A human posture analysis system and method for sports equipment based on big data

Abstract

The present invention discloses a human posture analysis system and method for sports equipment based on big data, which belongs to the field of data processing. The present invention imports the acquired usage data of sports equipment into a normal usage model of sports equipment to perform sports equipment usage analysis, imports the acquired human posture data information of exercise into a human posture analysis strategy to perform human posture analysis, obtains sports equipment usage analysis results and human posture analysis results and imports them into a motion matching analysis model to perform motion matching analysis, performs early warning of the exercise process of exercisers based on the motion matching analysis results, analyzes the matching degree between exercisers and sports equipment, improves the safety of exercisers during exercise, and avoids the occurrence of dangerous accidents during exercise.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular to a human posture analysis system and method for sports equipment based on big data. Background Art

[0002] Sports equipment using human posture analysis can help athletes better understand their body movements, thereby improving their performance and skill levels. By using human posture analysis technology, sports equipment can provide athletes with a better sports experience and feedback, thereby helping them improve their performance and skill levels. In addition, human posture analysis can also be used in the prevention and rehabilitation of sports injuries, as well as sports biomechanics research.

[0003] For example, the patent with publication number CN109344790A discloses a method and system for analyzing human behavior based on posture analysis, the method comprising: step S1, building a data set, estimating the two-dimensional coordinates of the human skeleton joints for each image in the data set, using the two-dimensional coordinate data to train the SVM classifier, and generating a human posture classification model; step S2, obtaining an image to be analyzed, estimating the two-dimensional coordinates of the human skeleton joints of each person in the image to be analyzed, and using the two-dimensional coordinates as feature data; step S3, inputting the feature data obtained in step S2 into the trained human posture classification model, and outputting the human posture analysis result. The invention can improve the accuracy of human behavior analysis;

[0004] At the same time, for example, the patent with publication number CN103559491A involves a human motion capture and posture analysis system, as well as a method for using the system for foreground detection and automatic extraction of joints. This system captures human motion images through a camera, extracts human foreground and human joints from them, and then analyzes human motion through tracking and recognition algorithms. The invention uses an algorithm that combines background difference method and inter-frame difference method to extract human targets in the video, overcoming the defect that foreground detection based on background difference method is prone to background noise interference. And a multi-algorithm fusion method is used to realize automatic initialization of human joints, overcoming the defects of traditional joint extraction methods that require optical markers or manual annotations, as well as the defects of existing automatic extraction methods that have large computational complexity and low accuracy. This system has low complexity and high accuracy, which improves the intelligence and efficiency of the system.

[0005] The above patents all have the problems raised by this background technology: the existing technology is unable to analyze the degree of matching between the exercisers and the sports equipment, which reduces the safety of the exercisers during the exercise and causes dangerous accidents to the exercisers during the exercise. The above problems exist in the existing technology. In order to solve these problems, this application designs a human posture analysis system and method for sports equipment based on big data. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention proposes a human posture analysis system and method for sports equipment based on big data.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A human body posture analysis method for sports equipment based on big data, comprising the following specific steps:

[0009] S1. Obtaining usage data of sports equipment and simultaneously obtaining data information of human posture when exercising on the sports equipment;

[0010] S2, importing the acquired usage data of the sports equipment into a normal usage model of the sports equipment to perform sports equipment usage analysis;

[0011] S3, importing the acquired human posture data information of the exercise into the human posture analysis strategy to perform human posture analysis;

[0012] S4, obtaining sports equipment use analysis results and human posture analysis results and importing them into a motion matching analysis model to perform motion matching analysis;

[0013] S5. Provide early warning of the exercise process of the exerciser based on the results of the motion matching analysis.

[0014] A further improvement of the present invention is that S1 comprises the following specific steps:

[0015] S11, acquiring operation data of the sports equipment during operation through a sports equipment data acquisition sensor, wherein the operation data of the sports equipment during operation includes swing amplitude, swing frequency data and image data of the moving part of the sports equipment during operation, and is stored in a storage module, wherein the moving part of the sports equipment is, for example, a cycling trainer, and the moving part of the sports equipment is a pedal part of the cycling trainer;

[0016] S12. The human body posture images during the movement are collected by the image acquisition component, and standard movement posture image data are extracted and stored in the storage module. It should be noted that the movement posture image data here is the optimal posture image data of the human body performing the movement which is output according to the shape of the human body.

