Intelligent exercise and physical training evaluation system and method

Abstract

The invention relates to the technical field of physical training evaluation, and discloses an intelligent exercise and physical training evaluation system and method, and the system comprises a hardware installation module which is responsible for the installation of hardware facilities of a table tennis training field; and the software use module provides a user interface and allows a user to select training items and parameter settings. According to the intelligent exercise and physical training evaluation system and method provided by the invention, through a video analysis unit and a sensor data processing unit in an action capture module, multi-aspect data of actions of athletes and interaction of rackets and balls are obtained from videos captured by a camera and a sensor respectively; the data from different sources are fused through a data fusion unit of the data analysis module, the performance of the athlete is comprehensively evaluated, compared with subjective judgment only depending on a coach, the actual situation of the athlete can be reflected more comprehensively and objectively, the evaluation accuracy is improved, and more accurate training guidance is provided for the athlete.

Description

Technical Field

[0001] The present invention relates to the technical field of physical training evaluation, and in particular to an intelligent sports and physical training evaluation system and method. Background Art

[0002] Table tennis, known as China's "national ball", is a ball sport played on a table. It is famous for its speed, flexibility and strong skills. Table tennis requires not only good physical coordination and reaction speed, but also precise control and tactical strategies. When conducting table tennis training, it is very important to accurately evaluate the athletes' movements, speed, strength and tactical execution. In the competitive arena of table tennis, athletes must not only have excellent physical coordination and be able to react quickly in an instant, but also have extremely precise control over the power and direction of the ball. At the same time, the clever use of tactical strategies is also a key factor in defeating the enemy. During the training process of table tennis players, it is of great significance to conduct accurate and comprehensive evaluation of their movement standardization, hitting speed, force and tactical execution effect. However, as far as the current existing technology is concerned, table tennis training evaluation work mostly relies on the personal subjective judgment of coaches and records relevant data manually. This traditional evaluation model obviously has many disadvantages. Its work efficiency is low, and it is difficult to conduct efficient evaluation of a large number of athletes within a limited time. Moreover, due to the limitations of subjective judgment, accuracy is difficult to be effectively guaranteed. The evaluation results often vary from person to person and lack unified objective standards.

[0003] To solve the above problems, this application proposes an intelligent sports and physical training evaluation system and method. Summary of the invention

[0004] Based on the technical problems existing in the background technology, the present invention proposes an intelligent sports and physical training evaluation system and method.

[0005] The present invention provides an intelligent sports and physical training evaluation system, comprising: Hardware installation module: responsible for the installation of hardware facilities in table tennis training venues; Software usage module: provides a user interface, allowing users to select training items and parameter settings; Face recognition module: used to identify and verify the identity of athletes and ensure the accuracy of data; Motion capture module: captures the athlete's movements through cameras and sensors; Data analysis module: Analyze the captured motion data and evaluate the athlete’s performance; Real-time feedback module: Provide real-time feedback to athletes based on analysis results; Result statistics module: after the training is completed, the training results are counted and displayed; Standard Demonstration Module: Provides standard action demonstration videos for athletes to learn and refer to; The hardware installation module is communicatively connected with the motion capture module, the software use module is connected with the face recognition module through the software interface, the motion capture module is connected with the data analysis module through the data transmission interface, the data analysis module is connected with the real-time feedback module through the internal communication protocol, the real-time feedback module is synchronized with the result statistics module through the database, and the result statistics module is integrated with the standard demonstration module through the user interface.

[0006] As a further optimization solution of the present invention, the hardware installation module includes: Sensor installation unit: responsible for installing and configuring various sensors, including motion capture sensors and pressure sensors, to capture the athlete's motion data and the interaction data between the racket and the ball; Camera deployment unit: responsible for deploying high-definition cameras at the training grounds to capture video of athletes’ movements, as well as the trajectory and speed of the ball; Network connection unit: Ensure that all hardware devices are connected to the central processing system through the network, including wireless and wired connections, to achieve real-time data transmission.

