WO2020087364A1

WO2020087364A1 - Method and device for evaluating exercise index

Info

Publication number: WO2020087364A1
Application number: PCT/CN2018/113086
Authority: WO
Inventors: 陈霄汉; 王宇; 李祥臣; 孙启政
Original assignee: 华为技术有限公司
Priority date: 2018-10-31
Filing date: 2018-10-31
Publication date: 2020-05-07
Also published as: CN112188864A

Abstract

A method and device for evaluating an exercise index, the method comprising: obtaining at least one exercise feature parameter of a user during exercise (S301); obtaining a score value of each exercise feature parameter during exercise according to a scoring model of each exercise feature parameter (S302); and bringing the score value of the exercise feature parameter into a pre-designed calculation expression of an exercise index to obtain an evaluation result of the exercise index (S303). The method and device for evaluating an exercise index cause the exercise feature parameter to play a greater role and improve user experience.

Description

Sports index evaluation method and device

Technical field

The present application relates to the field of electronic equipment, in particular to a method and device for evaluating sports indexes.

Background technique

Nowadays, with the rapid development of technology, more and more electronic devices can measure the movement characteristics of users. For example, electronic devices such as wearable devices and mobile phones can measure the running characteristics, such as the user's running time, moving distance, step length, step frequency, leg swing angle, ground contact time, and left and right foot balance.

At present, after acquiring motion feature parameters, the electronic device presents these parameters to the user. How to use these parameters measured by electronic equipment has become an urgent problem to be solved.

Summary of the invention

Embodiments of the present application provide an exercise index evaluation method and device, which realize the use of exercise characteristic parameters to evaluate user exercise indexes.

To achieve the above purpose, the embodiments of the present application adopt the following technical solutions:

In a first aspect, a method for evaluating an exercise index is provided, which includes: acquiring at least one movement feature parameter of a user during exercise; obtaining a score value of each exercise feature parameter during the exercise according to a scoring model of each exercise feature parameter; The score value of the sports feature parameter is substituted into the preset calculation expression of the sports index to obtain the evaluation result of the sports index.

Through the sports index evaluation method provided in this application, the sports index is used to evaluate the sports index to obtain the sports index evaluation result. On the one hand, the sports feature parameters play a greater role, on the other hand, the evaluation of sports indicators can be applied to all aspects, improving the user experience.

With reference to the first aspect, in a possible implementation manner, the exercise index may include: exercise ability, or, exercise efficiency, or risk of injury. Among them, the exercise index is the embodiment of the ability of the user in all aspects of exercise. Of course, in addition to the enumeration here, there may be other types of sports indicators, which are not enumerated here one by one.

With reference to the first aspect or any one of the foregoing possible implementation manners, in another possible implementation manner, the exercise index evaluation method provided by the present application may further include: displaying exercise suggestions to the user, where the exercise suggestions are in preset exercise suggestions The database corresponds to the evaluation result of the sports index. By providing users with motion suggestions, the user experience is improved.

With reference to the first aspect or any one of the foregoing possible implementation manners, in another possible implementation manner, according to the scoring model of each sports feature parameter, the scoring value of each sports feature parameter is obtained, which may be specifically implemented as: The average value of at least one motion feature parameter of the user under each action, respectively input the average value of the at least one motion feature parameter into the respective scoring model of each sport feature parameter to obtain the score value of each sport feature parameter. In this implementation, the obtained motion feature parameters are averaged before being substituted into the model score.

With reference to the first aspect or any one of the foregoing possible implementation manners, in another possible implementation manner, according to the scoring model of each sports feature parameter, the scoring value of each sports feature parameter is obtained, which can be specifically implemented as: The user enters at least one sport feature parameter under each action into the respective scoring model of each sport feature parameter to obtain the score value of each sport feature parameter under each action; calculate the average value of the score value of each sport feature parameter To get the score value of each motion feature parameter. In this implementation, each motion feature parameter is first substituted into the model score, and then the score value is averaged.

With reference to the first aspect or any one of the foregoing possible implementation manners, in another possible implementation manner, the sports described in this application may include running or walking. Of course, other sports forms are also possible, which is not specifically limited in this application.

With reference to the first aspect or any one of the foregoing possible implementation manners, in another possible implementation manner, the motion characteristic parameters may include at least one of the following parameters: step length, step frequency, leg swing angle, and ground contact time , Left and right foot balance, vacating time, ground impact, vertical movement distance.

With reference to the first aspect or any one of the foregoing possible implementation manners, in another possible implementation manner, the motion feature parameter may include: a composite feature parameter. Among them, the compound feature parameter is defined as calculated by at least one conventional motion feature parameter. This application does not specifically limit the content of the composite feature parameters.

With reference to the first aspect or any one of the foregoing possible implementation manners, in another possible implementation manner, a preset calculation expression of a sports index may include: a weighted sum of score values of sports feature parameters. The weighted sum of the score values of the sports feature parameters related to the sports index may be defined as the preset calculation expression of the sports index. For the selection of the sports characteristic parameters related to the sports index, it can be selected according to the actual needs, which is not specifically limited in this application.

Among them, the sum of the weight coefficients in the preset calculation expression is equal to 1. However, this application does not specifically limit the value of each weighting system.

With reference to the first aspect or any one of the foregoing possible implementation manners, in another possible implementation manner, the sports index evaluation method provided by the present application may further include: establishing a scoring model for each sports feature parameter.

It should be noted that this application does not specifically limit the process of establishing a scoring model, and any method that can be used for modeling can be applied here.

With reference to the first aspect or any one of the foregoing possible implementation manners, in another possible implementation manner, the exercise index evaluation method provided by the present application may further include: periodically updating according to the exercise characteristic parameters acquired during exercise The scoring model of each motion feature parameter.

In a second aspect, a device for evaluating an exercise index is provided. The device may include an acquisition unit, a scoring unit, and an evaluation unit. Among them, the obtaining unit is used to obtain at least one motion feature parameter of the user during the exercise; the scoring unit is used to obtain the scoring value of each sport feature parameter according to the scoring model of each sport feature parameter; the evaluation unit is used to The score value of the sports feature parameter acquired by the scoring unit is substituted into the preset calculation expression of the sports index to obtain the evaluation result of the sports index.

