CN112188864A

CN112188864A - Motion index evaluation method and device

Info

Publication number: CN112188864A
Application number: CN201880093345.2A
Authority: CN
Inventors: 陈霄汉; 王宇; 李祥臣; 孙启政
Original assignee: Huawei Technologies Co Ltd
Current assignee: Huawei Technologies Co Ltd
Priority date: 2018-10-31
Filing date: 2018-10-31
Publication date: 2021-01-05
Also published as: WO2020087364A1

Abstract

A motion index evaluation method and a device thereof are provided, wherein the method comprises the following steps: acquiring at least one motion characteristic parameter of a user in a motion process (S301); obtaining the scoring value of each motion characteristic parameter in the motion process according to the scoring model of each motion characteristic parameter (S302); and substituting the score value of the motion characteristic parameter into a preset calculation expression of the motion index to obtain an evaluation result of the motion index (S303). The motion index evaluation method and the motion index evaluation device enable the motion characteristic parameters to play a greater role, and improve user experience.

Description

Motion index evaluation method and device

Technical Field

The present application relates to the field of electronic devices, and in particular, to a method and an apparatus for evaluating a motion index.

Background

Nowadays, with the rapid development of technology, more and more electronic devices can realize the measurement of the motion characteristics of users. For example, the electronic devices such as wearable devices and mobile phones can measure the running characteristics of the user, such as the movement time, the movement distance, the step length, the step frequency, the leg swing angle, the touchdown time, the left foot balance and the right foot balance, so as to obtain the running characteristic parameters.

Currently, after acquiring motion characteristic parameters, electronic devices present these parameters to users. How to measure these parameters by using electronic devices is an urgent problem to be solved.

Disclosure of Invention

The embodiment of the application provides a method and a device for evaluating a motion index, which are used for evaluating a user motion index by utilizing a motion characteristic parameter.

In order to achieve the above purpose, the embodiment of the present application adopts the following technical solutions:

in a first aspect, a method for evaluating a sports index is provided, including: acquiring at least one motion characteristic parameter of a user in a motion process; according to the grading model of each motion characteristic parameter, obtaining the grading value of each motion characteristic parameter in the motion process; and substituting the score values of the motion characteristic parameters into a preset calculation expression of the motion index to obtain an evaluation result of the motion index.

By the motion index evaluation method, the motion index is evaluated by utilizing the motion characteristic parameters, and the evaluation result of the motion index is obtained. On one hand, the motion characteristic parameters play a greater role, and on the other hand, the evaluation on the motion indexes can be applied to the aspects, so that the user experience is improved.

With reference to the first aspect, in a possible implementation manner, the motion index may include: exercise capacity, or exercise efficiency, or risk of injury. The exercise index is the capability expression of each aspect when the user exercises. Of course, there may be other types of motion indicators besides those listed here, and they are not listed here.

With reference to the first aspect or any one of the foregoing possible implementation manners, in another possible implementation manner, the method for evaluating a sports index provided by the present application may further include: and displaying the exercise suggestion to the user, wherein the exercise suggestion corresponds to the evaluation result of the exercise index in a preset exercise suggestion database. By providing the user with motion advice, 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, the obtaining a score value of each motion characteristic parameter according to a score model of each motion characteristic parameter may specifically be implemented as: and calculating the average value of at least one motion characteristic parameter of the user under each action, and respectively inputting the average value of at least one motion characteristic parameter into the respective scoring model of each motion characteristic parameter to obtain the scoring value of each motion characteristic parameter. In this implementation, the obtained motion characteristic parameters are averaged and then 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, the obtaining a score value of each motion characteristic parameter according to a score model of each motion characteristic parameter may specifically be implemented as: respectively inputting at least one motion characteristic parameter of a user under each action into a respective scoring model of each motion characteristic parameter to obtain a scoring value of each motion characteristic parameter under each action; and calculating the average value of the scoring values of each motion characteristic parameter to obtain the scoring value of each motion characteristic parameter. In this implementation, each motion characteristic parameter is substituted into the model score before averaging the score values.

In combination with the first aspect or any one of the above possible implementations, in another possible implementation, the exercise described herein may include running or walking. Of course, other movement forms are possible, and the present application is not limited to this.

With reference to the first aspect or any one of the foregoing possible implementations, in another possible implementation, the motion characteristic parameter may include at least one of the following parameters: step length, step frequency, leg swing angle, touchdown time, left foot balance, right foot balance, flight time, touchdown impact and 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 characteristic parameter may include: and compounding the characteristic parameters. Wherein the composite characteristic parameter is defined by calculation of at least one conventional motion characteristic parameter. The content of the composite characteristic parameter is not particularly limited in the present application.

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 motion index may include: a weighted sum of the scoring values of the motion characteristic parameters. The weighted sum of the score values of the sports characteristic parameters related to the sports index may be defined as a preset computational expression of the sports index. The motion characteristic parameters related to the selected motion index can be selected according to actual requirements, and the method is not specifically limited in this application.

Wherein the sum of the weight coefficients in the preset calculation expression is equal to 1. However, the values of the weighting systems are not particularly limited in the present application.

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

It should be noted that, the process of establishing the scoring model is not particularly limited in the present application, and all methods that can be used for modeling may be applied thereto.

With reference to the first aspect or any one of the foregoing possible implementation manners, in another possible implementation manner, the method for evaluating a sports index provided by the present application may further include: and periodically updating the scoring model of each motion characteristic parameter according to the motion characteristic parameters acquired in the motion process.

