating a heart recovery index R by combining the motion type s, the motion intensity m, the motion duration T, the time after motion T and the motion parameter confidence level w_L。

Specifically, R_L＝c₁*B_A+c₂*H_R+c₃*B_R+c₄*B_I+c₅*s+c₆*m+c₇*t+c₈*T+c₉*w；

Wherein, c₁，c₂，c₃，c₄，c₅，c₆，c₇，c₈，c₉The coefficient can be flexibly set according to actual requirements.

It can be understood that the embodiment of the invention represents the physiological performance change before and after the exercise by calculating the difference value of the physiological parameters such as the body stability, the heart rate, the respiratory intensity and the like before and after the exercise, and then calculates the muscle fatigue, the heart recovery capacity and the lung recovery capacity of the user after the exercise by combining the exercise parameters and using a weighted average algorithm, so that the accuracy is high and the calculation is simple and convenient.

The second method comprises the following steps: through a neural network.

The method comprises the steps of constructing a neural network, training the constructed neural network to obtain a trained neural network, and then performing muscle fatigue F according to the trained neural network_MCardiac recovery index R_HAnd cardiac recovery index R_L。

The training set when training the neural network model may include an input training set and an input training set: wherein inputting the training set may include: difference of physiological parameters before and after movement of user (body smoothness difference B)_AHeart rate difference H_RRespiratory rate difference B_RTo callDifference in absorption strength B_I) And motion parameters (motion type s, motion intensity m, motion duration T, time after motion T, motion parameter confidence level w); outputting the training set may include: degree of muscular fatigue F_MHeart recovery R_HAnd lung recovery capacity R_LWherein, the muscle fatigue FM can be measured by adopting a blood lactic acid method or an electromyography. Heart recovery capacity R_HAnd lung recovery capacity R_LCan be measured by a motion tester.

Training the neural network to obtain the calculation parameters of the neural network for new input data, and calculating the muscle fatigue degree F by using the parameters in the neural network_MHeart recovery R_HLung-recovering ability R_L。

FIG. 8 is a diagram illustrating a method for calculating muscle fatigue F through a neural network according to an embodiment of the present invention_MCardiac recovery index R_HAnd lung recovery index R_LA schematic diagram of (a);

as shown in fig. 8, the input information of the trained neural network may include: difference of physiological parameters before and after movement of user (body smoothness difference B)_AHeart rate difference H_RRespiratory rate difference B_RRespiratory intensity difference B_I) And motion parameters (motion type s, motion intensity m, motion duration T, time after motion T, motion parameter confidence w).

The trained neural network can calculate the muscle fatigue degree F through the input information_MCardiac recovery index R_HAnd lung recovery index R_LAnd output.

It can be understood that the embodiment of the invention calculates the muscle fatigue degree F by adopting the neural network_MCardiac recovery index R_HAnd lung recovery index R_LWith the development of exercise health and wearable equipment, measurable human physiological parameters can be gradually increased, and multi-parameter fusion can be effectively carried out by using a neural network to obtain deeper physiological parameters.

The following describes an interface interaction process of the device for detecting muscle fatigue after exercise according to the embodiment of the present invention with reference to the accompanying drawings.

the user may select the state of the user through the state selection interface, whether the state is a resting state before the exercise or a state after the exercise, and if the user is the resting state before the exercise, the state may be selected as [ still ] in fig. 9, and if the user is the state after the exercise, the state may be selected as [ after the exercise ] in fig. 9.

the setting interface for automatically acquiring the motion parameters is used for showing whether the motion parameters are automatically acquired from the exercise health App or not to a user, and the motion parameters comprise one or more of motion type s, motion intensity m, motion duration T, time after motion T and motion parameter credibility w.

If the user selects [ yes ] in fig. 10, the detection means of muscle fatigue after exercise will be set to obtain the exercise parameters from the exercise health App.