[0017] A further improvement of the present invention is that the specific content of the normal use model of the sports equipment in S2 is as follows:

[0018] S21, obtaining the swing amplitude, swing frequency data and motion part image data of the motion part during the operation of the sports equipment during the training period;

[0019] S22, importing the swing amplitude, swing frequency data and image data of the moving part of the sports equipment during operation into the sports equipment use abnormal value calculation formula to calculate the sports equipment use abnormal value, wherein the sports equipment use abnormal value calculation formula is: Among them, T is the duration of the running process, M t is the average oscillation frequency of the moving part in time period t, is the maximum swing frequency of the moving part within the safe range in time period t, s t is the average swing amplitude of the moving part in time period t, is the swing amplitude safety value of the moving part, dt is the time integral, n is the number of pixels of the moving part image, x i is the pixel value of the i-th pixel of the motion image after operation, x im is the pixel value of the i-th pixel of the motion image before running, a q is the swing anomaly ratio coefficient, a p is the image abnormality ratio, where a q +a p =1.

[0020] A further improvement of the present invention is that the specific content of the human posture analysis strategy in S3 is:

[0021] S31, acquiring a human body posture image during the motion process, and acquiring standard motion posture image data at the same time, merging the two images at the same scale, and acquiring the overlap area of ​​the overlapped parts of the two image sequences and the total overlapped area;

[0022] S32, importing the overlap area of ​​the overlapped parts of the two image sequences and the total overlapped area into the human body posture analysis abnormal value calculation formula to calculate the human body posture analysis abnormal value, wherein the human body posture analysis abnormal value calculation formula is: Where z is the number of images in the image sequence, S() is the area of ​​the image in brackets, and c j is the j-th human posture image during the movement, is the standard motion posture image corresponding to the j-th human posture image during the motion process.

[0023] A further improvement of the present invention is that the motion matching analysis model in S4 includes the following specific contents:

[0024] Obtain the calculated sports equipment usage anomaly values ​​and human posture analysis anomaly values, add the calculated sports equipment usage anomaly values ​​and human posture analysis anomaly values ​​to calculate the motion matching coefficient, compare the obtained motion matching coefficient with the set motion matching coefficient threshold, if the obtained motion matching coefficient is greater than or equal to the motion matching coefficient threshold, it means that the corresponding sports equipment does not match the corresponding exerciser, if the obtained motion matching coefficient is less than the motion matching coefficient threshold, it means that the corresponding sports equipment matches the corresponding exerciser.

[0025] A further improvement of the present invention is that S5 includes the following specific contents:

[0026] Obtain the matching results between the corresponding sports equipment and the corresponding exercisers, and provide a visual early warning of the exerciser's exercise process based on the matching results.

[0027] It should be noted that the swing abnormality ratio coefficient, image abnormality ratio coefficient and motion matching coefficient threshold value are obtained as follows: 500 sets of safe exercise process data and 500 sets of dangerous exercise process data are obtained, and the data are imported to calculate the motion matching coefficient, and the calculation results and classification results are imported into the fitting software to obtain the swing abnormality ratio coefficient, image abnormality ratio coefficient and motion matching coefficient threshold value that meet the classification accuracy;

[0028] A human posture analysis system for sports equipment based on big data is implemented based on the above-mentioned human posture analysis method for sports equipment based on big data, and specifically includes: a data acquisition module, a sports equipment use analysis module, a human posture analysis module, a motion matching analysis module, an early warning module and a control module. The data acquisition module is used to obtain the use data of the sports equipment and simultaneously obtain the human posture data information of exercising on the sports equipment. The sports equipment use analysis module is used to import the acquired sports equipment use data into a normal use model of the sports equipment to perform sports equipment use analysis.

[0029] A further improvement of the present invention is that the human posture analysis module is used to import the acquired human posture data information of exercise into the human posture analysis strategy to perform human posture analysis, the motion matching analysis module is used to obtain the sports equipment use analysis results and the human posture analysis results and import them into the motion matching analysis model to perform motion matching analysis, and the early warning module is used to issue an early warning of the exercise process of the exerciser according to the motion matching analysis results.

[0030] A further improvement of the present invention is that the control module is used to control the operation of the data acquisition module, the sports equipment use analysis module, the human posture analysis module, the motion matching analysis module and the early warning module.

[0031] An electronic device comprises: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0032] The processor executes the above-mentioned human posture analysis method for sports equipment based on big data by calling the computer program stored in the memory.