Through the exercise index evaluation device provided by the present application, the exercise index is used to evaluate the exercise index to obtain the exercise index evaluation result. On the one hand, the sports feature parameters play a greater role, on the other hand, the evaluation of sports indicators can be applied to all aspects, improving the user experience.

With reference to the second aspect, in a possible implementation manner, the exercise index may include: exercise ability, or, exercise efficiency, or, risk of injury.

With reference to the second aspect or any one of the foregoing possible implementation manners, in another possible implementation manner, the obtaining unit may be further configured to obtain a motion recommendation corresponding to the evaluation result of the motion index in the preset motion recommendation database; the The device may further include a display unit for displaying to the user the motion suggestions acquired by the acquisition unit.

With reference to the second aspect or any one of the foregoing possible implementation manners, in another possible implementation manner, the scoring unit may be specifically used to include: the average value of at least one motion feature parameter of the user under each action, Enter the respective scoring model of each sports feature parameter to obtain the scoring value of each sports feature parameter; or, enter at least one sports feature parameter of the user under each action into the respective scoring model of each sports feature parameter to obtain The score value of each sport feature parameter under each action; the average value of the score value of each sport feature parameter is calculated to obtain the score value of each sport feature parameter.

With reference to the second aspect or any one of the foregoing possible implementation manners, in another possible implementation manner, the exercise may include running or walking.

With reference to the second aspect or any one of the foregoing possible implementation manners, in another possible implementation manner, the motion characteristic parameter may include at least one of the following parameters: step length, step frequency, leg swing angle, and ground contact time , Left and right foot balance, vacating time, ground impact, vertical movement distance.

It should be noted that the exercise index evaluation device provided in the second aspect is used to execute the exercise index evaluation method provided in the first aspect or any possible implementation manner. For specific implementation, reference may be made to the first aspect or the first aspect. A possible implementation manner will not be repeated here.

In a third aspect, an embodiment of the present application provides an exercise index evaluation device that can implement the corresponding function in the method example of the first aspect described above. The function may be implemented by hardware, or may be implemented by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions.

With reference to the third aspect, in a possible implementation manner, the structure of the device includes a processor and a transceiver, and the processor is configured to support the device to perform the corresponding function in the foregoing method. The transceiver is used to support communication between the device and other devices. The device may also include a memory for coupling with the processor, which stores necessary program instructions and data of the device.

According to a fourth aspect, an electronic device is provided, including the exercise index evaluation device described in any one of the above aspects or any possible implementation manner.

According to a fifth aspect, a computer-readable storage medium is provided, which includes instructions that, when run on a computer, cause the computer to execute the exercise index evaluation method described in the first aspect or any possible implementation manner described above.

In a sixth aspect, a computer program product is provided, which when run on a computer, causes the computer to execute the exercise index evaluation method described in the first aspect or any possible implementation manner.

The solutions provided in the third aspect to the sixth aspect are used to implement the sports index evaluation method provided in the first aspect or any possible implementation manner, and therefore can achieve the same beneficial effects as the first aspect, and will not be repeated here Repeat.

BRIEF DESCRIPTION

1 is a schematic structural diagram of a mobile phone provided by an embodiment of the present application;

2 is a schematic structural diagram of an exercise index evaluation device provided by an embodiment of the present application;

FIG. 3 is a schematic flowchart of an exercise index evaluation method provided by an embodiment of the present application;

4 is a schematic flowchart of a modeling method for constructing a running feature parameter scoring model provided by an embodiment of the present application;

FIG. 5 is a schematic diagram of a step frequency histogram distribution provided by an embodiment of the present application;

6 is a schematic structural diagram of another exercise index evaluation device provided by an embodiment of the present application;

7 is a schematic structural diagram of yet another exercise index evaluation device provided by an embodiment of the present application.

detailed description

In the embodiments of the present application, words such as "exemplary" or "for example" are used as examples, illustrations or explanations. Any embodiments or design solutions described as “exemplary” or “for example” in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or design solutions. Rather, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific manner.

In the following, the terms "first" and "second" are used for description purposes only, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present application, unless otherwise stated, the meaning of "plurality" is two or more.

Nowadays, there are many electronic devices that support motion measurement, and they can measure the motion process of users who use the electronic device by themselves or configure motion software to obtain motion characteristic parameters. For example, an electronic device that measures running can measure the user's running time, distance, step length, cadence, leg swing angle, ground contact time, left and right foot balance, and other running characteristics.

Based on this, the present invention provides an exercise index evaluation method, which uses the measured exercise feature parameters to evaluate the exercise index based on the exercise feature parameters.

The sports index evaluation method provided in the embodiments of the present application is applied to a sports index evaluation device. The exercise index evaluation method is applied to the sports health field, and the exercise index evaluation device may be an electronic device, and the electronic device is applied to the exercise health field to execute the exercise index evaluation method.

In a possible implementation, the exercise index evaluation device may be an electronic device supporting exercise measurement, a function module of the electronic device supporting exercise measurement, or an application (Application, APP) in the electronic device supporting exercise measurement ), This embodiment of the present application does not specifically limit this.

The aforementioned electronic device may be a mobile phone (phone 100 shown in FIG. 1), a tablet computer, a personal computer (personal computer (PC)), a personal digital assistant (personal digital assistant) that allows a user to input a processing operation and instruct the electronic device to perform related operation events, PDA), smart watches, netbooks, wearable electronic devices, etc. The embodiments of the present application do not specifically limit the specific form of the electronic device.

As shown in FIG. 1, the mobile phone 100 is taken as an example of the above electronic device. The mobile phone 100 may specifically include: a processor 101, a radio frequency (RF) circuit 102, a memory 103, a touch screen 104, a Bluetooth device 105, one or more sensors 106, a wireless fidelity (Wi-Fi) device 107 , Positioning device 108, audio circuit 109, peripheral interface 110 and power supply device 111 and other components. These components can communicate through one or more communication buses or signal lines (not shown in FIG. 1).

Those skilled in the art may understand that the hardware structure shown in FIG. 1 does not constitute a limitation on the mobile phone, and the mobile phone 100 may include more or fewer components than those illustrated, or combine certain components, or arrange different components.