In a second aspect, an exercise index evaluation apparatus is provided, which may include an acquisition unit, a scoring unit, and an evaluation unit. The device comprises an acquisition unit, a processing unit and a control unit, wherein the acquisition unit is used for acquiring at least one motion characteristic parameter of a user in the motion process; the scoring unit is used for acquiring the scoring value of each motion characteristic parameter according to the scoring model of each motion characteristic parameter; and the evaluation unit is used for substituting the score value of the motion characteristic parameter acquired by the scoring unit into a preset calculation expression of the motion index to obtain an evaluation result of the motion index.

Through the exercise index evaluation device provided by the application, the exercise index is evaluated by utilizing the exercise characteristic parameters, and the evaluation result of the exercise index is obtained. On one hand, the motion characteristic parameters play a greater role, and on the other hand, the evaluation on the motion indexes can be applied to the aspects, so that the user experience is improved.

With reference to the second aspect, in a possible implementation manner, the motion index may include: exercise capacity, 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 an exercise suggestion corresponding to the evaluation result of the exercise index in the preset exercise suggestion database; the apparatus may further include a display unit for displaying the exercise advice acquired by the acquisition unit to a user.

With reference to the second aspect or any one of the foregoing possible implementation manners, in another possible implementation manner, the scoring unit may specifically be configured to include: respectively inputting the average value of at least one motion characteristic parameter of the user under each action into a respective scoring model of each motion characteristic parameter to obtain the scoring value of each motion characteristic parameter; or respectively inputting at least one motion characteristic parameter of the user under each action into the respective scoring model of each motion characteristic parameter to obtain the scoring value of each motion characteristic parameter under each action; and calculating the average value of the scoring values of each motion characteristic parameter to obtain the scoring value of each motion characteristic parameter.

In combination with the second aspect or any one of the above possible implementations, in another possible implementation, the exercise may include running or walking.

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

It should be noted that, the exercise index estimation apparatus provided in the second aspect is configured to execute the exercise index estimation method provided in the first aspect or any possible implementation manner, and specific implementation may refer to any possible implementation manner of the first aspect or the first aspect, and details are not described here again.

In a third aspect, an embodiment of the present application provides a motion index evaluation apparatus, where the apparatus may implement corresponding functions in the above-described method example of the first aspect, and the functions may be implemented by hardware or by hardware executing corresponding software. The hardware or software comprises one or more modules corresponding to the functions.

With reference to the third aspect, in a possible implementation manner, the apparatus includes a processor and a transceiver in a structure, where the processor is configured to support the apparatus to perform corresponding functions in the foregoing method. The transceiver is configured to support communication between the apparatus and other devices. The apparatus may also include a memory, coupled to the processor, that retains program instructions and data necessary for the apparatus.

In a fourth aspect, an electronic device is provided, which includes the motion index estimation apparatus according to any one of the above aspects or any possible implementation manner.

In a fifth aspect, a computer-readable storage medium is provided, which includes instructions, when executed on a computer, cause the computer to perform the exercise metric evaluation method according to the first aspect or any one of the possible implementations.

A sixth aspect provides a computer program product which, when run on a computer, causes the computer to perform the method for motion indicator assessment of the first aspect or any of the possible implementations.

The schemes provided in the third aspect to the sixth aspect are used to implement the motion index assessment method provided in the first aspect or any possible implementation manner, and therefore the same beneficial effects as those of the first aspect may be achieved, and are not described again here.

Drawings

Fig. 1 is a schematic structural diagram of a mobile phone according to an embodiment of the present application;

fig. 2 is a schematic structural diagram of an exercise index evaluation apparatus according to an embodiment of the present disclosure;

fig. 3 is a schematic flowchart of a motion index evaluation method according to an embodiment of the present application;

fig. 4 is a schematic flowchart of a modeling method for constructing a running characteristic parameter scoring model according to an embodiment of the present application;

fig. 5 is a schematic distribution diagram of a step frequency histogram according to an embodiment of the present application;

fig. 6 is a schematic structural diagram of another exercise index evaluation device according to an embodiment of the present application;

fig. 7 is a schematic structural diagram of another sports index evaluation device according to an embodiment of the present application.

Detailed Description

In the embodiments of the present application, words such as "exemplary" or "for example" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "e.g.," is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion.

In the following, the terms "first", "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the embodiments of the present application, "a plurality" means two or more unless otherwise specified.

Nowadays, there are many electronic devices supporting motion measurement, which measure the motion process of a user using the electronic device by themselves or by configuring motion software to obtain motion characteristic parameters. For example, an electronic device for measuring running can measure running characteristics such as exercise time, exercise distance, step size, step frequency, leg swing angle, touchdown time, left and right foot balance, and the like of a user.

Based on the above, the invention provides a motion index evaluation method, which evaluates a motion index according to a motion characteristic parameter by using a motion characteristic parameter obtained by measurement.

The exercise index evaluation method provided by the embodiment of the application is applied to an exercise index evaluation device. The exercise index evaluation method is applied to the field of exercise health, the exercise index evaluation device can be electronic equipment, and the electronic equipment is applied to the field of exercise health to execute the exercise index evaluation method.

In a possible implementation, the motion index estimation apparatus may be an electronic device supporting motion measurement, may also be a function module of the electronic device supporting motion measurement, and may also be an Application (APP) in the electronic device supporting motion measurement, which is not specifically limited in this embodiment of the present Application.

The electronic device may be a mobile phone (e.g., the mobile phone 100 shown in fig. 1), a tablet computer, a Personal Computer (PC), a Personal Digital Assistant (PDA), a smart watch, a netbook, a wearable electronic device, and the like, which allow a user to input processing operations instructing the electronic device to perform related operation events.

As shown in fig. 1, a mobile phone 100 is taken as an example of the electronic device. The mobile phone 100 may specifically include: a processor 101, Radio Frequency (RF) circuitry 102, a memory 103, a touch screen 104, a bluetooth device 105, one or more sensors 106, a wireless fidelity (Wi-Fi) device 107, a pointing device 108, audio circuitry 109, a peripheral interface 110, and a power supply 111. These components may communicate over one or more communication buses or signal lines (not shown in fig. 1).