[0007] As a further optimization solution of the present invention, the software usage module includes: User Interface Design Unit: Develop an intuitive user interface that allows users to easily select training items and parameter settings, including racket holding hand and exercise duration; Project selection unit: provides a project library from which users can select specific training action projects, including forehand and backhand shots; Parameter configuration unit: allows users to configure training parameters according to personal needs, including training difficulty, number of repetitions, and save user configurations for subsequent use.

[0008] As a further optimization solution of the present invention, the face recognition module includes: Face detection unit: uses the face image captured by the camera to identify the facial features of the athlete through the face detection algorithm; Identity verification unit: compares the detected face with the athlete’s facial data stored in the database to verify the athlete’s identity; Data Association Unit: Associates the identified athletes with their training data to ensure data accuracy and personalization.

[0009] As a further optimization solution of the present invention, the motion capture module includes: Video analysis unit: performs frame analysis on the video captured by the camera to extract the athlete's movement features; Sensor data processing unit: processes data from sensors, including racket acceleration and ball speed, to supplement the results of video analysis; Action Synchronization Unit: Synchronizes the results of video analysis and sensor data processing to obtain a complete view of the player's movements.

[0010] As a further optimization solution of the present invention, the data analysis module includes: Action recognition unit: uses machine learning algorithms to classify and recognize athletes’ actions; Performance evaluation unit: evaluates the athlete's performance, including hitting accuracy and reaction speed, based on the action recognition results; Data Fusion Unit: Fusion of data from different sources to obtain a more comprehensive analysis of athlete performance.

[0011] As a further optimization solution of the present invention, the real-time feedback module includes: Feedback generation unit: Generates real-time feedback information, including action correction suggestions, based on data analysis results; User Interaction Unit: Provides an interactive interface that enables athletes to receive and understand real-time feedback; Feedback Optimization Unit: Based on athlete feedback and performance improvements, feedback content is adjusted to improve training results.

[0012] As a further optimization solution of the present invention, the result statistics module includes: Data aggregation unit: collects all data during the training process, including action data and scores; Result display unit: displays the summarized data to users in the form of charts and reports; Historical Comparison Unit: Provides comparative analysis of athletes’ historical training data to observe progress and trends.

[0013] As a further optimization solution of the present invention, the standard demonstration module includes: Video library management unit: manage the standard demonstration video library to ensure the update and accuracy of video content; Video playback unit: provides an interface for athletes to watch and learn standard demonstration videos; Personalized Recommendation Unit: Based on the athlete’s training performance, relevant standard demonstration videos are recommended to help them improve their techniques.

[0014] An intelligent sports and physical training evaluation method, using the above-mentioned intelligent sports and physical training evaluation system, is characterized by comprising the following steps: Step 1: First, the hardware installation module is responsible for installing the necessary hardware facilities, including sensors and cameras, at the table tennis training venue to ensure that the athletes' movement data can be captured; Step 2: Next, the software usage module provides a user-friendly interface that allows users to select specific training items and set parameters to personalize the training experience; Step 3: Then, the facial recognition module is activated to ensure the accuracy and personalization of the training data by identifying and verifying the identity of the athlete; Step 4: Next, the motion capture module captures the athlete’s movements in real time through deployed cameras and sensors, providing raw data for subsequent analysis; Step 5: Then, the data analysis module conducts in-depth analysis of the captured motion data to evaluate the athlete’s technical performance and physical condition; Step 6: Next, the real-time feedback module provides athletes with immediate feedback and suggestions based on the data analysis results, helping them to make immediate adjustments and improvements; Step 7: After the training, the result statistics module is responsible for summarizing and displaying the training results, including action scores and violation statistics, to provide comprehensive feedback to athletes; Step 8: Finally, the standard demonstration module provides standard action demonstration videos for athletes to learn and refer to.

