Earphone-based action evaluation method and system, readable storage medium and computer
Obtain healthy exercise movement data through headphones, build an evaluation matrix and calculate the action score, solving the problem of inaccurate action evaluation in the existing technology, achieving fast and accurate action scores and corrections, and improving exercise effects.
Patent Information
- Application Number
- CN202411454046.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-17
- Publication Date
- 2024-12-10
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The existing methods of evaluating movements for healthy exercises rely on naked eyes to compare the movements, resulting in inaccurate movement scores, making it difficult for users to quickly correct movement differences, affecting the exercise effect.
The action data is obtained through the headphones worn by the user, the evaluation index matrix is constructed, the action score is calculated using matrix algorithms and weight data, and the action evaluation is performed in combination with the influence indicators to provide accurate action scores.
It improves the accuracy and speed of action evaluation, helps users quickly identify movement differences and correct them, and improves the exercise effect.
Smart Images
Figure CN119110212A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of data processing technology, and in particular to an earphone-based action evaluation method, system, readable storage medium and computer. Background Art
[0002] For white-collar workers or people who work in offices for a long time, due to the intense work, the body may maintain a posture for a long time, which will cause the cervical spine to remain at an inappropriate angle for too long, causing fatigue to the cervical muscles. If the cervical spine is bent too much, it will also cause compression on the carotid artery, which will cause symptoms such as cervical pain and dizziness over time.
[0003] As people pay more and more attention to their health status, more and more people begin to do health exercises, such as cervical spine exercises, aerobics, etc. However, the current action scores for health exercises are only evaluated by comparing the difference between the amplitude of one's own movements and the amplitude of movements of the objects in the health exercise videos with the naked eye. The action scores obtained by this effect are inaccurate, so that users cannot quickly correct their movement differences in this way, resulting in poor health exercise effects. Summary of the invention
[0004] Based on this, an object of the present invention is to provide a headphone-based action evaluation method, system, readable storage medium and computer to at least solve the deficiencies in the above-mentioned technology.
[0005] The present invention provides a headphone-based action evaluation method, comprising: Using the earphones worn by the user, obtaining action data generated by the user performing a preset action, and constructing a number of evaluation indicators based on the action data; Constructing a corresponding evaluation index matrix according to each of the evaluation indexes, and performing data processing on each of the evaluation index matrices to construct a corresponding one-dimensional data matrix; Calculating a corresponding action score matrix according to the one-dimensional data matrix and the weight data, and calculating an average value and an interference average value of the action score matrix; An influence index of the preset action is defined, and the action data is evaluated according to the influence index, the average value of the action score matrix, and the interference average value to obtain the action score of the user.
[0006] Furthermore, the steps of obtaining action data generated by the user performing a preset action by using the earphones worn by the user, and constructing a plurality of evaluation indicators according to the action data include: Obtaining the action amplitude and action standard of the preset action, and defining the scoring rule of the preset action; The action data is indexed according to the scoring rules, the action amplitude and the action standard of the preset action to obtain corresponding evaluation indicators.
[0007] Furthermore, the evaluation index matrix includes an action standard matrix, an action amplitude matrix, a difference vector matrix and a plurality of influence vector matrices, and the step of performing data processing on each of the evaluation index matrices to construct a corresponding one-dimensional data matrix includes: Performing matrix calculation on the action standard matrix and the difference vector matrix using a matrix algorithm to obtain a corresponding optimal action matrix; Performing a dimensionality reduction operation on the optimal action matrix, and calculating the optimal action matrix after the dimensionality reduction operation and the action amplitude matrix to obtain a corresponding gap matrix; Matrix operations are performed on the influence vector matrices according to the gap matrix to construct corresponding one-dimensional data matrices.
[0008] Furthermore, the influencing index of the preset action includes an age index, a gender index and a body mass index, and the step of evaluating the action data according to the influencing index, the average value of the action score matrix and the interference average value to obtain the action score of the user includes: Calculating weight data of the age index, the gender index and the body mass index respectively, and calculating corresponding evaluation scores according to the weight data of the age index, the gender index and the body mass index and the age index, the gender index and the body mass index; The action data is evaluated according to the evaluation score, the average value of the action score matrix, and the interference average value to obtain the action score of the user.