Those skilled in the art will appreciate that the hardware configuration shown in fig. 1 is not intended to be limiting, and that the handset 100 may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.

The following describes the components of the handset 100 in detail with reference to fig. 1:

the processor 101 is a control center of the cellular phone 100, connects various parts of the cellular phone 100 using various interfaces and lines, and performs various functions of the cellular phone 100 and processes data by running or executing an application program stored in the memory 103 and calling data stored in the memory 103. In some embodiments, processor 101 may include one or more processing units.

In this embodiment, the processor 101 is specifically configured to: acquiring at least one motion characteristic parameter of a user under each action in the motion process; according to the grading model of each motion characteristic parameter, obtaining the grading value of each motion characteristic parameter in the motion process; and substituting the score values of the motion characteristic parameters into a preset calculation expression of the motion index to obtain an evaluation result of the motion index.

The rf circuit 102 may be used for receiving and transmitting wireless signals during the transmission and reception of information or calls. In particular, the rf circuit 102 may receive downlink data of the base station and then process the received downlink data to the processor 101; in addition, data relating to uplink is transmitted to the base station. Typically, the radio frequency circuitry 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 circuitry 102 may also communicate with other devices via wireless communication. The wireless communication may use any communication standard or protocol including, but not limited to, global system for mobile communications, general packet radio service, code division multiple access, wideband code division multiple access, long term evolution, email, short message service, and the like.

The memory 103 is used for storing 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 program storage area and a data storage area, wherein the program storage area can store an operating system and application programs (such as a sound playing function, an image processing function, etc.) required by at least one function; the storage data area may store data (e.g., audio data, a phonebook, etc.) created from use of the handset 100. Further, the memory 103 may include high speed Random Access Memory (RAM), and may also include non-volatile memory, such as magnetic disk storage devices, flash memory devices, or other volatile solid state storage devices. The memory 103 may store various operating systems such as, for example,

the operating system is used to operate the system,

an 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 configured to store a scoring model of each motion characteristic parameter.

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

Wherein the touch pad 104-1 can capture touch events on or near the touch pad 104-1 by a user of the cell phone 100 (e.g., user operation on or near the touch pad 104-1 using any suitable object such as a finger, a stylus, etc.) and transmit the captured touch information to other devices (e.g., the processor 101). Among them, a touch event of a user near the touch pad 104-1 can be called a hover touch; hover touch may refer to a user not having to directly contact the touchpad in order to select, move, or drag a target (e.g., an icon, etc.), but rather only having to be in proximity to the device in order to perform a desired function. In addition, the touch pad 104-1 can be implemented by various types, such as resistive, capacitive, infrared, and surface acoustic wave.

Display (also referred to as a display screen) 104-2 may be used to display information entered by or provided to the user as well as various menus for handset 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 trackpad 104-1 may be overlaid on the display 104-2, and when the trackpad 104-1 detects a touch event thereon or nearby, it is communicated to the processor 101 to determine the type of touch event, and the processor 101 may then provide a corresponding visual output on the display 104-2 based on the type of touch event.

Although in FIG. 1, the touch pad 104-1 and the display screen 104-2 are shown as two separate components to implement the input and output functions of the cell phone 100, in some embodiments, the touch pad 104-1 and the display screen 104-2 may be integrated to implement the input and output functions of the cell phone 100. It is understood that the touch screen 104 is formed by stacking multiple layers of materials, and only the touch pad (layer) and the display screen (layer) are shown in the embodiment of the present application, and other layers are not described in the embodiment of the present application. In addition, the touch pad 104-1 may be disposed on the front surface of the mobile phone 100 in a full panel manner, and the display screen 104-2 may also be disposed on the front surface of the mobile phone 100 in a full panel manner, so that a frameless structure can be implemented on the front surface of the mobile phone.

In addition, the mobile phone 100 may also have a fingerprint recognition function. For example, the fingerprint identifier 112 may be disposed on the back side of the handset 100 (e.g., below the rear facing camera), or the fingerprint identifier 112 may be disposed on the front side of the handset 100 (e.g., below the touch screen 104). For another example, the fingerprint acquisition device 112 may be configured in the touch screen 104 to realize the fingerprint identification function, i.e., the fingerprint acquisition device 112 may be integrated with the touch screen 104 to realize the fingerprint identification function of the mobile phone 100. In this case, the fingerprint acquisition device 112 is disposed in the touch screen 104, may be a part of the touch screen 104, and may be disposed in the touch screen 104 in other manners. The main component of the fingerprint acquisition device 112 in the present embodiment is a fingerprint sensor, which may employ any type of sensing technology, including but not limited to optical, capacitive, piezoelectric, or ultrasonic sensing technologies, among others.

The handset 100 may also include a bluetooth device 105 for enabling data exchange between the handset 100 and other short-range devices (e.g., cell phones, smart watches, etc.). The bluetooth device in the embodiment of the present application may be an integrated circuit or a bluetooth chip.

The handset 100 may also include at least one sensor 106, such as a light sensor, motion sensor, and other sensors. Specifically, the motion sensor is used to measure a motion characteristic parameter of the user of the mobile phone 100 during the motion process. As one of the motion sensors, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally three axes), detect the magnitude and direction of gravity when stationary, and can be used for applications of recognizing the posture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), and the like. The light sensor may include an ambient light sensor that adjusts the brightness of the display of the touch screen 104 based on the intensity of ambient light, and a proximity sensor that turns off the display when the cell phone 100 is moved to the ear. As for other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor, which can be configured on the mobile phone 100, further description is omitted here.