[0033] A computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute a human posture analysis method for sports equipment based on big data as described above.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The present invention obtains usage data of sports equipment, and simultaneously obtains data information of human posture during exercise on the sports equipment, imports the obtained usage data of the sports equipment into a normal usage model of the sports equipment to perform sports equipment usage analysis, imports the obtained data information of human posture during exercise into a human posture analysis strategy to perform human posture analysis, obtains sports equipment usage analysis results and human posture analysis results and imports them into a motion matching analysis model to perform motion matching analysis, performs early warning of the exercise process of exercisers based on the motion matching analysis results, analyzes the degree of matching between the exercisers and the sports equipment, thereby improving the safety of the exercisers during exercise and avoiding the occurrence of dangerous accidents during exercise. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a flow chart of a method for analyzing human posture for sports equipment based on big data according to the present invention;

[0037] Figure 2 This is a schematic diagram of a specific process of step S1 of a method for analyzing human posture for sports equipment based on big data according to the present invention;

[0038] Figure 3 This is a schematic diagram of a specific process of step S2 of a method for analyzing human posture for sports equipment based on big data according to the present invention;

[0039] Figure 4 This is a schematic diagram of the framework of a human posture analysis system for sports equipment based on big data according to the present invention. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0041] Example 1

[0042] See also Figure 1-Figure 3 , an embodiment provided by the present invention: a human posture analysis method for sports equipment based on big data, which comprises the following specific steps:

[0043] S1. Obtaining usage data of sports equipment and simultaneously obtaining data information of human posture when exercising on the sports equipment;

[0044] S2, importing the acquired usage data of the sports equipment into a normal usage model of the sports equipment to perform sports equipment usage analysis;

[0045] S3, importing the acquired human posture data information of the exercise into the human posture analysis strategy to perform human posture analysis;

[0046] S4, obtaining sports equipment use analysis results and human posture analysis results and importing them into a motion matching analysis model to perform motion matching analysis;

[0047] S5, early warning of the exercise process of the exerciser according to the motion matching analysis result; in this embodiment, it should be noted that S1 includes the following specific steps:

[0048] S11, acquiring operation data of the sports equipment during operation through a sports equipment data acquisition sensor, wherein the operation data of the sports equipment during operation includes swing amplitude, swing frequency data and image data of the moving part of the sports equipment during operation, and is stored in a storage module, wherein the moving part of the sports equipment is, for example, a cycling trainer, and the moving part of the sports equipment is a pedal part of the cycling trainer;

[0049] The following is a simplified C language code example, which is used to obtain the operation data of the sports equipment during operation (including the swing amplitude, swing frequency data and image data of the moving part), and store these data in the storage module;

[0050]

[0051]

[0052]

[0053] In this code example, functions for obtaining sensor data and image data are defined, and the data is stored in the storage module through the `saveDataToStorage` function. Please make sure to adapt and improve it according to the actual sensor and camera connection method and the specific requirements for data acquisition and storage;

[0054] S12, collecting human posture images during the movement through the image acquisition component, and extracting standard movement posture image data, and storing them in the storage module. It should be noted that the standard movement posture image data here is the optimal posture image data of the human body for the movement output according to the shape of the human body; in this embodiment, it should be noted that the specific content of the normal use model of the sports equipment in S2 is as follows:

[0055] S21, obtaining the swing amplitude, swing frequency data and motion part image data of the motion part during the operation of the sports equipment during the training period;

[0056] S22, importing the swing amplitude, swing frequency data and image data of the moving part of the sports equipment during operation into the sports equipment use abnormal value calculation formula to calculate the sports equipment use abnormal value, wherein the sports equipment use abnormal value calculation formula is: Among them, T is the duration of the running process, M t is the average oscillation frequency of the moving part in time period t, is the maximum swing frequency of the moving part within the safe range in time period t, s t is the average swing amplitude of the moving part in time period t, is the swing amplitude safety value of the moving part, dt is the time integral, n is the number of pixels of the moving part image, x i is the pixel value of the i-th pixel of the motion image after operation, x im is the pixel value of the i-th pixel of the motion image before running, a q is the swing anomaly ratio coefficient, a p is the image abnormality ratio, where a q +a p =1; It should be noted that in this embodiment, the specific content of the human body posture analysis strategy in S3 is:

[0057] S31, acquiring a human body posture image during the motion process, and acquiring standard motion posture image data at the same time, merging the two images at the same scale, and acquiring the overlap area of ​​the overlapped parts of the two image sequences and the total overlapped area;

[0058] S32, importing the overlap area of ​​the overlapped parts of the two image sequences and the total overlapped area into the human body posture analysis abnormal value calculation formula to calculate the human body posture analysis abnormal value, wherein the human body posture analysis abnormal value calculation formula is: Where z is the number of images in the image sequence, S() is the area of ​​the image in brackets, and c j is the j-th human posture image during the movement, is the standard motion posture image corresponding to the j-th human body posture image during the motion process; in this embodiment, it should be noted that the motion matching analysis model in S4 includes the following specific contents:

[0059] Obtain the calculated sports equipment use abnormal value and human posture analysis abnormal value, add the calculated sports equipment use abnormal value and human posture analysis abnormal value to calculate the motion matching coefficient, compare the obtained motion matching coefficient with the set motion matching coefficient threshold, if the obtained motion matching coefficient is greater than or equal to the motion matching coefficient threshold, it means that the corresponding sports equipment does not match the corresponding exerciser, if the obtained motion matching coefficient is less than the motion matching coefficient threshold, it means that the corresponding sports equipment matches the corresponding exerciser; in this embodiment, it should be noted that S5 includes the following specific contents:

[0060] Obtain the matching results between the corresponding sports equipment and the corresponding exercisers, and provide a visual early warning of the exerciser's exercise process based on the matching results.

[0061] It should be noted here that the values ​​of the swing abnormality ratio coefficient, image abnormality ratio coefficient and motion matching coefficient threshold are determined as follows: 500 sets of safe exercise process data and 500 sets of dangerous exercise process data are obtained, the data are imported to calculate the motion matching coefficient, and the calculation results and classification results are imported into the fitting software to obtain the values ​​of the swing abnormality ratio coefficient, image abnormality ratio coefficient and motion matching coefficient threshold that meet the classification accuracy.

[0062] Through this embodiment, it is possible to achieve the following: obtaining usage data of sports equipment and simultaneously obtaining data information of human posture during exercise on the sports equipment, importing the obtained usage data of the sports equipment into a normal usage model of the sports equipment to perform sports equipment usage analysis, importing the obtained human posture data information during exercise into a human posture analysis strategy to perform human posture analysis, obtaining sports equipment usage analysis results and human posture analysis results and importing them into a motion matching analysis model to perform motion matching analysis, issuing early warnings of the exercise process of exercisers based on the motion matching analysis results, analyzing the degree of matching between exercisers and sports equipment, thereby improving the safety of exercisers during exercise and avoiding the occurrence of dangerous accidents during exercise.

[0063] Example 2

[0064] like Figure 4As shown, a human posture analysis system for sports equipment based on big data is implemented based on the above-mentioned human posture analysis method for sports equipment based on big data, and specifically includes: a data acquisition module, a sports equipment use analysis module, a human posture analysis module, a motion matching analysis module, an early warning module and a control module, the data acquisition module is used to acquire the use data of the sports equipment, and at the same time acquire the human posture data information of exercising on the sports equipment, and the sports equipment use analysis module is used to import the acquired use data of the sports equipment into the normal use model of the sports equipment to perform sports equipment use analysis;

[0065] In this embodiment, the human posture analysis module is used to import the acquired human posture data information of the exercise into the human posture analysis strategy to perform human posture analysis, the motion matching analysis module is used to obtain the sports equipment use analysis results and the human posture analysis results and import them into the motion matching analysis model to perform motion matching analysis, and the early warning module is used to give an early warning of the exercise process of the exerciser according to the motion matching analysis results;

[0066] In this embodiment, the control module is used to control the operation of the data acquisition module, the sports equipment use analysis module, the human posture analysis module, the motion matching analysis module and the early warning module.

[0067] Example 3

[0068] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0069] The processor executes the above-mentioned human posture analysis method for sports equipment based on big data by calling the computer program stored in the memory.

[0070] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement a human posture analysis method for sports equipment based on big data provided by the above method embodiment. The electronic device may also include other components for realizing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface, so as to input and output data. This embodiment will not be described in detail here.

[0071] Example 4

[0072] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0073] When the computer program runs on a computer device, the computer device executes the above-mentioned human posture analysis method for sports equipment based on big data.