[0015] The above technical solution of the present invention has the following beneficial technical effects: 1. Through the video analysis unit and sensor data processing unit in the motion capture module, the athlete's movements and the interaction between the racket and the ball are obtained from the video captured by the camera and the sensor respectively. Then, the data fusion unit of the data analysis module fuses the data from different sources to comprehensively evaluate the athlete's performance. Compared with relying solely on the subjective judgment of the coach, it can more comprehensively and objectively reflect the actual situation of the athlete, improve the accuracy of the evaluation, and provide more accurate training guidance for the athletes; 2. The real-time feedback module can quickly generate real-time feedback including action correction suggestions based on the data analysis results, and promptly convey it to the athletes through the user interaction unit. The athletes can immediately adjust their actions and tactics accordingly to avoid continuous wrong actions, significantly improve training efficiency, and speed up the training process; 3. The software usage module allows users to independently select training items, set parameters, and save configurations for subsequent use. At the same time, the standard demonstration module can recommend relevant standard demonstration videos based on the athlete's training performance to meet the personalized needs of different athletes, allowing them to focus on training their weak links and avoid wasting time on inapplicable content, thereby improving overall training efficiency; 4. With the help of motion capture module and data analysis module, various indicators of athletes' movements can be quantitatively analyzed, changing the previous situation where subjective judgment is difficult to quantify, providing athletes with specific numerical evaluation results, and clearly understanding their own level and room for improvement; 5. The historical comparison unit in the result statistics module can collect and compare the training data of athletes in different periods and display them in the form of charts and reports. Athletes and coaches can intuitively observe their progress trends, summarize experiences, and adjust training plans in time to ensure that the training direction is correct and effective, which helps athletes to improve their competitive level in a long-term and stable manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a module block diagram of an intelligent sports and physical training evaluation system proposed by the present invention.

[0017] Figure 2 The present invention provides a flow chart of an intelligent sports and physical training evaluation method. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.

[0019] like Figure 1 and Figure 2 As shown, the present invention proposes an intelligent sports and physical training evaluation system, comprising: Hardware installation module: responsible for the installation of hardware facilities in table tennis training venues; Software usage module: provides a user interface, allowing users to select training items and parameter settings; Face recognition module: used to identify and verify the identity of athletes and ensure the accuracy of data; Motion capture module: captures the athlete's movements through cameras and sensors; Data analysis module: Analyze the captured motion data and evaluate the athlete’s performance; Real-time feedback module: Provide real-time feedback to athletes based on analysis results; Result statistics module: after the training is completed, the training results are counted and displayed; Standard Demonstration Module: Provides standard action demonstration videos for athletes to learn and refer to; The hardware installation module is communicatively connected with the motion capture module, the software use module is connected with the face recognition module through the software interface, the motion capture module is connected with the data analysis module through the data transmission interface, the data analysis module is connected with the real-time feedback module through the internal communication protocol, the real-time feedback module is synchronized with the result statistics module through the database, and the result statistics module is integrated with the standard demonstration module through the user interface.

[0020] In this embodiment, the hardware installation module includes: Sensor installation unit: responsible for installing and configuring various sensors, including motion capture sensors and pressure sensors, to capture the athlete's motion data and the interaction data between the racket and the ball; Camera deployment unit: responsible for deploying high-definition cameras at the training grounds to capture video of athletes’ movements, as well as the trajectory and speed of the ball; Network connection unit: Ensure that all hardware devices are connected to the central processing system through the network, including wireless and wired connections, to achieve real-time data transmission.

[0021] It should be noted that the motion capture sensor can accurately record the movement trajectory, speed and acceleration information of various parts of the athlete's body, including the athlete's body rotation angle and arm swing speed when hitting the ball. The pressure sensor is installed on the table surface and the racket grip to obtain the pressure change at the moment of contact between the racket and the ball and the strength data of the athlete's grip. These sensors are connected to the central processing system through wired or wireless means to ensure that the data can be transmitted stably and quickly; The installation height and angle of the cameras have been carefully adjusted to obtain the best shooting field of view, including installing cameras directly above the table, on both sides and behind the players, which can not only clearly capture the players' hitting movements and body postures, but also accurately record the flight trajectory and speed of the ball. The cameras transmit the captured video data to the central processing system in real time through a network connection; For wired connections, use high-speed Ethernet cables to connect sensors and cameras to switches, and then connect them to the central processing system through switches. For wireless connections, select high-performance wireless access points to ensure sufficient bandwidth and signal strength so that devices can transmit data stably. At the same time, set up network security mechanisms to prevent data leakage and interference.