The Wi-Fi device 107 is used for providing network access for the mobile phone 100 according to Wi-Fi related standard protocols, the mobile phone 100 can be accessed to a Wi-Fi access point through the Wi-Fi device 107, so that the mobile phone helps a user to send and receive e-mails, browse webpages, access streaming media and the like, and wireless broadband internet access is provided for the user. In other embodiments, the Wi-Fi apparatus 107 can also act as a Wi-Fi wireless access point and can provide Wi-Fi network access to other devices.

And a positioning device 108 for providing a geographical position for the handset 100. It is understood that the positioning device 108 may be a receiver of a Global Positioning System (GPS) or a positioning system such as the beidou satellite navigation system, russian GLONASS, etc. After receiving the geographical location transmitted by the positioning system, the positioning device 108 transmits the information to the processor 101 for processing or transmits the information to the memory 103 for storage. In some other embodiments, the positioning device 108 may also be an Assisted Global Positioning System (AGPS) receiver that assists the positioning device 108 in performing ranging and positioning services by acting as an assistance server, in which case the assistance server provides positioning assistance by communicating with the positioning device 108 (i.e., GPS receiver) of the apparatus, such as the handset 100, over a wireless communication network. In other embodiments, the location device 108 may also be a Wi-Fi access point based location technology.

Because each Wi-Fi access point has a globally unique Media Access Control (MAC) address, the device can scan and collect broadcast signals of surrounding Wi-Fi access points under the condition of starting Wi-Fi, and therefore the MAC address broadcasted by the Wi-Fi access points can be acquired; the device sends the data (such as the MAC address) capable of identifying the Wi-Fi access points to the location server through the wireless communication network, the location server retrieves the geographical location of each Wi-Fi access point, and calculates the geographical location of the device according to the strength of the Wi-Fi broadcast signal and sends the geographical location of the device to the positioning device 108 of the device.

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

The audio circuitry 109, speaker 113, microphone 114 can provide an audio interface between a user and the handset 100. The audio circuit 109 may transmit the electrical signal converted from the received audio data to the speaker 113, and convert the electrical signal into a sound signal by the speaker 113 for output; on the other hand, the microphone 114 converts the collected sound signal into an electrical signal, converts the electrical signal into audio data after being received by the audio circuit 109, and outputs the audio data to the RF circuit 102 to be transmitted to, for example, another cellular phone, or outputs the audio data to the memory 103 for further processing.

Peripheral interface 110, which is used to provide various interfaces for external input/output devices (e.g., keyboard, mouse, external display, external memory, SIM card, etc.). For example, the mouse is connected through a Universal Serial Bus (USB) interface, and the SIM card provided by the telecom operator is connected through a metal contact on a SIM card slot. Peripheral interface 110 may be used to couple the aforementioned external input/output peripherals to processor 101 and memory 103.

In this embodiment, the mobile phone 100 may communicate with other devices in the device group through the peripheral interface 110, for example, the peripheral interface 110 may receive display data sent by the other devices for displaying, and the like, which is not limited in this embodiment.

The mobile phone 100 may further include a power supply device 111 (such as a battery and a power management chip) for supplying power to each component, and the battery may be logically connected to the processor 101 through the power management chip, so as to implement functions of managing charging, discharging, and power consumption through the power supply device 111.

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-projector, a Near Field Communication (NFC) device, etc., which will not be described herein.

In a possible implementation, the exercise index evaluation apparatus provided by the present application may be a functional unit. As shown in fig. 2, the exercise index estimation apparatus 20 may specifically include a sensor 201 for sampling data, a calculation unit 202 for performing calculation, and a storage unit 203 for storing data.

The following specifically describes each component of the sports index evaluation device 20 with reference to fig. 2:

the sensor 201 may be a speedometer, a gyroscope, a magnetometer, or other device with a capturing function, which is not particularly limited in this embodiment of the present application.

A storage unit 203, which may be a volatile memory (volatile memory), such as a random-access memory (RAM); or a non-volatile memory (non-volatile memory), such as a read-only memory (ROM), a flash memory (flash memory), a Hard Disk Drive (HDD) or a solid-state drive (SSD); or a combination of the above types of memories, for storing program code, and configuration files, which implement the methods of the present application. For example, the storage unit 203 stores a scoring model, a motion advice database, and the like.

The calculating unit 202 is a control center of the motion index estimating apparatus 20, and may be a Central Processing Unit (CPU), A Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application, for example: one or more microprocessors (digital signal processors, DSPs), or one or more Field Programmable Gate Arrays (FPGAs).

As shown in fig. 2, the sports index evaluation device 20 may further include a display unit 204 for displaying the sports advice to the user.

The calculation unit 202 performs the following functions: acquiring at least one motion characteristic parameter of a user in a motion process; according to the grading model of each motion characteristic parameter, obtaining the grading value of each motion characteristic parameter in the motion process; and substituting the score values of the motion characteristic parameters into a preset calculation expression of the motion index to obtain an evaluation result of the motion index.

For example, each unit included in the motion index estimation apparatus 20 may be deployed in one entity device, or may be deployed in a plurality of devices, which is not specifically limited in this embodiment of the present application.

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

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

The following describes the exercise index evaluation method provided in the present application in detail.

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

As shown in fig. 3, a method for evaluating a sports index provided in an embodiment of the present application may include:

s301, the motion index evaluation device obtains at least one motion characteristic parameter of the user in the motion process.

For example, the exercise described herein may include running or walking. Of course, other motions that can be measured are also possible, and the embodiment of the present application is not particularly limited to the type of motion.

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

Specifically, the motion index evaluation device may be configured to obtain the motion characteristic parameters through a sensor or other modules having a sampling function, which is not repeated in the process of obtaining the motion characteristic parameters in the embodiment of the present application. The sensors may include, but are not limited to, speedometers, gyroscopes, magnetometers, and the like.