[0009] Furthermore, the calculation formula of the evaluation score calculated by the age indicator is: 100 * Wa * Ca = y1; In the formula, Wa represents the weight data of the age indicator, and Ca represents the age coefficient; The calculation formula for the evaluation score calculated by the gender indicator is: 100 * Wg * Cg = y2; In the formula, Wg represents the weight data of gender indicators, and Cg represents the gender coefficient; The calculation formula of the evaluation score calculated by the body mass index is: 100 * Wb * Cb = y3; Where Wb represents the weight data of body mass index, and Cb represents body shape index.
[0010] The present invention also provides a headphone-based motion evaluation system, comprising: An indicator construction module, used to obtain action data generated by the user performing a preset action using the earphones worn by the user, and to construct a number of evaluation indicators based on the action data; A matrix processing module, used for constructing a corresponding evaluation index matrix according to each of the evaluation indexes, and performing data processing on each of the evaluation index matrices to construct a corresponding one-dimensional data matrix; A data calculation module, used to calculate a corresponding action score matrix according to the one-dimensional data matrix and the weight data, and calculate an average value and an interference average value of the action score matrix; The action evaluation module is used to define the influence index of the preset action, and evaluate the action data according to the influence index, the average value of the action score matrix and the interference average value to obtain the action score of the user.
[0011] Furthermore, the indicator construction module includes: A rule definition unit, used to obtain the action range and action standard of the preset action, and define a scoring rule for the preset action; The indicator construction unit is used to construct an indicator for the action data according to the scoring rule, the action amplitude and the action standard of the preset action to obtain a corresponding evaluation indicator.
[0012] Furthermore, the evaluation index matrix includes an action standard matrix, an action amplitude matrix, a difference vector matrix and a plurality of influence vector matrices, and the matrix processing module includes: A matrix calculation unit, used for performing matrix calculation on the action standard matrix and the difference vector matrix using a matrix algorithm to obtain a corresponding optimal action matrix; A dimensionality reduction operation unit, used for performing a dimensionality reduction operation on the optimal action matrix, and calculating the optimal action matrix after the dimensionality reduction operation with the action amplitude matrix to obtain a corresponding gap matrix; A matrix processing unit is used to perform matrix operations on each of the influence vector matrices according to the gap matrix to construct a corresponding one-dimensional data matrix.
[0013] Furthermore, the influencing index of the preset action includes an age index, a gender index and a body mass index, and the action evaluation module includes: An evaluation scoring unit, used to calculate the weight data of the age index, the gender index and the body mass index respectively, and calculate the corresponding evaluation score according to the weight data of the age index, the gender index and the body mass index and the age index, the gender index and the body mass index; An action evaluation unit is used to evaluate the action data according to the evaluation score, the average value of the action score matrix and the interference average value to obtain the action score of the user.
[0014] The present invention also provides a readable storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned headphone-based action evaluation method is implemented.
[0015] The present invention also provides a computer, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned headphone-based action evaluation method when executing the computer program.
[0016] The headset-based action evaluation method, system, readable storage medium and computer of the present invention obtain action data generated by a user performing an action through the headset, and construct a number of evaluation indicators based on the action data, and construct an evaluation indicator matrix using the evaluation indicators, perform data processing for different evaluation indicator matrices, and use weight data to quickly calculate the corresponding data average value, and quickly score the action data based on the data average value and the action influence indicator, so as to quickly identify the difference between the user's action data and the standard data while improving the data processing accuracy, so that the user can quickly correct his or her own action difference according to the action score. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] Figure 1 is a flow chart of a headphone-based action evaluation method in a first embodiment of the present invention; Figure 2 for Figure 1 Detailed flow chart of step S101; Figure 3 for Figure 1 Detailed flow chart of step S102; Figure 4 for Figure 1 Detailed flow chart of step S104; Figure 5 is a structural block diagram of a headphone-based motion evaluation system in a second embodiment of the present invention; Figure 6 FIG. 4 is a structural block diagram of a computer in a third embodiment of the present invention.
[0018] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION
[0019] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.
[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.
[0021] Embodiment 1 See also Figure 1 , which shows a method for evaluating an action based on headphones in a first embodiment of the present invention, and the method specifically includes steps S101 to S104: S101, using the earphones worn by the user to obtain action data generated by the user performing a preset action, and constructing a plurality of evaluation indicators according to the action data; For further information, see Figure 2 , the step S101 specifically includes steps S1011~S1012: S1011, obtaining the action range and action standard of the preset action, and defining the scoring rule of the preset action; S1012, constructing indicators for the action data according to the scoring rules, the action amplitude and action standard of the preset action to obtain corresponding evaluation indicators.