[0022] In this embodiment, the software usage module includes: User Interface Design Unit: Develop an intuitive user interface that allows users to easily select training items and parameter settings, including racket holding hand and exercise duration; Project selection unit: provides a project library from which users can select specific training action projects, including forehand and backhand shots; Parameter configuration unit: allows users to configure training parameters according to personal needs, including training difficulty, number of repetitions, and save user configurations for subsequent use.

[0023] It should be noted that the interface adopts a graphical design, with large icons and clear text to identify training items and parameter setting options, including icons of different colors and shapes to represent forehand, backhand and serve training items. Users only need to click on the corresponding icon to select. In terms of parameter setting, the drop-down menu and slider control allow users to easily select the racket hand, set the exercise duration, training difficulty and number of repetitions. After the user completes the setting, the interface will display the set parameter summary in real time for the user to confirm; Each training action item in the project library is equipped with detailed text descriptions and short animation demonstrations to help users better understand the action requirements. For example, in the forehand shot project, in addition to the basic action description, it also shows the changes in forehand shots under different hitting points, hitting forces and rotation conditions. Users can choose specific forehand shot training modes according to their needs, such as forehand fast attack and forehand loop.

[0024] In this embodiment, the face recognition module includes: Face detection unit: uses the face image captured by the camera to identify the facial features of the athlete through the face detection algorithm; Identity verification unit: compares the detected face with the athlete’s facial data stored in the database to verify the athlete’s identity; Data Association Unit: Associates the identified athletes with their training data to ensure data accuracy and personalization.

[0025] It should be noted that the face detection algorithm uses an advanced deep learning model that can quickly and accurately locate and identify the facial features of athletes in complex environments, including the position and contours of key parts such as eyes, nose, and mouth. Even if the athlete's face is blocked or the angle changes during exercise, the algorithm can still stably detect the face; The facial data in the database is collected in advance by a high-definition camera and obtained through strict preprocessing. The comparison process uses a high-precision feature matching algorithm to calculate the similarity between the two. If the similarity exceeds the set threshold, the identity verification is considered to be successful. Otherwise, the athlete is prompted to re-authenticate or contact the administrator for identity verification; During the entire training process, whether it is motion capture data, data analysis results or feedback information, they are all closely tied to the athlete's identity to ensure the accuracy and personalization of the data, making it convenient for subsequent comprehensive tracking and analysis of the athlete's training situation.

[0026] In this embodiment, the motion capture module includes: Video analysis unit: performs frame analysis on the video captured by the camera to extract the athlete's movement features; Sensor data processing unit: processes data from sensors, including racket acceleration and ball speed, to supplement the results of video analysis; Action Synchronization Unit: Synchronizes the results of video analysis and sensor data processing to obtain a complete view of the player's movements.

[0027] It should be noted that the video analysis unit analyzes the video data transmitted by the camera in real time frame by frame; first, the video is preprocessed, including image denoising, grayscale, and edge enhancement operations to improve image quality, and then the human posture estimation algorithm is used to identify the key joints of the athlete's body, such as the head, shoulders, elbows, wrists, hips, knees, and ankles. By tracking the movement trajectory of these joints, the athlete's movement characteristics are extracted, such as the body's center of gravity transfer, joint angle changes, and movement rhythm; The sensor data processing unit receives and processes the data from the sensor in real time. It filters and calibrates the data from the motion capture sensor to remove noise interference and improve the accuracy of the data. It fuses the processed sensor data with the action features obtained from the video analysis, including combining the racket acceleration data with the motion trajectory of the racket in the video to more accurately analyze the action details at the moment of hitting the ball. For the pressure sensor data, it analyzes the pressure change curve when the racket contacts the ball to determine the force of the hit and the location of the hit point.