The following describes the components of the mobile phone 100 in detail with reference to FIG. 1:

The processor 101 is the control center of the mobile phone 100, and uses various interfaces and lines to connect various parts of the mobile phone 100, and executes the mobile phone 100 by running or executing application programs stored in the memory 103 and calling data stored in the memory 103. Various functions and processing data. In some embodiments, the processor 101 may include one or more processing units.

In the embodiment of the present application, the processor 101 is specifically configured to: acquire at least one movement feature parameter of the user in each action during the movement; acquire each movement feature parameter during the movement according to the scoring model of each movement feature parameter The score value of the sports characteristic parameter is substituted into the preset calculation expression of the sports index to obtain the evaluation result of the sports index.

The radio frequency circuit 102 can be used for receiving and sending wireless signals during the process of receiving and sending information or talking. In particular, the radio frequency circuit 102 may receive the downlink data of the base station and process it to the processor 101; in addition, send the data related to the uplink to the base station. Generally, the radio frequency circuit includes but is not limited to an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like. In addition, the radio frequency circuit 102 can also communicate with other devices through wireless communication. The wireless communication may use any communication standard or protocol, including but not limited to global mobile communication system, general packet radio service, code division multiple access, broadband code division multiple access, long-term evolution, e-mail, and short message service.

The memory 103 is used to store application programs and data, and the processor 101 executes various functions and data processing of the mobile phone 100 by running the application programs and data stored in the memory 103. The memory 103 mainly includes a storage program area and a storage data area, wherein the storage program area can store an operating system and at least one function required application programs (such as sound playback function, image processing function, etc.); Data created at 100 (such as audio data, phone book, etc.). In addition, the memory 103 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as a disk storage device, a flash memory device, or other volatile solid-state storage devices. The memory 103 may store various operating systems, for example,

operating system,

Operating system, etc. The memory 103 may be independent and connected to the processor 101 through the communication bus; the memory 103 may also be integrated with the processor 101.

In the embodiment of the present application, the memory 103 may be used to store a scoring model of each motion feature parameter.

The touch screen 104 may specifically include a touch panel 104-1 and a display 104-2.

Among them, the touchpad 104-1 can collect touch events on or near the user of the mobile phone 100 (such as the user using a finger, a stylus, or any other suitable object on the touchpad 104-1 or on the touchpad 104 -1 near operation), and send the collected touch information to other devices (such as the processor 101). Among them, the user's touch event near the touchpad 104-1 can be called floating touch; floating touch can mean that the user does not need to directly touch the touchpad in order to select, move, or drag a target (such as an icon, etc.) , And only need the user to be near the device to perform the desired function. In addition, the touch panel 104-1 may be implemented in various types such as resistive, capacitive, infrared, and surface acoustic waves.

The display (also referred to as a display screen) 104-2 may be used to display information input by the user or provided to the user and various menus of the mobile phone 100. The display 104-2 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like. The touchpad 104-1 can be overlaid on the display 104-2. When the touchpad 104-1 detects a touch event on or near it, it is transmitted to the processor 101 to determine the type of touch event, and then the processor 101 may provide corresponding visual output on the display 104-2 according to the type of touch event.

Although in FIG. 1, the touchpad 104-1 and the display screen 104-2 are used as two independent components to realize the input and output functions of the mobile phone 100, in some embodiments, the touchpad 104- 1 Integrated with the display screen 104-2 to realize the input and output functions of the mobile phone 100. It can be understood that the touch screen 104 is formed by stacking multiple layers of materials. Only the touch panel (layer) and the display screen (layer) are shown in the embodiments of the present application, and other layers are not described in the embodiments of the present application. . In addition, the touchpad 104-1 can be arranged on the front of the mobile phone 100 in the form of a full board, and the display screen 104-2 can also be arranged on the front of the mobile phone 100 in the form of a full board, so that no border can be realized on the front of the phone Structure.

In addition, the mobile phone 100 may also have a fingerprint recognition function. For example, the fingerprint reader 112 may be disposed on the back of the mobile phone 100 (for example, below the rear camera), or on the front of the mobile phone 100 (for example, below the touch screen 104). For another example, the fingerprint collection device 112 may be configured in the touch screen 104 to implement the fingerprint recognition function, that is, the fingerprint collection device 112 may be integrated with the touch screen 104 to implement the fingerprint recognition function of the mobile phone 100. In this case, the fingerprint collecting device 112 is configured in the touch screen 104, and may be a part of the touch screen 104, or may be configured in the touch screen 104 in other ways. The main component of the fingerprint collecting device 112 in the embodiment of the present application is a fingerprint sensor, which may use any type of sensing technology, including but not limited to optical, capacitive, piezoelectric, or ultrasonic sensing technology.

The mobile phone 100 may also include a Bluetooth device 105 for implementing data exchange between the mobile phone 100 and other short-range devices (such as mobile phones, smart watches, etc.). The Bluetooth device in the embodiment of the present application may be an integrated circuit or a Bluetooth chip.

The mobile phone 100 may also include at least one sensor 106, such as a light sensor, a motion sensor, and other sensors. Specifically, the motion sensor is used to measure the motion characteristic parameters of the user of the mobile phone 100 during the motion. As a type of motion sensor, the accelerometer sensor can detect the magnitude of acceleration in various directions (generally three axes), and can detect the magnitude and direction of gravity when at rest, and can be used to identify mobile phone gesture applications (such as horizontal and vertical screen switching, related Games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tap), etc. The light sensor may include an ambient light sensor and a proximity sensor. The ambient light sensor may adjust the brightness of the display of the touch screen 104 according to the brightness of the ambient light. The proximity sensor may turn off the power of the display when the mobile phone 100 moves to the ear. As for other sensors such as a gyroscope, barometer, hygrometer, thermometer, infrared sensor, etc., which can also be configured on the mobile phone 100, they will not be repeated here.

The Wi-Fi device 107 is used to provide the mobile phone 100 with network access following Wi-Fi related standard protocols. The mobile phone 100 can be connected to a Wi-Fi access point through the Wi-Fi device 107 to help users send and receive emails, Browse web pages and access streaming media, etc., it provides users with wireless broadband Internet access. In some other embodiments, the Wi-Fi device 107 may also serve as a Wi-Fi wireless access point, and may provide Wi-Fi network access for other devices.