[0022] In the specific implementation, the action data formed by the user performing the preset action is obtained by using the earphones worn by the user. In this embodiment, the earphones worn can be in-ear Bluetooth earphones, head-mounted Bluetooth earphones or wired earphones and other related communication devices. In this embodiment, the preset action includes but is not limited to cervical spine exercises. Specifically, the cervical spine exercises are divided into 8 sections, each section of the action needs to be repeated three times, and the content of each section is as follows: Section 1: Leaning forward and backward: vertical movement of the head up and down; target angles: horizontal: 0°, vertical: -60° / 70°, left and right deviation: 0°.
[0023] Section 2: Left-right swaying: left-right tilting movement of the head; target angles: horizontal: 0°, vertical: 0°, left-right tilt: -60° / 60°.
[0024] Section 3: Looking left and right: horizontal movement of the head left and right; target angles: horizontal: -90° / 90°, vertical: 0°, left and right deviation: 0°.
[0025] Section 4: Lift shoulders and shrink neck: lift shoulders up and shrink head down; target angles: horizontal: 0°, vertical: -20° / 20°, left and right deviation: 0°.
[0026] Section 5: Duck neck exercise: extend the head forward, then press down, and then retract; target angles: horizontal: 0°, vertical: -60° / 30°, left and right deviation: 0°.
[0027] Section 6: Head-hand confrontation, hug the neck with both hands and tilt the head back; target angles: horizontal: 0°, vertical: 0° / 45°, left and right deviation: 0°.
[0028] Section 7: Shaking your head; 1> Shake your head horizontally left and right, 2> Shake your head vertically up and down, 3> Shake your head 45° to the left and raise your head 45°, then straighten it, 4> Shake your head 45° to the right and raise your head 45°, then straighten it, 5> Shake your head 45° to the left and raise your head 45°, then straighten it, and shake your head to the right, then straighten it, 6> Shake your head 45° to the right and raise your head 45°, then straighten it, and shake your head to the left, then straighten it; Target angles: horizontal: -70° / 70°, vertical: -45° / 70°, left and right deviation: -30° / 30°.
[0029] Section 8: Relaxation movements, rub your palms together to make them warm, pinch your neck muscles 10 times with your left and right hands alternately; target angles: horizontal: 0°, vertical: 0°, left and right deviation: 0°.
[0030] According to the above action definition corresponding scoring rules: 1> Adopt a deduction system 2> Points will be deducted if the standard is not met, and points will be added if the standard is exceeded 3> Extra (interference) action deduction 4> The better the physical condition (BMI), the higher the requirements, and vice versa 5> Women are more flexible than men, so they have higher requirements, and vice versa 6> The younger the age, the higher the requirements, and vice versa Based on the above rules, the corresponding scoring algorithm (i.e. evaluation indicators and their corresponding evaluation indicator matrix) is constructed:
[0031] S102, constructing a corresponding evaluation index matrix according to each of the evaluation indexes, and performing data processing on each of the evaluation index matrices to construct a corresponding one-dimensional data matrix; Furthermore, the evaluation index matrix includes an action standard matrix, an action amplitude matrix, a difference vector matrix and a plurality of influence vector matrices, see Figure 3, the step S102 specifically includes steps S1021 to S1023: S1021, performing matrix calculation on the action standard matrix and the difference vector matrix using a matrix algorithm to obtain a corresponding optimal action matrix; S1022, performing a dimensionality reduction operation on the optimal action matrix, and calculating the optimal action matrix after the dimensionality reduction operation and the action amplitude matrix to obtain a corresponding gap matrix; S1023, performing matrix operations on the influence vector matrices respectively according to the gap matrix to construct a corresponding one-dimensional data matrix.
[0032] In the specific implementation, the action standard matrix (i.e., the action standard requirement matrix S) and the difference vector matrix (Q) in the above evaluation index matrix are calculated according to the following formula to obtain the corresponding optimal action matrix: (S o Q) * (-1) = C;
[0033] Where “o” represents the Hadamard product operation of the matrix.