[0074] For example, the computer readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0075] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0076] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0077] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD) or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0078] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0079] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0080] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only one way of division, and there may be other ways of division in actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0081] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0082] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0083] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0084] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A human posture analysis method for sports equipment based on big data, It is characterized in that It includes the following specific steps: S1. Obtaining usage data of sports equipment and simultaneously obtaining data information of human posture when exercising on the sports equipment; S2, importing the acquired usage data of the sports equipment into a normal usage model of the sports equipment to perform sports equipment usage analysis; The specific contents of the normal use model of sports equipment in S2 are as follows: S21, obtaining the swing amplitude, swing frequency data and motion part image data of the motion part during the operation of the sports equipment during the training period; S22, importing the swing amplitude, swing frequency data and image data of the moving part of the sports equipment during operation into the sports equipment use abnormal value calculation formula to calculate the sports equipment use abnormal value, wherein the sports equipment use abnormal value calculation formula is: Among them, T is the duration of the running process, M t is the average oscillation frequency of the moving part in time period t, is the maximum swing frequency of the moving part within the safe range in time period t, s t is the average swing amplitude of the moving part in time period t, is the swing amplitude safety value of the moving part, dt is the time integral, n is the number of pixels in the moving part image, xi is the pixel value of the i-th pixel in the moving part image after operation, x im is the pixel value of the i-th pixel of the motion image before running, a q is the swing anomaly ratio coefficient, a p is the image abnormality ratio, where a q +a p =1; S3, importing the acquired human posture data information of the exercise into the human posture analysis strategy to perform human posture analysis; S4, obtaining sports equipment use analysis results and human posture analysis results and importing them into a motion matching analysis model to perform motion matching analysis; S5. Provide early warning of the exercise process of the exerciser based on the results of the motion matching analysis.

2. A human body posture analysis method for sports equipment based on big data as claimed in claim 1, It is characterized in that The S1 comprises the following specific steps: S11, acquiring operation data of the sports equipment during operation through a sports equipment data acquisition sensor, wherein the operation data of the sports equipment during operation includes the swing amplitude, swing frequency data and image data of the moving part of the sports equipment during operation, and storing them in a storage module; S12. The human body posture images during the movement are collected by the image collection component, and standard movement posture image data are extracted and stored in the storage module.

3. A method for analyzing human posture for sports equipment based on big data as claimed in claim 2, It is characterized in that The specific content of the human posture analysis strategy in S3 is: S31, acquiring a human body posture image during the motion process, and acquiring standard motion posture image data at the same time, merging the two images at the same scale, and acquiring the overlap area of ​​the overlapped parts of the two image sequences and the total overlapped area; S32, importing the overlap area of ​​the overlapped parts of the two image sequences and the total overlapped area into the human body posture analysis abnormal value calculation formula to calculate the human body posture analysis abnormal value, wherein the human body posture analysis abnormal value calculation formula is: Where z is the number of images in the image sequence, S() is the area of ​​the image in brackets, cj is the jth human posture image during the motion process, is the standard motion posture image corresponding to the j-th human posture image during the motion process.

4. A method for analyzing human posture for sports equipment based on big data as claimed in claim 3, It is characterized in that The motion matching analysis model in S4 includes the following specific contents: Obtain the calculated sports equipment usage anomaly values ​​and human posture analysis anomaly values, add the calculated sports equipment usage anomaly values ​​and human posture analysis anomaly values ​​to calculate the motion matching coefficient, compare the obtained motion matching coefficient with the set motion matching coefficient threshold, if the obtained motion matching coefficient is greater than or equal to the motion matching coefficient threshold, it means that the corresponding sports equipment does not match the corresponding exerciser, if the obtained motion matching coefficient is less than the motion matching coefficient threshold, it means that the corresponding sports equipment matches the corresponding exerciser.

5. A method for analyzing human posture for sports equipment based on big data as claimed in claim 4, It is characterized in that The S5 includes the following specific contents: Obtain the matching results between the corresponding sports equipment and the corresponding exercisers, and provide a visual early warning of the exerciser's exercise process based on the matching results.

6. A human posture analysis system for sports equipment based on big data, which is implemented based on the human posture analysis method for sports equipment based on big data as claimed in any one of claims 1 to 5. It is characterized in that It specifically includes: a data acquisition module, a sports equipment usage analysis module, a human posture analysis module, a motion matching analysis module, an early warning module and a control module. The data acquisition module is used to obtain the usage data of the sports equipment and simultaneously obtain the human posture data information of exercising on the sports equipment. The sports equipment usage analysis module is used to import the acquired sports equipment usage data into the normal usage model of the sports equipment to perform sports equipment usage analysis.

7. A human posture analysis system for sports equipment based on big data as claimed in claim 6, It is characterized in that The human posture analysis module is used to import the acquired human posture data information of exercise into the human posture analysis strategy to perform human posture analysis, the motion matching analysis module is used to obtain the sports equipment use analysis results and the human posture analysis results and import them into the motion matching analysis model to perform motion matching analysis, and the early warning module is used to provide early warning of the exercise process of the exerciser according to the motion matching analysis results.

8. A human posture analysis system for sports equipment based on big data as claimed in claim 7, It is characterized in that The control module is used to control the operation of the data acquisition module, the sports equipment use analysis module, the human posture analysis module, the motion matching analysis module and the early warning module.

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