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

For example, a composite characteristic parameter 1 is defined as flight time/touchdown time; the composite characteristic parameter 2 is defined as step length/vertical movement distance. The coincidence characteristic parameter is exemplified only by the form of distance, and is not particularly limited. In practical application, the composite characteristic parameters can be defined according to practical requirements.

In a possible implementation manner, in S301, the exercise index evaluation device may obtain a set of data of at least one exercise characteristic parameter during the exercise process through one measurement, and then perform the subsequent process of the present 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 characteristic parameter during the exercise process through multiple measurements, and then perform the subsequent process of the present application to evaluate the exercise index.

Optionally, the exercise index evaluation device obtains multiple sets of data of at least one exercise characteristic parameter in the exercise process through multiple measurements, and the multiple sets of data may be obtained through periodic measurement or measurement obtained through measurement under each action in the exercise process of the user, which is not specifically limited in this embodiment of the application.

In one possible implementation, the exercise index evaluation device obtains at least one exercise characteristic parameter of the user at each step in the exercise process through multiple measurements.

S302, the sports index evaluation device obtains the score value of each sports characteristic parameter according to the score model of each sports characteristic parameter.

The scoring model is a pre-established model, each motion characteristic parameter corresponds to one model, and the scoring value of the motion characteristic parameter can be obtained by inputting the obtained motion characteristic parameter into the corresponding model.

In one possible implementation, the scoring model may be a statistical model established by modeling based on a large number of motion characteristic parameters, and a motion characteristic parameter scoring value reflects the ability of the user in the dimension of the motion characteristic parameter.

Optionally, the scoring module may also be dependent on a physiological parameter of the user. Physiological parameters may include, but are not limited to, gender, age, height, Body Mass Index (BMI), race, and the like.

For example, a scoring model is established for each running characteristic. As for step size, there is a step size scoring model; for the step frequency, a step frequency scoring model and the like are available. The scoring model can comprehensively consider factors such as gender, age, BMI, speed, height and the like of the user, and can be understood as the following parameter models: run feature 1 score ═ f (run feature 1; gender, age, BMI, speed, height, race).

Wherein, in the f (;) function, parameters dependent on the scoring model are represented after the semicolon. In the above formula, the running characteristic 1 may be any running characteristic parameter.

It should be noted that, in the embodiment of the present application, the process of establishing the scoring model is not specifically limited, and the specific implementation of establishing the scoring model may be selected according to actual requirements.

For example, fig. 4 illustrates a modeling method for constructing a running characteristic parameter scoring model. As shown in fig. 4, the method may include:

401. the running data of a large number of users are collected by the sensors, the running characteristic parameters of the users are obtained, and a running characteristic parameter database is built.

402. The running characteristic database is classified according to gender, age, BMI, running speed, and the like.

403. For each type of data, the data distribution for each running characteristic is analyzed.

For example, taking the step frequency as an example, a histogram analysis method is adopted to analyze the step frequency histogram distribution of a certain kind of data as shown in fig. 5.

404. And fitting the histogram distribution by using a Gaussian function to construct a grading model.

In 404, a discrete scoring model or a continuous scoring model may be constructed by fitting a gaussian function to the histogram distribution.

For example, assume that the mean of the fitted gaussian is μ and the variance is. According to actual requirements, the step frequency value can be divided into five intervals, the score corresponding to each interval is 1-5, a discrete scoring model is constructed, and the specific content is shown in table 1.

TABLE 1

Step frequency interval	(-∞，μ-2δ]	(μ-2δ，μ-δ]	(μ-δ，μ+δ]	(μ+δ,μ+2δ]	(μ+2δ，-∞)
Scoring	1	2	3	4	5

For example, a continuous scoring model can be built from the fitted gaussian function as:

score 5 + cdf ((running features- μ)/, μ,).

Where cdf (-) is a probability cumulative distribution function.

Further, the score value obtained according to the scoring model may be mapped to the [0,1] interval, or [0,100], etc. by scaling, which is not specifically limited in the embodiment of the present application.

It should be noted that the modeling process illustrated in fig. 4 is an exemplary illustration, and is not a specific limitation on the process of constructing the model.

Furthermore, the scoring model is a statistical model, and depends on the feature database, and the scale of the feature database can be continuously increased with the increase of user data. The iterative update process is not described in detail in the embodiment of the present application.

In a possible implementation, the scoring model may be a preset corresponding relationship, where the preset corresponding relationship includes different motion parameter intervals and corresponding scores thereof, and is similar to the discrete scoring model described above. The scoring process is equivalent to inquiring the preset corresponding relation. The preset corresponding relationship may be constructed according to a large amount of data, or may be constructed according to a theoretical empirical value, which is not specifically limited in the embodiment of the present application.

In a possible implementation, the scoring model may be a preset process of the motion characteristic parameter, and a result of the preset process is used as a scoring value. The content of the preset processing may be weighting, preset function, normalization, and the like, and this is not specifically processed in the embodiment of the present application.

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

In a possible implementation, if the sports index evaluation device obtains a set of data of at least one sports characteristic parameter during the sports process through one measurement in S301, in S302, the sports index evaluation device inputs each sports characteristic parameter in the set of data into a respective scoring model to obtain a respective scoring value.

In a possible implementation, if the motion index evaluation device obtains multiple sets of data of at least one motion characteristic parameter during the motion process through multiple measurements in S301, in S302, the motion index evaluation device averages each motion characteristic parameter in the multiple sets of data, and inputs the average values into respective scoring models to obtain respective scoring values.