[0034] Specifically, the optimal action matrix obtained above is reduced in dimension to obtain the P0 matrix, but p7 = 0 in the P0 matrix. For the convenience of calculation, p7 is set to the sum of all direction scales, that is, p7 = |-90| + 90 + |-90| + 90+ |-60| + 60 = 480. Divide 1 by each element of P1 to obtain P2.
[0035]
[0036] Furthermore, the gap matrix E between the actual value and the target value is obtained:
[0037] Use matrix E and matrix Q to calculate the Hadamard product and get the gap matrix F between each action and the target value:
[0038] Using matrix F and matrix R to perform Hadamard product calculation, we can get the positive and negative influence value matrix G of each action:
[0039] Specifically, the Hadamard product is calculated using the matrix F and the matrix V to obtain the effective value matrix H of each action:
[0040] Use the matrix F and the matrix (1-V) to calculate the Hadamard product and obtain the interference value matrix J for each action:
[0041] Multiply the effective value matrix H and the dimension reduction matrix Y to obtain a one-dimensional effective matrix M (i.e., a one-dimensional data matrix).
[0042] S103, calculating a corresponding action score matrix according to the one-dimensional data matrix and the weight data, and calculating an average value and an interference average value of the action score matrix; In the specific implementation, the one-dimensional data matrix and weight data obtained above are used to calculate the corresponding action score matrix U (action score for each section): 100 * We * 0.8 * (1+M o P2) = U, where 0.8 is the weight of the effective value and 0.2 is the weight of the interference value. The average value u_avg of the action score matrix U is calculated: u_avg = 61.09401709:
[0043] At the same time, the average value of the interference value of each section is calculated as t_avg = -2.205235043:
[0044] S104, defining an influence index of the preset action, and evaluating the action data according to the influence index, the average value of the action score matrix, and the interference average value to obtain the action score of the user.
[0045] Furthermore, the impact index of the preset action includes age index, gender index and body mass index, please refer to Figure 4 , the step S104 specifically includes steps S1041-S1042: S1041, respectively calculating weight data of the age index, the gender index, and the body mass index, and calculating a corresponding evaluation score according to the weight data of the age index, the gender index, and the body mass index and the age index, the gender index, and the body mass index; S1042: Evaluate the action data according to the evaluation score, the average value of the action score matrix, and the interference average value to obtain the action score of the user.
[0046] In specific implementation, the influence of age index on the above action scores is calculated according to the following table: 100 * Wa * Ca = y1: Assume that the user is older than 40 years old, Ca = 1.0; We can get y1 = 100 * 0.08 * 1.0 = 8.
[0047]
[0048] Furthermore, the influence of gender index on the above action scores is calculated according to the following table: 100 * Wg * Cg = y2; Assuming the user is male, Cg = 1.0; We can get y2 = 100 * 0.04 * 1 = 4:
[0049] Specifically, the effect of body mass index on the above action scores is calculated according to the following table: 100 * Wb * Cb = y3; Assuming the user's BMI is 27.5, then Cb = 0.6; We can get y3 = 100 * 0.08 * 0.6 = 4.8:
[0050] Among them, the BMI calculation formula is: weight / height^2, the unit of weight is: Kg, and the unit of height is: m.
[0051] Add the above effective value u_avg and interference value t_avg u_avg + t_avg = y0, that is, y0 = 58.88878205, and add it to the evaluation score, that is: y0+y1+y2+y3 = z; For example: y0 = 58.88878205; y1 = 8, y2 = 4, y3 = 4.8, z = 58.88878205 + 8+ 4 +4.8 = 75.7.
[0052] That is, the total score of 75.7 is the final score of this user's action.
[0053] In summary, the headphone-based action evaluation method in the above-mentioned embodiment of the present invention obtains the action data generated by the user's action execution through the headphone, and constructs a number of evaluation indicators based on the action data, and constructs an evaluation indicator matrix using the evaluation indicators, performs data processing for different evaluation indicator matrices, and uses weight data to quickly calculate the corresponding data average value, and quickly scores the action data according to the data average value and the action influence indicator, so as to quickly identify the difference between the user's action data and the standard data while improving the data processing accuracy, so that the user can quickly correct his own action difference according to the action score.