The positioning device 108 is used to provide a geographic location for the mobile phone 100. It can be understood that the positioning device 108 may specifically be a receiver of a positioning system such as a global positioning system (GPS), a Beidou satellite navigation system, or Russian GLONASS. After receiving the geographical position sent by the positioning system, the positioning device 108 sends the information to the processor 101 for processing, or sends it to the memory 103 for storage. In some other embodiments, the positioning device 108 may also be a receiver of an assisted global positioning system (AGPS). The AGPS system assists the positioning device 108 to complete ranging and positioning services by acting as an auxiliary server. In this case, the auxiliary positioning server communicates with the positioning device 108 (ie, GPS receiver) of the device, such as the mobile phone 100, through a wireless communication network to provide positioning assistance. In some other embodiments, the positioning device 108 may also be a positioning technology based on Wi-Fi access points.

Since each Wi-Fi access point has a globally unique media access control (MAC) address, the device can scan and collect the surrounding Wi-Fi access points when Wi-Fi is turned on Broadcast signal, so you can get the MAC address broadcast by the Wi-Fi access point; the device sends these data (such as MAC address) that can mark the Wi-Fi access point to the location server through the wireless communication network, and the location server retrieves it Calculate the geographic location of each Wi-Fi access point and combine the strength of the Wi-Fi broadcast signal to calculate the geographic location of the device and send it to the positioning device 108 of the device.

In the embodiment of the present application, the processor 101 can measure the motion feature parameters related to the distance and the position through the positioning device 108.

The audio circuit 109, the speaker 113, and the microphone 114 may provide an audio interface between the user and the mobile phone 100. The audio circuit 109 can transmit the converted electrical signal of the received audio data to the speaker 113, which converts the speaker 113 into a sound signal for output; on the other hand, the microphone 114 converts the collected sound signal into an electrical signal, which is converted by the audio circuit 109 After receiving, it is converted into audio data, and then the audio data is output to the RF circuit 102 to be sent to, for example, another mobile phone, or the audio data is output to the memory 103 for further processing.

The peripheral interface 110 is used to provide various interfaces for external input / output devices (such as a keyboard, a mouse, an external display, an external memory, a user identification module card, etc.). For example, it is connected to the mouse through a Universal Serial Bus (USB) interface, and connected to the subscriber identification module (SIM) card provided by the telecommunications operator through the metal contacts on the card slot of the user identification module card . The peripheral interface 110 may be used to couple the above-mentioned external input / output peripheral devices to the processor 101 and the memory 103.

In the embodiment of the present application, the mobile phone 100 can communicate with other devices in the device group through the peripheral interface 110, for example, the display data sent by other devices can be received through the peripheral interface 110 for display, etc. No restrictions.

The mobile phone 100 may further include a power supply device 111 (such as a battery and a power management chip) that supplies power to various components. The battery can be logically connected to the processor 101 through the power management chip, so that the power supply device 111 can manage charging, discharging, and power consumption management. And other functions.

Although not shown in FIG. 1, the mobile phone 100 may further include a camera (front camera and / or rear camera), a flash, a micro-projection device, a near field communication (NFC) device, etc., which will not be repeated here.

In a possible implementation, the exercise index evaluation device provided in this application may be a functional unit. As shown in FIG. 2, the exercise index evaluation device 20 may specifically include a sensor 201 that samples data, a calculation unit 202 that performs calculation, and a storage unit 203 that stores data.

The following describes each component of the exercise index evaluation device 20 with reference to FIG. 2:

The sensor 201 may be a speedometer, gyroscope, magnetometer, or other device with a collection function, which is not specifically limited in the embodiments of the present application.

The storage unit 203 may be a volatile memory (volatile memory), such as random-access memory (RAM); or a non-volatile memory (non-volatile memory), such as read-only memory (read-only) memory, ROM), flash memory (flash memory), hard disk (hard disk drive) or solid-state drive (SSD); or a combination of the above types of memory, used to store the memory Program code and configuration file. For example, the storage unit 203 stores a scoring model, a sports recommendation database, and the like.

The calculation unit 202 is the control center of the exercise index evaluation device 20, and may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or configured to implement the application One or more integrated circuits of the embodiment, for example, one or more microprocessors (digital processor), or one or more field programmable gate arrays (FPGA).

As shown in FIG. 2, the exercise index evaluation device 20 may further include a display unit 204 for displaying exercise suggestions to the user.

The calculation unit 202 performs the following functions: acquiring at least one movement feature parameter of the user during the exercise; acquiring the score value of each movement feature parameter in the exercise process according to the scoring model of each movement feature parameter; , Substitute the preset calculation expression of the sports index to get the assessment result of the sports index.

Exemplarily, each unit included in the exercise index evaluation apparatus 20 may be deployed in one physical device, or may be separately deployed in multiple devices, which is not specifically limited in this embodiment of the present application.

In a possible implementation, the sensor 201, the calculation unit 202, the storage unit 203, and the display unit 204 are all deployed in an electronic device, which may be a wearable device or a mobile phone.

In a possible implementation, the sensor 201 may be deployed in a wearable device or a mobile phone, the display unit 204 may be deployed in a mobile phone or a wearable device, and the computing unit 202 and the storage unit 203 are deployed in a remote server.

In a possible implementation, the sensor 201, the storage unit 203, and the display unit 204 may be deployed in a wearable device or a mobile phone, and the computing unit 202 is deployed in a remote server.

The sports index evaluation method provided in this application will be described in detail below.

The sports index evaluation method provided in the embodiments of the present application is applied to a sports index evaluation device. The apparatus may be an electronic device, or a functional module or chip in the electronic device that executes the exercise index evaluation method provided by the present application.

As shown in FIG. 3, the sports index evaluation method provided by the embodiment of the present application may include:

S301. The exercise index evaluation device obtains at least one exercise feature parameter of the user during exercise.

Exemplarily, the sports described in this application may include running or walking. Of course, it can also be other motions that can be measured. The embodiment of the present application does not specifically limit the types of motions.

Optionally, the motion characteristic parameters may include at least one of the following parameters: step length, step frequency, leg swing angle, ground contact time, left and right foot balance, flight time, landing impact, and vertical movement distance.

Specifically, the exercise index evaluation device may use a sensor or other module with a sampling function and a motion feature parameter. The embodiment of the present application does not repeat the process of acquiring the motion feature parameter. Among them, the sensor may include, but not limited to, a speedometer, a gyroscope, a magnetometer, and the like.