If the detection device of the muscle fatigue degree after exercise does not detect exercise data, a manual input selection interface as shown in fig. 11 may be presented to the user, and if the user selects [ no ] in fig. 10 or [ yes ] in fig. 11, a motion parameter input interface as shown in fig. 12 may be presented to the user, and the user may manually input or set a motion parameter and the like through the interface, and specifically, the user may select or input a motion type s, a motion intensity m and/or a motion duration T, and a time after exercise (motion ending time) T.

Specifically, the user may be allowed to select an exercise type such as running, swimming, squatting, basketball, etc., and a time after exercise (exercise end time) T using a pull-down menu, and, when the user selects the exercise type, the user is allowed to select or input the exercise intensity/duration of the exercise type selected by the user according to the exercise type selected by the user. For example, if the exercise type selected by the user is running/swimming, a menu is developed that lets the user select or set the length of the running session. If the type of exercise selected by the user is squat, a menu is expanded to allow the user to select or set the number of squats, as can be seen in particular in fig. 12 and 13, fig. 12 and 13 providing schematic diagrams of two exercise parameter input interfaces.

as shown in fig. 14, the measurement result display interface shows to the user: weight, heart rate H of the user_R2Cardiac recovery index R_HRespiratory rate B_R2Respiratory intensity B_I2Lung recovery index R_HBody stability B_A2Muscle fatigue degree F_MAnd physical recovery and exercise advice.

Of course, fig. 14 is only an exemplary embodiment, and in other embodiments, only the weight and heart rate H of the user may be shown_R2Cardiac recovery index R_HRespiratory rate B_R2Respiratory intensity B_I2Lung recovery index R_HBody stability B_A2Muscle fatigue degree F_MAs well as part of the information in the body recovery and movement advice or may also present other information to the user.

It can be understood that the embodiment of the invention is convenient for the user to know the current body state in a man-machine interaction mode by displaying the detection result to the user, and provides guidance on movement and body recovery for the user, so that the user can be prevented from performing exercise training with unreasonable intensity and time and can be guided to recover the body function quickly.

The embodiment of the present application further discloses a device for detecting muscle fatigue after exercise, where the device for detecting muscle fatigue after exercise may be the above weight measuring device, and may also be the above terminal device, and it should be understood that the device 400 can perform each step in the method for detecting muscle fatigue after exercise, and in order to avoid repetition, detailed description is omitted here. As shown in fig. 14, the apparatus 400 includes: a first obtaining module 410 and a first calculating module 420.

A first obtaining module 410, configured to obtain body state parameters of a user before exercise and body state parameters of the user after exercise, where the body state parameters include body smoothness, heart rate, respiratory rate, and respiratory intensity; and

the first calculating module 420 is configured to calculate muscle fatigue of the user after exercise according to the physical state parameter of the user before exercise and the physical state parameter of the user after exercise.

In one possible implementation, the first obtaining module 410 may include:

the first acquisition unit is used for receiving a first pressure signal generated by the weight measuring equipment and determining a body state parameter of the user before movement according to the first pressure signal, wherein the first pressure signal is generated by the weight measuring equipment when the weight measuring equipment is used for measuring the weight of the user before movement; and

and the second acquisition unit is used for receiving a second pressure signal generated by the weight measuring equipment and determining the body state parameter of the user after movement according to the second pressure signal, wherein the second pressure signal is generated by the weight measuring equipment when the weight measuring equipment is used for measuring the weight of the user after movement.

In one possible implementation, the first obtaining module 410 may include:

the first determination unit is used for measuring the weight of the user before the user exercises, generating a first pressure signal according to the pressure applied by the user and determining the body state parameter of the user before the user exercises according to the first pressure signal; and

and the second determination unit is used for measuring the weight of the user after the user exercises, generating a second pressure signal according to the pressure applied by the user and determining the physical state parameter of the user after the user exercises according to the second pressure signal.

In one possible implementation, the apparatus 400 may further include:

the second acquisition module is used for acquiring motion parameters corresponding to the motion of the user; and

In one possible implementation, the first calculation module 410 may include:

the first calculating unit is used for calculating the muscle fatigue degree of the user after the user moves according to the body state parameters of the user before the user moves, the body state parameters of the user after the user moves and the motion parameters of the user.