[0054] Embodiment 2 Another aspect of the present invention is to provide a headphone-based motion evaluation system. Figure 5 , which is a headphone-based action evaluation system in a second embodiment of the present invention, and the system comprises: An indicator construction module 11 is used to obtain action data generated by the user performing a preset action using the earphones worn by the user, and to construct a number of evaluation indicators based on the action data; Furthermore, the indicator construction module 11 includes: A rule definition unit, used to obtain the action range and action standard of the preset action, and define a scoring rule for the preset action; The indicator construction unit is used to construct an indicator for the action data according to the scoring rule, the action amplitude and the action standard of the preset action to obtain a corresponding evaluation indicator.
[0055] A matrix processing module 12 is used to construct a corresponding evaluation index matrix according to each of the evaluation indexes, and perform data processing on each of the evaluation index matrices to construct a corresponding one-dimensional data matrix; Furthermore, the evaluation index matrix includes an action standard matrix, an action amplitude matrix, a difference vector matrix and a plurality of influence vector matrices, and the matrix processing module 12 includes: A matrix calculation unit, used for performing matrix calculation on the action standard matrix and the difference vector matrix using a matrix algorithm to obtain a corresponding optimal action matrix; A dimensionality reduction operation unit, used for performing a dimensionality reduction operation on the optimal action matrix, and calculating the optimal action matrix after the dimensionality reduction operation with the action amplitude matrix to obtain a corresponding gap matrix; A matrix processing unit is used to perform matrix operations on each of the influence vector matrices according to the gap matrix to construct a corresponding one-dimensional data matrix.
[0056] A data calculation module 13 is used to calculate a corresponding action score matrix according to the one-dimensional data matrix and the weight data, and calculate an average value and an interference average value of the action score matrix; The action evaluation module 14 is used to define the influence index of the preset action, and evaluate the action data according to the influence index, the average value of the action score matrix and the interference average value to obtain the action score of the user.
[0057] Furthermore, the influencing index of the preset action includes an age index, a gender index and a body mass index, and the action evaluation module 14 includes: An evaluation scoring unit, used to calculate the weight data of the age index, the gender index and the body mass index respectively, and calculate the corresponding evaluation score according to the weight data of the age index, the gender index and the body mass index and the age index, the gender index and the body mass index; An action evaluation unit is used to evaluate the action data according to the evaluation score, the average value of the action score matrix and the interference average value to obtain the action score of the user.
[0058] The functions or operation steps implemented when the above modules and units are executed are generally the same as those in the above method embodiments, and will not be repeated here.
[0059] The implementation principle and technical effects of the headphone-based action evaluation system provided in the embodiment of the present invention are the same as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the system embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.
[0060] Embodiment 3 The present invention also provides a computer, see Figure 6 , shown is a computer in the third embodiment of the present invention, including a memory 10, a processor 20, and a computer program 30 stored in the memory 10 and executable on the processor 20. When the processor 20 executes the computer program 30, the above-mentioned headphone-based action evaluation method is implemented.
[0061] The memory 10 includes at least one type of readable storage medium, which includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 10 may be an internal storage unit of a computer, such as a hard disk of the computer. In other embodiments, the memory 10 may also be an external storage device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card, etc. Further, the memory 10 may also include both an internal storage unit of the computer and an external storage device. The memory 10 may be used not only to store application software and various types of data installed in the computer, but also to temporarily store data that has been output or is to be output.
[0062] Among them, in some embodiments, the processor 20 can be an electronic control unit (Electronic Control Unit, abbreviated as ECU, also known as a vehicle computer), a central processing unit (Central Processing Unit, CPU), a controller, a microcontroller, a microprocessor or other data processing chip, used to run the program code stored in the memory 10 or process data, such as executing access restriction programs, etc.
[0063] It should be pointed out that Figure 6 The structure shown does not constitute a limitation on the computer. In other embodiments, the computer may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.
[0064] The embodiment of the present invention further provides a readable storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned earphone-based action evaluation method is implemented.
[0065] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.
[0066] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.
[0067] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.
[0068] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0069] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.
Claims
1. A method for evaluating an action based on headphones, characterized in that: include: Using the earphones worn by the user, obtaining action data generated by the user performing a preset action, and constructing a number of evaluation indicators based on the action data; Constructing a corresponding evaluation index matrix according to each of the evaluation indexes, and performing data processing on each of the evaluation index matrices to construct a corresponding one-dimensional data matrix; Calculating a corresponding action score matrix according to the one-dimensional data matrix and the weight data, and calculating an average value and an interference average value of the action score matrix; An influence index of the preset action is defined, and the action data is evaluated according to the influence index, the average value of the action score matrix, and the interference average value to obtain the action score of the user.