In a possible implementation, the motion feature parameter may further include at least one compound feature parameter. Among them, the composite characteristic parameter refers to the parameter obtained by combining the characteristic parameters obtained by direct measurement.

For example, define the compound feature parameter 1 = flight time / touchdown time; define the compound feature parameter 2 = step size / vertical movement distance. Here, the conforming characteristic parameters are exemplarily described in the form of distance, and do not constitute a specific limitation. In practical applications, compound characteristic parameters can be defined according to actual needs.

In a possible implementation, in S301, the exercise index evaluation device may obtain a set of data of at least one exercise feature parameter during exercise through one measurement, and then execute the subsequent process of this application to evaluate the exercise index.

In another possible implementation, the exercise index evaluation device may obtain multiple sets of data of at least one exercise feature parameter during exercise through multiple measurements, and then execute the subsequent process of this application to evaluate the exercise index.

Optionally, the exercise index evaluation device obtains multiple sets of data of at least one exercise feature parameter during exercise through multiple measurements, which may be obtained by periodic measurement, or by measurement under each action of the user during exercise. The application examples do not specifically limit this.

In a possible implementation, the exercise index evaluation device obtains at least one exercise feature parameter at each step of the user's exercise through multiple measurements.

S302. The exercise index evaluation device obtains the score value of each sport feature parameter according to the score model of each sport feature parameter.

The scoring model is a pre-established model, and each motion feature parameter corresponds to a model. If the acquired motion feature parameter is input to its corresponding model, the score value of the motion feature parameter can be obtained.

In a possible implementation, the scoring model can be a statistical model established by modeling based on a large number of motion feature parameters. A motion feature parameter score value reflects the user's ability in the dimension of the motion feature parameter.

Optionally, the scoring module can also depend on the user's physiological parameters. Physiological parameters may include but are not limited to gender, age, height, body mass index (BMI), ethnicity, and so on.

For example, for each running feature, a scoring model is established. For step size, there is a step size scoring model; for step frequency, there is a step frequency scoring model. The scoring model can comprehensively consider the user's gender, age, BMI, speed, height and other factors. The scoring model can be understood as the following parameter model: running feature 1 score = f (running feature 1; gender, age, BMI, speed, height, Race).

Among them, in the f (;) function, the parameters following the scoring model are indicated after the semicolon. In the above formula, running feature 1 may be any running feature parameter.

It should be noted that the embodiment of the present application does not specifically limit the establishment process of the scoring model, and a specific implementation of establishing the scoring model may be selected according to actual needs.

Exemplarily, FIG. 4 illustrates a modeling method for constructing a running feature parameter scoring model. As shown in FIG. 4, the method may include:

401. Use sensors to collect a large number of user's running data, obtain the user's running feature parameters, and build a running feature parameter database.

402. Classify the running feature database according to gender, age, BMI, running speed, etc.

403. For each type of data, analyze the data distribution of each running feature.

For example, taking the step frequency as an example, using the histogram analysis method, the distribution of the step frequency histogram of a certain type of data can be analyzed as shown in Figure 5.

404. Use the Gaussian function to fit the histogram distribution to construct a scoring model.

In 404, a Gaussian function can be used to fit the histogram distribution to construct a discrete scoring model or a continuous scoring model.

For example, suppose the mean value of the Gaussian function after fitting is μ and the variance is δ. According to actual needs, the value of the step frequency can be divided into five intervals, and the score corresponding to each interval is 1 to 5 points. A discrete scoring model is constructed. The specific contents are shown in Table 1.

Table 1

步频区间Cadence interval	(-∞，μ-2δ](-∞, μ-2δ)	(μ-2δ，μ-δ](μ-2δ, μ-δ)	(μ-δ，μ+δ](μ-δ, μ + δ)	(μ+δ,μ+2δ](μ + δ, μ + 2δ)	(μ+2δ，-∞)(μ + 2δ, -∞)
评分score	11	22	33	44	55

For example, according to the Gaussian function fitted, a continuous scoring model can be established as:

Score = 5 * cdf ((running characteristics-μ) / δ, μ, δ).

Among them, cdf (·) is the cumulative probability distribution function.

Further, the score value obtained according to the score model can be scaled and mapped to the [0,1] interval, or [0,100], etc., which is not specifically limited in the embodiments of the present application.

It should be noted that the modeling process illustrated in FIG. 4 above is an exemplary description, and does not specifically limit the process of building the model.

Further, the scoring model is a statistical model, which depends on the feature database. With the increase of user data, the size of the feature database can continue to increase. The method provided in the embodiments of the present application can also update the scoring model with new feature data, that is, the rating The model is updated iteratively. The embodiment of the present application will not repeat the process of iterative update.

In a possible implementation, the scoring model may be a preset correspondence, which includes different motion parameter intervals and their corresponding scores, similar to the discrete scoring model described above. The scoring process is equivalent to querying the preset correspondence. The preset correspondence relationship may be constructed based on a large amount of data, or may be constructed based on theoretical empirical values, which is not specifically limited in the embodiments of the present application.

In a possible implementation, the scoring model may be preset processing of motion feature parameters, and the result of the preset processing is used as the score value. The content of the preset processing may be weighting, preset function, normalization, etc., and no specific processing is performed on this embodiment of the present application.

In a possible implementation, the scoring model may be the recording of motion feature parameters, and the motion feature parameters themselves are used as scoring values.

In a possible implementation, if the exercise index evaluation device in S301 obtains a set of data of at least one exercise feature parameter during exercise through one measurement, in S302, the exercise index evaluation device combines each exercise in the set of data Feature parameters, enter their own scoring model, get their own scoring value.

In a possible implementation, if the exercise index evaluation device in S301 obtains multiple sets of data of at least one motion feature parameter during exercise through multiple measurements, in S302, the exercise index evaluation device combines each of the multiple sets of data The motion feature parameters are taken as their respective average values, and then the average values are input into their respective scoring models to obtain their respective scoring values.

In a possible implementation, if the exercise index evaluation device in S301 obtains multiple sets of data of at least one motion feature parameter during exercise through multiple measurements, in S302, the exercise index evaluation device combines each of the multiple sets of data The motion feature parameters are input into their respective scoring models to obtain multiple score values for each motion feature parameter, and then averaged as the final score value.