In a possible implementation, if the sports index evaluation device obtains multiple sets of data of at least one sports characteristic parameter during the sports process through multiple measurements in S301, in S302, the sports index evaluation device inputs each sports characteristic parameter in the multiple sets of data into a respective scoring model to obtain multiple scoring values of each sports characteristic parameter, and then averages the scoring values to obtain a final scoring value.

For example, it is assumed that the running characteristic parameters to be acquired include: step length, step frequency and leg swing angle, three groups of data of running characteristic parameters are sampled and obtained by the motion index evaluation device in S301, and the record is as follows: { step 1, step frequency 1, and swing leg angle 1}, { step 2, step frequency 2, and swing leg angle 2}, { step 3, step frequency 3, and swing leg angle 3 }.

In a possible implementation, in S302, the exercise index evaluation device may average a plurality of records of each running characteristic parameter and then input the average to the scoring model to obtain a score value, that is, the average of step length 1, step length 2, and step length 3 is input to the step length scoring model to obtain a score value of step length; inputting the average value of the step frequency 1, the step frequency 2 and the step frequency 3 into a step frequency scoring model to obtain a scoring value of the step frequency; inputting the average values of the leg swinging angle 1, the leg swinging angle 2 and the leg swinging angle 3 into the leg swinging angle scoring model to obtain the scoring value of the leg swinging angle.

In a possible implementation, in S302, the exercise index evaluating device may input the plurality of records of each running characteristic parameter into the scoring model to obtain score values, and then average the score values to obtain a final score value. Inputting step length 1, step length 2 and step length 3 into a step length scoring model to obtain the scoring value of step length 1, the scoring value of step length 2 and the scoring value of step length 3, and averaging the scoring value of step length 1, the scoring value of step length 2 and the scoring value of step length 3 to be used as step length scoring values; inputting the step frequency 1, the step frequency 2 and the step frequency 3 into a step frequency scoring model to obtain a score of the step frequency 1, a score of the step frequency 2 and a score of the step frequency 3, and taking the average of the score of the step frequency 1, the score of the step frequency 2 and the score of the step frequency 3 as a step frequency score; inputting the leg swing angle 1, the leg swing angle 2 and the leg swing angle 3 into the leg swing angle scoring model to obtain the score value of the leg swing angle 1, the score value of the leg swing angle 2 and the score value of the leg swing angle 3, and averaging the score value of the leg swing angle 1, the score value of the leg swing angle 2 and the score value of the leg swing angle 3 to obtain the leg swing angle score value.

And S303, substituting the score value of the motion characteristic parameter into a preset calculation expression of the motion index by the motion index evaluation device to obtain an evaluation result of the motion index.

Wherein the sports index is used for reflecting the state of the user using the electronic equipment to which the sports index evaluation device belongs.

For example, sports metrics may include, but are not limited to: exercise capacity, or exercise efficiency, or risk of injury. The sports index can be defined according to actual requirements, which is not specifically limited in the embodiment of the present application, and all indexes defined according to the score values of the sports characteristic parameters and used for reflecting 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 motion characteristic parameter may be substituted into a preset calculation expression of at least one motion index to obtain an evaluation result of the at least one motion index. The number of the estimated exercise indexes is not specifically limited in the embodiment of the application, and can be determined according to actual requirements.

Specifically, each sports index has a sports characteristic parameter depending on the state of the user, which each sports index reflects, and the preset calculation expression of the sports index is a function of the score value of the sports characteristic parameter depending on. Of course, a motion characteristic parameter that a motion index depends on may be determined according to actual requirements, which is not specifically limited in this embodiment of the present application.

Optionally, the preset calculation expression of the sports index is a function of the score values of the sports characteristic parameters on which the preset calculation expression of the sports index depends, and the function may be a simple linear function, such as a weighted sum; alternatively, the function may also be an exponential function model or other types of functions, and the embodiment of the present application is not particularly limited to the type of the function.

In one possible implementation, when the pre-set computational expression of the sports index is then a weighted sum of the scoring values for the sports characteristic parameters on which it depends, the sum of the respective weighting systems is 1.

For example, the exercise index is assumed to be the running ability, and the running ability is mainly comprehensively evaluated on the muscle strength, endurance and the like of the user. Intuitively understand that for one run, if the user's run is long in duration, the speed is fast, indicating that the user's ability to run is strong. Therefore, the running ability is related to the running speed, the running time, the leg swing angle, the landing time and the ground pedaling acceleration, that is, the running ability depends on the running speed, the running time, the leg swing angle, the landing time and the ground pedaling acceleration, and therefore, the preset calculation expression for the running ability is defined as follows:

the running ability is f1 (pace score, exercise time score, leg swing angle score, landing time score, and kick-off acceleration score).

For example, to simplify the functional model, a linear model may be used, such as: running ability score value k1+ k2+ time of movement score value k3+ k4+ time of landing score value k 5. Wherein k1+ k2+ k3+ k4+ k5 is 1. k1, k2, k3, k4, and k5 are weighting coefficients, and may be determined according to actual requirements, which is not specifically limited in this embodiment of the present application.

For example, running efficiency may be the furthest distance run with the least amount of energy. If two users run consuming the same calories, but user A runs farther than user B, user A's running efficiency is higher than user B. Therefore, the running efficiency can be defined to depend on the step frequency, the leg swing time, the landing time, the step length and the vertical movement distance, and the preset calculation expression for defining the running efficiency is as follows:

the running efficiency was f2 (step frequency score, leg rest time score, landing time score, step size score, vertical movement distance score).

For example, to simplify the functional model, a linear model may be used, such as: running efficiency w1 step score + w2 normalation (step score/vertical movement distance score) + w3 normalation (leg swing time score/landing time score).

Wherein w1+ w2+ w3 is 1. The values of w1, w2, and w3 may be determined according to actual requirements, and this is not specifically limited in this embodiment of the present application.