In a possible implementation manner, the motion parameter includes one or any of a motion type, a motion intensity, a motion duration, a time duration after motion, and a motion parameter reliability.

In a possible implementation manner, the first obtaining module or the first determining module may include:

the first processing unit is used for sequentially carrying out high-pass amplification processing and low-pass filtering processing on the first pressure signal to obtain a first signal, carrying out high-pass filtering processing on the first signal to obtain a pre-exercise heart impact signal, determining a pre-exercise heart rate according to the pre-exercise heart impact signal, determining a pre-exercise heart impact signal waveform profile according to the pre-exercise heart impact signal waveform change, calculating a pre-exercise respiratory frequency according to peak characteristic points of the pre-exercise heart impact signal waveform profile, and calculating a pre-exercise respiratory intensity according to peak values of the pre-exercise heart impact signal waveform profile; and

In a possible implementation manner, the second obtaining module or the second determining module may include:

the third processing unit is used for sequentially carrying out high-pass amplification processing and low-pass filtering processing on the second pressure signal to obtain a third signal, carrying out high-pass filtering processing on the third signal to obtain a moved impact cardiac signal, determining the heart rate after movement according to the moved impact cardiac signal, determining the waveform profile of the moved impact cardiac signal according to the waveform change of the moved impact cardiac signal, calculating the breathing frequency after movement according to the peak characteristic point of the waveform profile of the moved impact cardiac signal and calculating the breathing intensity after movement according to the peak value of the waveform profile of the moved impact cardiac signal; and

and the fourth processing unit is used for performing low-pass filtering processing on the third signal to obtain a fourth signal, calculating the main frequency, the peak-to-peak value and the standard deviation of the fourth signal, and calculating the body stability after movement according to the main frequency, the peak-to-peak value and the standard deviation of the fourth signal.

In one possible implementation, the first calculation module 420 may include:

the second calculation unit is used for calculating a body smoothness difference value between the body smoothness of the user before movement and the body smoothness of the user after movement, calculating a heart rate difference value between the heart rate of the user before movement and the heart rate of the user after movement, calculating a respiratory rate difference value between the respiratory rate of the user before movement and the respiratory rate of the user after movement, and calculating a respiratory intensity difference value between the respiratory intensity of the user before movement and the respiratory intensity of the user after movement; and

In one possible implementation, the second computing unit may include:

and the first calculating subunit is used for calculating the heart recovery index and the lung recovery index of the user after movement by adopting a weighted average algorithm according to the body smoothness difference value, the heart rate difference value, the respiratory rate difference value and the respiratory intensity difference value and combining the movement parameters of the user.

In one possible implementation, the first computing unit may include:

the second calculating subunit is used for calculating a body smoothness difference value between the body smoothness of the user before the movement and the body smoothness of the user after the movement, calculating a heart rate difference value between the heart rate of the user before the movement and the heart rate of the user after the movement, calculating a respiratory rate difference value between the respiratory rate of the user before the movement and the respiratory rate of the user after the movement, and calculating a respiratory intensity difference value between the respiratory intensity of the user before the movement and the respiratory intensity of the user after the movement; and

and the third calculation subunit is used for calculating the muscle fatigue degree, the heart recovery index and the lung recovery index of the user by using the neural network according to the body smoothness difference value, the heart rate difference value, the respiratory rate difference value and the respiratory intensity difference value and by combining the motion parameters of the user.

In one possible implementation, the apparatus 400 may further include:

the first display module is used for displaying any one or more of the muscle fatigue, the heart recovery index and the lung recovery index of the user, which are obtained through calculation, and generating and displaying one or more of the motion quantity evaluation, the body function evaluation, the motion suggestion and the body recovery suggestion according to the muscle fatigue, the heart recovery index and the lung recovery index of the user.

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 implement the method for detecting muscle fatigue after exercise.

The application also provides a weight measuring device, which comprises a memory and a processor, wherein the memory stores a computer program, the processor is connected with the memory, and the processor executes the computer program to realize the method for detecting the muscle fatigue after exercise.