Exemplarily, it is assumed that the running characteristic parameters to be acquired include: step length, step frequency, and leg swing angle. The sports index evaluation device in S301 samples and acquires three sets of data of running characteristic parameters, which are recorded as: 1 and leg swing angle 1}, {step 2, cadence 2 and leg swing angle 2}, {step length 3, cadence 3 and leg swing angle 3}.

In a possible implementation, in S302, the exercise index evaluation device may average multiple records of each running feature parameter and input the score model to obtain a score value, that is, the average of step 1, step 2, and step 3. Enter the step score model to get the score value of the step; enter the average value of step frequency 1, step frequency 2 and step frequency 3 into the step frequency score model to get the score value of the step frequency; The average value of angle 2 and leg swing angle 3 is input into the swing leg angle scoring model to obtain the score value of the swing leg angle.

In a possible implementation, in S302, the exercise index evaluation device may input multiple records of each running feature parameter into a scoring model to obtain a scoring value, and then average the results as the final scoring value. That is, step 1, step 2, and step 3 are entered into the step scoring model to obtain the score of step 1, the score of step 2, the score of step 3, and the score of step 1, step The score value of 2 and the score value of step 3 are averaged as the score value of step size; enter step frequency 1, step frequency 2 and step frequency 3 into the step frequency score model to obtain the score value of step frequency 1 and the score of step frequency 2 Value, scoring value of cadence 3, averaging the scoring value of cadence 1, scoring value of cadence 2, and scoring value of cadence 3 as the scoring score of cadence; Leg Angle 3 Enter the swing leg angle scoring model to get the score value of leg swing angle 1, the score value of swing leg angle 2, the score value of swing leg angle 3, and the score value of swing leg angle 1 and the score of swing leg angle The score value of the leg swing angle 3 is averaged as the score value of the swing leg angle.

S303. The exercise index evaluation device substitutes the score value of the exercise characteristic parameter into the preset calculation expression of the exercise index to obtain the evaluation result of the exercise index.

Among them, the exercise index is used to reflect the state of the user who uses the electronic device to which the exercise index evaluation device belongs.

For example, exercise indicators may include, but are not limited to: exercise ability, or exercise efficiency, or risk of injury. The sports index can be defined according to actual needs, which is not specifically limited in the embodiment of the present application. Any index defined according to the score value of the sports feature parameter to reflect the state of the user can be used as the sports index referred to in the present application.

Optionally, in S303, the score value of the sports feature parameter may be substituted into a preset calculation expression of at least one sports index to obtain an evaluation result of at least one sports index. The embodiment of the present application does not specifically limit the number of evaluated sports indexes, and can be determined according to actual needs.

Specifically, according to the state of the user reflected by each sport index, there are motion feature parameters that it depends on, and the preset calculation expression of the sport index is a function of the score value of the sport feature parameters that it depends on. Of course, the sports characteristic parameters that a sports index depends on can be determined according to actual needs, and this embodiment of the present application does not specifically limit this.

Optionally, the preset calculation expression of the sports index is a function of the score value of the sports feature parameter that it depends on. The function may be a simple linear function, such as a weighted sum; or, the function may also be an exponential function For models or other types of functions, the embodiments of the present application do not specifically limit the types of the functions.

In a possible implementation, when the preset calculation expression of the sports index is a weighted sum of the score values of the sports feature parameters that it depends on, the sum of the weight systems is 1.

Exemplarily, it is assumed that the exercise index is running ability, and running ability mainly evaluates the user's muscle strength and endurance. Intuitively understand that for a run, if the user's running duration is long and the speed is fast, it indicates that the user's running ability is strong. Therefore, running ability is related to running speed, running time, leg swing angle, landing time, and pedal acceleration. That is to say, running ability depends on running speed, running time, leg swing angle, landing time, and pedal acceleration. Therefore, The preset calculation expression that defines running ability is:

Running ability = f1 (speed score score, exercise time score, leg swing angle score, landing time score, ground acceleration score).

For example, to simplify the function model, a linear model can be used, such as: running ability = k1 * pace score + k2 * exercise time score + k3 * swing angle score + k4 * landing time score + k5 * pedal Ground acceleration score value. Where k1 + k2 + k3 + k4 + k5 = 1. k1, k2, k3, k4, and k5 are weighting coefficients, which can be determined according to actual needs, and this embodiment of the present application does not specifically limit this.

Exemplarily, the running efficiency may be running the longest distance with the least energy. If two users run consume the same calories, but user A runs longer than user B, then user A's running efficiency is higher than user B's. Therefore, it can be defined that running efficiency depends on cadence, leg swing time, landing time, step length, and vertical movement distance. The preset calculation expression for defining running efficiency is:

Running efficiency = f2 (pace frequency score value, leg swing time score value, landing time score value, step length score value, vertical movement distance score value).

For example, to simplify the function model, a linear model may be used, such as: running efficiency = w1 * cadence score + w2 * normalization (step length score / vertical movement distance score) + w3 * normalization (leg swing time score) / Ground time score value).

Where w1 + w2 + w3 = 1. The values of w1, w2, and w3 can be determined according to actual needs, and this embodiment of the present application does not specifically limit this.

Normalization () is a normalization function, which is mapped to the value interval of running efficiency score. In the above formula, the ratio of the step length and the vertical movement distance is used. It can be understood that if the step length / vertical movement distance value is larger, as much energy as possible is used for the movement in the forward direction, rather than the vertical movement, so running efficient. Similarly, the greater the value of the leg swing time / landing time, the longer the user spends in the air, the more time he spends exercising, and the higher the running efficiency.

For example, the risk of injury is used to evaluate the user's possible risk of injury. The higher the risk assessment result, the greater the risk of injury.

For example, if the step length is too large, there is a high possibility that the landing point is in front of the center of gravity of the body, which is easy to cause the braking effect and cause great damage to the ankle joint and knee. The risk of injury depends on the step length, impact on the ground, and angle of landing. Therefore, the preset calculation expression that defines the risk of injury is:

Risk of injury = f3 (step size score, landing impact score, landing angle score).

For example, to simplify the function model, a linear model may be used, injury risk = m1 * step size score + m2 * landing impact score + m3 * landing angle score.