2. The headphone-based motion evaluation method according to claim 1, characterized in that: The steps of obtaining action data generated by the user performing a preset action by using the earphones worn by the user, and constructing a plurality of evaluation indicators according to the action data include: Obtaining the action amplitude and action standard of the preset action, and defining the scoring rule of the preset action; The action data is indexed according to the scoring rules, the action amplitude and the action standard of the preset action to obtain corresponding evaluation indicators.
3. The headphone-based motion evaluation method according to claim 1, characterized in that: The evaluation index matrix includes an action standard matrix, an action amplitude matrix, a difference vector matrix and a plurality of influence vector matrices. The step of performing data processing on each of the evaluation index matrices to construct a corresponding one-dimensional data matrix includes: Performing matrix calculation on the action standard matrix and the difference vector matrix using a matrix algorithm to obtain a corresponding optimal action matrix; Performing a dimensionality reduction operation on the optimal action matrix, and calculating the optimal action matrix after the dimensionality reduction operation and the action amplitude matrix to obtain a corresponding gap matrix; Matrix operations are performed on the influence vector matrices according to the gap matrix to construct corresponding one-dimensional data matrices.
4. The headphone-based motion evaluation method according to claim 1, characterized in that: The influence index of the preset action includes an age index, a gender index and a body mass index, and the step of evaluating the action data according to the influence index, the average value of the action score matrix and the interference average value to obtain the action score of the user includes: Calculating weight data of the age index, the gender index and the body mass index respectively, and calculating corresponding evaluation scores according to the weight data of the age index, the gender index and the body mass index and the age index, the gender index and the body mass index; The action data is evaluated according to the evaluation score, the average value of the action score matrix, and the interference average value to obtain the action score of the user.
5. The headphone-based motion evaluation method according to claim 4, characterized in that: The calculation formula of the evaluation score calculated by the age index is: 100 * Wa * Ca = y1; In the formula, Wa represents the weight data of the age indicator, and Ca represents the age coefficient; The calculation formula for the evaluation score calculated by the gender indicator is: 100 * Wg * Cg = y2; In the formula, Wg represents the weight data of gender indicators, and Cg represents the gender coefficient; The calculation formula of the evaluation score calculated by the body mass index is: 100 * Wb * Cb = y3; Where Wb represents the weight data of body mass index, and Cb represents body shape index.
6. An earphone-based motion evaluation system, characterized in that: include: An indicator construction module, used to obtain action data generated by the user performing a preset action using the earphones worn by the user, and to construct a number of evaluation indicators based on the action data; A matrix processing module, used for constructing a corresponding evaluation index matrix according to each of the evaluation indexes, and performing data processing on each of the evaluation index matrices to construct a corresponding one-dimensional data matrix; A data calculation module, used to calculate a corresponding action score matrix according to the one-dimensional data matrix and the weight data, and calculate an average value and an interference average value of the action score matrix; The action evaluation module is used to define the influence index of the preset action, and evaluate the action data according to the influence index, the average value of the action score matrix and the interference average value to obtain the action score of the user.
7. The headphone-based motion evaluation system according to claim 6, characterized in that: The indicator building module includes: A rule definition unit, used to obtain the action range and action standard of the preset action, and define a scoring rule for the preset action; The indicator construction unit is used to construct an indicator for the action data according to the scoring rule, the action amplitude and the action standard of the preset action to obtain a corresponding evaluation indicator.
8. The headphone-based motion evaluation system according to claim 6, characterized in that: The evaluation index matrix includes an action standard matrix, an action amplitude matrix, a difference vector matrix and a plurality of influence vector matrices, and the matrix processing module includes: A matrix calculation unit, used for performing matrix calculation on the action standard matrix and the difference vector matrix using a matrix algorithm to obtain a corresponding optimal action matrix; A dimensionality reduction operation unit, used for performing a dimensionality reduction operation on the optimal action matrix, and calculating the optimal action matrix after the dimensionality reduction operation with the action amplitude matrix to obtain a corresponding gap matrix; A matrix processing unit is used to perform matrix operations on each of the influence vector matrices according to the gap matrix to construct a corresponding one-dimensional data matrix.
9. A readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the headphone-based action evaluation method according to any one of claims 1 to 5 is implemented.
10. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the headphone-based action evaluation method according to any one of claims 1 to 5 is implemented.