The normalization () is a normalization function, and is mapped to a value interval of the running efficiency score. In the above equation, the ratio of the step size to the vertical movement distance is used, and it can be understood that if the step size/vertical movement distance value is larger, as much energy as possible is used for the movement in the forward direction, not the movement in the vertical direction, and thus the running efficiency is high. Likewise, the greater the value of swing/landing time, the longer the user spends in the air, the more time exercising, and the more efficient the running.

For example, the risk of injury is used to assess the likely risk of injury to the user. The higher the assessment result of the injury risk, the greater the injury risk.

For example, if the step length is too large, the landing point is likely to be located in front of the center of gravity of the body, which tends to cause a braking effect and causes a large damage to the ankle and knee. The risk of injury depends on the step size, landing impact, and landing angle, and therefore, the preset calculation expression defining the risk of injury is:

the risk of injury is f3 (step score, landing impact score, landing angle score).

For example, to simplify the functional model, a linear model may be used, with a risk of injury of m1 step score + m2 landing impact score + m3 landing angle score.

Wherein m1+ m2+ m3 is 1. m1, m2 and m3 can be determined according to actual requirements, and the embodiment of the application is not particularly limited in this respect.

It should be noted that the above examples illustrate the process of determining the evaluation result of the exercise index (running ability, running efficiency, injury risk) by way of example, but are not limited thereto.

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

Wherein, 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 preset motion establishing database can be determined according to actual requirements, and this is not specifically limited in the embodiment of the present application.

Illustratively, if the user's motor ability score is low, the user is correspondingly advised to do more cardiopulmonary function related training to improve lower limb strength. If the running efficiency of the user is low, the user is advised to increase the step frequency and reduce the displacement in the vertical direction. And if the injury risk score of the user is high, the user is advised to pay attention to the landing impact strength, and the step length is prevented from being too large.

In one possible implementation, the preset exercise suggestion database includes an exercise suggestion library of at least one exercise index, and one exercise suggestion library is based on one exercise index. After the evaluation result of at least one exercise index is obtained in S303, in S304, the exercise suggestion library of each exercise index in the preset exercise suggestion database is queried to obtain an exercise suggestion of each exercise index, and all the obtained exercise suggestions are displayed to the user in S304.

In one possible implementation, the preset motion suggestion database includes motion suggestion databases of at least one motion index, which are independently distributed, as shown in table 2, which illustrates a preset motion suggestion database.

TABLE 2

With the preset exercise suggestion database illustrated in table 2, assuming that the evaluation result of exercise capacity obtained in S303 is located in the interval 3 and the evaluation result of exercise efficiency is located in the interval 4, the suggestion c and the suggestion x are displayed to the user in S304.

In one possible implementation, the preset motion suggestion database includes a merged distribution of motion suggestion databases of at least one motion index, as shown in table 3, which illustrates a preset motion suggestion database.

TABLE 3

	Motion efficiency interval a	Section b of motion efficiency	Section c of motion efficiency	……
Exercise capacity interval 1				……
Exercise capacity interval 2				……
Exercise capacity interval 3		Exercise advice A		……
Exercise capacity interval 6				……
……	……	……	……	……

With the preset exercise suggestion database illustrated in table 3, assuming that the evaluation result of exercise capacity obtained in S303 is located in the exercise capacity interval 3 and the evaluation result of exercise efficiency is located in the exercise efficiency interval b, the exercise suggestion a is displayed to the user in S304.

It should be noted that the contents of table 2 and table 3 and the related description are exemplary and not limiting to the specific implementation of S304. In practical applications, the content of the preset motion suggestion database may be configured according to requirements to implement S304.

In the embodiment of the present application, the motion index estimation apparatus may be divided into the functional modules 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 integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The division of the modules in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation.

Fig. 6 shows a schematic diagram of a possible structure of the motion index estimation apparatus according to the above embodiment, in a case where each function module is divided according to each function. As shown in fig. 6, the sports index evaluation device 60 includes an acquisition unit 601, a scoring unit 602, and an evaluation unit 603.

Among other things, the obtaining unit 601 is used to support the athletic index evaluation device 60 to perform step S301 in the athletic index evaluation method illustrated in fig. 3, and/or other processes for the techniques described herein.

The scoring unit 602 is configured to support the athletic index evaluation device 60 to perform step S302 in the athletic index evaluation method illustrated in fig. 3 and/or other processes for the techniques described herein.

The evaluation unit 603 is used to support the sports index evaluation device 60 to perform step S303 in the sports index evaluation method illustrated in fig. 3, and/or other processes for the techniques described herein.

All relevant contents of each step related to the above method embodiment may be referred to the functional description of the corresponding functional module, and are not described herein again.

Of course, the convolution device 80 for neural network provided by the embodiment of the present application includes, but is not limited to, the above modules. As shown in fig. 6, the sports index evaluation device 60 may further include a display unit 604 for supporting the sports index evaluation device 60 to perform step S304 in the sports index evaluation method illustrated in fig. 3, and/or other processes for the techniques described herein.

In the case of using an integrated unit, a schematic structural diagram of the exercise index evaluation device provided in the embodiment of the present application is shown in fig. 7. In fig. 7, the sports index evaluation device 70 includes: a processing module 701 and a communication module 702. The processing module 701 is used for controlling and managing the actions of the sports index evaluation device 70, for example, executing the steps executed by the above-mentioned obtaining unit 601, scoring unit 602, evaluation unit 603, and/or other processes for executing the techniques described herein. The communication module 702 is used to support the interaction between the sports index evaluation apparatus 70 and other devices. As shown in fig. 7, the sports index evaluation device 70 may further include a storage module 703, and the storage module 703 is used for storing program codes and data of the sports index evaluation device 70.