Where m1 + m2 + m3 = 1. m1, m2, and m3 can be determined according to actual needs, and this embodiment of the present application does not specifically limit this.

It should be noted that the above example illustrates the process of determining the evaluation result of the sports index (running ability, running efficiency, injury risk) by way of example, but it is not specifically limited to this.

S304. The exercise index evaluation device displays exercise suggestions to the user.

Among them, the exercise suggestion corresponds to the evaluation result of the exercise index obtained in S303 in the preset exercise suggestion database.

Specifically, the preset exercise suggestion database includes exercise suggestions corresponding to at least one exercise index. The content of the database established by preset movements can be determined according to actual needs, and this embodiment of the present application does not specifically limit this.

Exemplarily, if the user's athletic ability score is low, the user is accordingly recommended to do more cardiopulmonary function related training to improve lower limb strength. If the user's running efficiency is low, it is recommended that the user increase the cadence and reduce the vertical displacement. If the user has a high injury risk score, it is recommended that the user pay attention to the impact strength to avoid the step size being too large.

In a possible implementation, the preset exercise suggestion database includes at least one exercise index exercise suggestion library, and one exercise suggestion library is based on one exercise index. After obtaining the evaluation result of at least one sports index in S303, query the preset sports recommendation database in S304, the sports recommendation library of each sports index, get the sports recommendation of each sports index, and then all the sports recommendations obtained in S304 Display to the user in the middle.

In a possible implementation, in the preset exercise suggestion database, the exercise suggestion libraries of at least one exercise index included are independently distributed, as shown in Table 2, a preset exercise suggestion database is illustrated.

Table 2

Based on the preset exercise suggestion database shown in Table 2, assuming that the evaluation result of the exercise ability obtained in S303 is located in the interval 3 and the evaluation result of the exercise efficiency is located in the interval 4, the recommendation c and the recommendation x are displayed to the user in S304.

In a possible implementation, in the preset exercise suggestion database, the merged distribution of exercise suggestion libraries of at least one exercise index included, as shown in Table 3, illustrates a preset exercise suggestion database.

table 3

A	运动效率区间aExercise efficiency interval a	运动效率区间bExercise efficiency interval b	运动效率区间cExercise efficiency interval c	……...
运动能力区间1Exercise Ability Range 1	A	A	A	……...
运动能力区间2Exercise capacity interval 2	A	A	A	……...
运动能力区间3Exercise range 3	A	运动建议AExercise Recommendation A	A	……...
运动能力区间6Exercise range 6	A	A	A	……...
……...	……...	……...	……...	……...

Based on the preset exercise suggestion database shown in Table 3, assuming that the evaluation result of the exercise ability obtained in S303 is located in the exercise ability section 3, and the exercise efficiency evaluation result is located in the exercise efficiency section b, the exercise suggestion A is displayed to the user in S304 .

It should be noted that the contents and related descriptions in Table 2 and Table 3 are exemplary descriptions, and are not limitations on the specific implementation of S304. In practical applications, the content of the preset motion suggestion database can be configured according to requirements to achieve S304.

The embodiment of the present application may divide the functional module of the sports index evaluation device according to the above method example, for example, each functional module may be divided corresponding to each function, or two or more functions may be integrated into one processing module. The above integrated modules may be implemented in the form of hardware or software function modules. The division of the modules in the embodiments of the present application is schematic, and is only a division of logical functions. In actual implementation, there may be another division manner.

In a case where each functional module is divided corresponding to each function, FIG. 6 shows a possible structural schematic diagram of the exercise index evaluation device involved in the above embodiment. As shown in FIG. 6, the exercise index evaluation device 60 includes an acquisition unit 601, a scoring unit 602, and an evaluation unit 603.

Wherein, the obtaining unit 601 is used to support the exercise index evaluation device 60 to execute step S301 in the exercise index evaluation method illustrated in FIG. 3, and / or other processes used in the technology described herein.

The scoring unit 602 is used to support the exercise index evaluation device 60 to execute step S302 in the exercise index evaluation method illustrated in FIG. 3, and / or other processes used in the technology described herein.

The evaluation unit 603 is used to support the exercise index evaluation device 60 to execute step S303 in the exercise index evaluation method illustrated in FIG. 3, and / or other processes used in the technology described herein.

Wherein, all relevant content of each step involved in the above method embodiments can be referred to the function description of the corresponding function module, which will not be repeated here.

Of course, the convolution device 80 for a neural network provided by the embodiment of the present application includes but is not limited to the above-mentioned modules. As shown in FIG. 6, the exercise index evaluation device 60 may further include a display unit 604 for supporting the exercise index evaluation device 60 to perform step S304 in the exercise index evaluation method illustrated in FIG. 3, and / or used for the Other processes of technology.

In the case of using an integrated unit, a schematic structural diagram of an exercise index evaluation device provided by an embodiment of the present application is shown in FIG. 7. In FIG. 7, the exercise index evaluation device 70 includes: a processing module 701 and a communication module 702. The processing module 701 is used to control and manage the actions of the sports index evaluation device 70, for example, to perform the steps performed by the acquisition unit 601, the scoring unit 602, and the evaluation unit 603, and / or other processes for performing the techniques described herein . The communication module 702 is used to support the interaction between the exercise index evaluation device 70 and other devices. As shown in FIG. 7, the exercise index evaluation device 70 may further include a storage module 703 for storing the program code and data of the exercise index evaluation device 70.

The processing module 701 may be a processor or a controller, for example, it may be a CPU, general-purpose processor, digital signal processor (Digital Signal Processor, DSP), ASIC, FPGA or other programmable logic device, transistor logic device, hardware component Or any combination thereof. It can implement or execute various exemplary logical blocks, modules, and circuits described in conjunction with the disclosure of the present application. The processor may also be a combination of computing functions, for example, including one or more microprocessor combinations, a combination of DSP and microprocessor, and so on. The communication module 702 may be a transceiver, an RF circuit, a communication interface, or the like. The storage module 703 may be a memory.

If the exercise index evaluation device 70 is a mobile phone, the processing module 701 may be the processor 101 in FIG. 1, the communication module 702 may be the antenna in FIG. 1, and the storage module 703 may be the memory 103 in FIG. 1.