The present application further provides a computer storage medium comprising computer instructions which, when run on the electronic device 100 or the weight measuring device, cause the electronic device 100 or the weight measuring device to perform the steps of the method for detecting muscle fatigue after exercise as described above.

The present application also provides a computer program product, which when run on a computer causes the computer to perform the steps of the above-described method for detecting muscle fatigue after exercise.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

The above embodiments are only specific embodiments of the present application, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present disclosure, and all the changes or substitutions should be covered by the protection scope of the present application. The protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for detecting muscle fatigue after exercise, the method comprising:

2. The method of claim 1, wherein the obtaining of the physical state parameters of the user before the movement comprises:

3. The method of claim 1, wherein the obtaining of the physical state parameters of the user before the movement comprises:

4. The method of claim 1, further comprising:

acquiring a motion parameter corresponding to the user motion;

5. The method of claim 4, wherein 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:

6. The method according to claim 4 or 5, wherein the motion parameters comprise one or any more of motion type, motion intensity, motion duration, post-motion duration and motion parameter reliability.

7. The method according to claim 2 or 3, wherein said determining a physical state parameter of the user prior to exercise from the first pressure signal comprises:

8. The method according to claim 2 or 3, wherein said determining the physical state parameter of the user after the movement from the second pressure signal comprises:

9. The method of claim 4, wherein 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:

10. The method of claim 9, wherein calculating the heart recovery index and the lung recovery index after the exercise of the user according to the physical state parameter of the user before the exercise, the physical state parameter of the user after the exercise, and the exercise parameter of the user comprises:

11. The method of claim 5, wherein calculating the muscle fatigue, heart recovery index and lung recovery index of the user according to the physical state parameter of the user before exercise, the calculation of the physical state parameter of the user after exercise and the exercise parameter of the user comprises:

12. The method according to claim 4 or 5, characterized in that the method further comprises:

13. A device for detecting muscle fatigue after exercise, the device comprising:

14. The apparatus for detecting muscle fatigue after exercise according to claim 13, wherein the first obtaining module comprises:

15. The apparatus for detecting muscle fatigue after exercise according to claim 13, wherein the first obtaining module comprises:

16. The apparatus for detecting muscular fatigue after exercise according to claim 13, further comprising:

17. The apparatus for detecting muscle fatigue after exercise according to claim 14, wherein the first calculating means comprises:

18. The apparatus for detecting muscle fatigue after exercise according to claim 16 or 17, wherein the exercise parameters include one or any more of exercise type, exercise intensity, exercise duration after exercise, and exercise parameter reliability.

19. The apparatus for detecting muscle fatigue after exercise according to claim 14 or 15, wherein the first acquiring means or the first determining means comprises:

20. The apparatus for detecting muscle fatigue after exercise according to claim 14 or 15, wherein the second acquiring means or the second determining means comprises:

21. The apparatus for detecting muscle fatigue after exercise according to claim 16, wherein the first calculating means comprises:

22. The apparatus for detecting post-exercise muscle fatigue according to claim 21, wherein the second calculation unit includes:

23. The apparatus for detecting post-exercise muscle fatigue according to claim 17, wherein the first calculation unit includes:

24. The apparatus for detecting muscular fatigue after exercise according to claim 15 or 17, further comprising:

25. An electronic device, comprising a memory and a processor, wherein the memory stores a computer program, the processor is connected with 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.

26. A weight measuring device, comprising a memory and a processor, wherein the memory stores a computer program, the processor is connected with the memory, and the processor executes the computer program to realize the method for detecting muscle fatigue after exercise according to any one of claims 1 to 12.

27. A computer storage medium comprising computer instructions which, when run on an electronic device, cause the electronic device to perform the steps of the method of detecting post-exercise muscle fatigue of any one of claims 1-12.

28. A computer program product, characterized in that it causes a computer to carry out the steps of the method for detecting post-exercise muscle fatigue according to any one of claims 1 to 12, when said computer program product is run on a computer.