The processing module 701 may be a Processor or a controller, and may be, for example, a CPU, a general-purpose Processor, a Digital Signal Processor (DSP), an ASIC, an FPGA or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. The processor may also be a combination of computing functions, e.g., comprising one or more microprocessors, DSPs, and microprocessors, among others. The communication module 702 may be a transceiver, an RF circuit or a communication interface, etc. The storage module 703 may be a memory.

If the exercise index evaluating apparatus 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 including one or more program codes, where the one or more programs include instructions, and when a processor in a sports index evaluation device executes the program codes, the sports index evaluation device executes the sports index evaluation method.

In another embodiment of the present application, there is also provided a computer program product comprising computer executable instructions stored in a computer readable storage medium; the at least one processor of the athletic metric evaluation device may read the computer executable instructions from the computer readable storage medium, and the execution of the computer executable instructions by the at least one processor causes the athletic metric evaluation device to perform the steps of performing the athletic metric evaluation method described above.

In the above embodiments, all or part of the implementation may be realized by software, hardware, firmware or any combination thereof. When implemented using a software program, may take the form of a computer program product, either entirely or partially. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part.

The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk (ssd)), among others.

Through the above description of the embodiments, it is clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device may be divided into different functional modules to complete all or part of the above described functions.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical functional division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another device, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

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

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially or partially contributed to by the prior art, or all or part of the technical solutions may be embodied in the form of a software product, where the software product is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

The above description is only an embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions within the technical scope of the present disclosure should be covered by the scope of the present 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 is characterized in that,

acquiring at least one motion characteristic parameter of a user in a motion process;

obtaining the score value of each motion characteristic parameter according to the score model of each motion characteristic parameter;

and substituting the score values of the motion characteristic parameters into a preset calculation expression of the motion index to obtain an evaluation result of the motion index.
The sports index evaluation method according to claim 1, wherein the sports index includes:

exercise capacity, or exercise efficiency, or risk of injury.
A sports index evaluation method according to claim 1 or 2, characterized in that the method further comprises:

and displaying an exercise suggestion to the user, wherein the exercise suggestion corresponds to the evaluation result of the exercise index in a preset exercise suggestion database.
A sports index evaluation method according to any one of claims 1 to 3, wherein said obtaining a score value of each of said sports feature parameters based on said scoring model for each of said sports feature parameters comprises:

calculating the average value of the at least one motion characteristic parameter of the user under each action, and respectively inputting the average value of the at least one motion characteristic parameter into the respective scoring model of each motion characteristic parameter to obtain the scoring value of each motion characteristic parameter;

alternatively, the first and second electrodes may be,

respectively inputting the at least one motion characteristic parameter of the user under each action into a respective scoring model of each motion characteristic parameter to obtain a scoring value of each motion characteristic parameter under each action; and calculating the average value of the scoring values of the motion characteristic parameters to obtain the scoring value of each motion characteristic parameter.
An exercise index evaluation method according to any of claims 1-4, characterized in that the exercise comprises running or walking.
A sports metric evaluation method according to claim 5, characterized in that the sports characteristic parameters comprise at least one of the following parameters: step length, step frequency, leg swing angle, touchdown time, left foot balance, right foot balance, flight time, touchdown impact and vertical movement distance.
The athletic metric evaluation method of any of claims 1-6, wherein the pre-set computational expression comprises: a weighted sum of the scoring values of the motion characteristic parameters.
A sports index evaluation device is characterized in that,

the device comprises an acquisition unit, a processing unit and a control unit, wherein the acquisition unit is used for acquiring at least one motion characteristic parameter of a user in the motion process;

the scoring unit is used for acquiring the scoring value of each motion characteristic parameter according to the scoring model of each motion characteristic parameter;

and the evaluation unit is used for substituting the score value of the motion characteristic parameter acquired by the scoring unit into a preset calculation expression of the motion index to obtain an evaluation result of the motion index.
The sports index evaluation device according to claim 8, wherein the sports index includes:

exercise capacity, or exercise efficiency, or risk of injury.
Sports index evaluation device according to claim 8 or 9,

the obtaining unit is further used for obtaining the exercise suggestion corresponding to the evaluation result of the exercise index in the preset exercise suggestion database

The apparatus further comprises a display unit for displaying the movement suggestion to the user.
A sports metric evaluation device according to any of the claims 8-10, characterized in that the scoring unit is specifically configured to include:

respectively inputting the average value of the at least one motion characteristic parameter of the user under each action into a respective scoring model of each motion characteristic parameter to obtain a scoring value of each motion characteristic parameter;

alternatively, the first and second electrodes may be,

respectively inputting the at least one motion characteristic parameter of the user under each action into a respective scoring model of each motion characteristic parameter to obtain a scoring value of each motion characteristic parameter under each action; and calculating the average value of the scoring values of the motion characteristic parameters to obtain the scoring value of each motion characteristic parameter.
An exercise index evaluation device according to any of claims 8-11 wherein the exercise comprises running or walking.
A sports metric evaluation device according to claim 12, characterized in that the sports characteristic parameter comprises at least one of the following parameters: step length, step frequency, leg swing angle, touchdown time, left foot balance, right foot balance, flight time, touchdown impact and vertical movement distance.
An athletic metric evaluation device comprising a processor, a memory, and instructions stored on the memory and executable on the processor, which when executed, cause the device to perform the athletic metric evaluation method of any of claims 1-7.
An electronic device, characterized in that it comprises a sports index evaluation apparatus according to any one of claims 8-14.
A computer-readable storage medium comprising instructions that, when executed on a computer, cause the computer to perform the athletic metric evaluation method of any of claims 1-7.
A computer program product which, when run on a computer, causes the computer to perform the sports index assessment method of any one of claims 1 to 7.