Another embodiment of the present application further provides a computer-readable storage medium, the computer-readable storage medium including one or more program codes, the one or more programs including instructions, when the processor in the exercise index evaluation device is executing In the case of the program code, the exercise index evaluation device executes the above exercise index evaluation method.

In another embodiment of the present application, a computer program product is further provided. The computer program product includes computer-executable instructions, which are stored in a computer-readable storage medium; at least one processor of the exercise index evaluation device may The computer-executed instruction is read from a computer-readable storage medium, and the execution of the computer-executed instruction by at least one processor causes the exercise index evaluation device to perform the steps of executing the aforementioned exercise index evaluation method.

In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using a software program, it may appear in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the processes or functions according to the embodiments of the present application are generated in whole or in part.

The computer may be a general-purpose computer, a dedicated computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be from a website site, computer, server or data center Transmission to another website, computer, server or data center via wired (eg coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (eg infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device including a server, a data center, and the like integrated with one or more available media. The available media may be magnetic media (eg, floppy disk, hard disk, magnetic tape), optical media (eg, DVD), or semiconductor media (eg, Solid State Disk (SSD)), and the like.

Through the description of the above embodiments, those skilled in the art can clearly understand that, for convenience and conciseness of description, only the above-mentioned division of each functional module is used as an example for illustration. In practical applications, the above-mentioned functions can be allocated as needed It is completed by different functional modules, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above.

In the several embodiments provided in this application, it should be understood that the disclosed device and method may be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules or units is only a division of logical functions. In actual implementation, there may be other divisions, for example, multiple units or components may be The combination can either be integrated into another device, or some features can be ignored, or not implemented. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components displayed as units may be one physical unit or multiple physical units, that is, may be located in one place, or may be distributed in multiple different places . Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or software functional unit.

If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a readable storage medium. Based on such an understanding, the technical solutions of the embodiments of the present application may essentially be part of or contribute to the existing technology, or all or part of the technical solutions may be embodied in the form of software products, which are stored in a storage medium In it, several instructions are included to enable a device (which may be a single-chip microcomputer, chip, etc.) or processor to execute all or part of the steps of the methods described in the embodiments of the present application. The foregoing storage media include various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk, or an optical disk.

The above is only the specific implementation of this application, but the scope of protection of this application is not limited to this, any changes or replacements within the technical scope disclosed in this application should be covered within the scope of protection of this application . Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

A sports index evaluation method characterized by,

Obtain at least one movement feature parameter of the user during the movement;

According to the scoring model of each sport feature parameter, obtain the score value of each of the sport feature parameters;

The score value of the sports feature parameter is substituted into a preset calculation expression of the sports index to obtain the assessment result of the sports index.
The exercise index evaluation method according to claim 1, wherein the exercise index includes:

Exercise ability, or exercise efficiency, or risk of injury.
The exercise index evaluation method according to claim 1 or 2, wherein the method further comprises:

Displaying exercise suggestions to the user, the exercise suggestions corresponding to the evaluation result of the exercise index in a preset exercise suggestion database.
The sports index evaluation method according to any one of claims 1 to 3, wherein the obtaining the score value of each of the sports feature parameters according to the scoring model of each of the sports feature parameters includes:

Calculating the average value of the at least one motion feature parameter of the user under each action, and inputting the average value of the at least one motion feature parameter into the respective scoring model of each motion feature parameter to obtain each of the motions The score value of the characteristic parameter;

or,

Input the at least one motion feature parameter of the user under each action into the respective scoring model of each motion feature parameter to obtain the score value of each of the motion feature parameters under each action; calculate each The average value of the score values of the sports feature parameters to obtain the score value of each of the sports feature parameters.
The method for evaluating an exercise index according to any one of claims 1 to 4, wherein the exercise includes running or walking.
The exercise index evaluation method according to claim 5, wherein the exercise characteristic parameters include at least one of the following parameters: step length, step frequency, leg swing angle, ground contact time, left and right foot balance, and emptying time , Landing impact, vertical movement distance.
The sports index evaluation method according to any one of claims 1-6, wherein the preset calculation expression includes: a weighted sum of score values of sports feature parameters.
A sports index evaluation device, characterized in that

An obtaining unit, configured to obtain at least one motion feature parameter of the user during the exercise;

The scoring unit is used to obtain the scoring value of each of the sports feature parameters according to the scoring model of each sports feature parameter;

The evaluation unit is configured to substitute the score value of the sports feature parameter acquired by the scoring unit into a preset calculation expression of a sports index to obtain an assessment result of the sports index.
The exercise index evaluation device according to claim 8, wherein the exercise index includes:

Exercise ability, or exercise efficiency, or risk of injury.
The exercise index evaluation device according to claim 8 or 9, wherein

The obtaining unit is further configured to obtain a motion suggestion corresponding to the evaluation result of the sport index in a preset motion suggestion database

The device also includes a display unit for displaying the exercise suggestion to the user.
The exercise index evaluation device according to any one of claims 8 to 10, wherein the scoring unit is specifically used to include:

Input the average value of the at least one sports feature parameter of the user under each action into the respective scoring model of each sports feature parameter to obtain the rating value of each of the sports feature parameters;

or,

Input the at least one motion feature parameter of the user under each action into the respective scoring model of each motion feature parameter to obtain the score value of each of the motion feature parameters under each action; calculate each The average value of the score values of the sports feature parameters to obtain the score value of each of the sports feature parameters.
The exercise index evaluation device according to any one of claims 8 to 11, wherein the exercise includes running or walking.
The exercise index evaluation device according to claim 12, wherein the exercise characteristic parameters include at least one of the following parameters: step length, step frequency, leg swing angle, ground contact time, left and right foot balance, and emptying time , Landing impact, vertical movement distance.
An exercise index evaluation device, characterized by comprising a processor, a memory, and instructions stored on the memory and executable on the processor, and when the instructions are executed, causing the device to execute claims 1 to 7 The sports index evaluation method as described in any one.
An electronic device, characterized by comprising the exercise index evaluation device according to any one of claims 8-14.
A computer-readable storage medium, including instructions, which when executed on a computer, causes the computer to execute the exercise index evaluation method according to any one of claims 1 to 7.
A computer program product, which when run on a computer, causes the computer to execute the exercise index evaluation method according to any one of claims 1 to 7.