[0028] The action synchronization unit synchronizes the results obtained by the video analysis unit and the sensor data processing unit through timestamp and data matching algorithm to ensure that at the same moment, the athlete's action in the video corresponds to the data recorded by the sensor, thereby obtaining a complete view of the athlete's action, including when the video shows the moment the athlete hits the ball, the acceleration of the racket, the speed of the ball and the posture data of various parts of the athlete's body are synchronously displayed at this time.

[0029] In this embodiment, the data analysis module includes: Action recognition unit: uses machine learning algorithms to classify and recognize athletes’ actions; Performance evaluation unit: evaluates the athlete's performance, including hitting accuracy and reaction speed, based on the action recognition results; Data Fusion Unit: Fusion of data from different sources to obtain a more comprehensive analysis of athlete performance.

[0030] It should be noted that the action recognition unit uses an action recognition algorithm based on deep learning to classify and recognize the athlete's actions. When training the model, a large amount of labeled table tennis action data is used, including samples of forehand shots, backhand shots, serves, and receiving shots of different athletes. The model can accurately identify the type of action currently performed by the athlete and judge the degree of standardization of the action. For the forehand shot, the recognition model can determine whether the athlete's arm swing trajectory conforms to the standard action pattern and whether the hitting point is accurate. The performance evaluation unit evaluates the athlete's performance in multiple dimensions based on the action recognition results and the detailed data provided by the motion capture module. The evaluation indicators include hitting accuracy (such as the deviation between the hitting point and the target area), reaction speed (the time from the ball flying to the athlete's reaction), action stability (action consistency when repeating actions multiple times), and power control (the size and stability of the hitting power). Through the comprehensive analysis of these indicators, a comprehensive performance evaluation report is provided to the athlete. The data fusion unit fuses data from different sources such as video analysis, sensor data processing, and motion recognition, including the athlete's body posture data, racket movement data, and hitting point location data to build a complete athlete motion model. Through data fusion, we can have a more comprehensive understanding of the athlete's technical characteristics and physical condition, discover potential problems and advantages, and provide a more accurate basis for personalized training.

[0031] In this embodiment, the real-time feedback module includes: Feedback generation unit: Generates real-time feedback information, including action correction suggestions, based on data analysis results; User Interaction Unit: Provides an interactive interface that enables athletes to receive and understand real-time feedback; Feedback Optimization Unit: Based on athlete feedback and performance improvements, feedback content is adjusted to improve training results.

[0032] It should be noted that the feedback generation unit generates targeted feedback information in real time based on the evaluation results of the data analysis module. The feedback information includes action correction suggestions, technical improvement directions, and physical training tips. For example, if the athlete's arm is not fully extended during the forehand stroke, the feedback generation unit will prompt "the arm should be stretched as straight as possible during the forehand stroke to increase the power and control range of the stroke." At the same time, it will also give affirmation and encouragement to excellent actions to enhance the confidence of athletes. The user interaction unit sets up a display terminal at the training venue, such as a large-screen display or smart glasses device, to display real-time feedback information to athletes. The display terminal presents the feedback content in a concise and clear manner, such as text prompts, animation demonstrations or voice broadcasts. Athletes can view the feedback information during training breaks or rests, and adjust their movements in time according to the suggestions. In addition, the interaction unit also provides some interactive functions, such as athletes can mark the feedback information or feedback their own questions, so as to further improve the feedback strategy later; Feedback Optimization Unit: Based on the athlete's response to feedback information and the improvement of actual performance, the feedback content and method are continuously adjusted. If the athlete fails to understand or implement a certain feedback suggestion for many times, the Feedback Optimization Unit will try to change the expression or provide a more detailed explanation. At the same time, as the athlete's technical level improves, the Feedback Optimization Unit will gradually improve the accuracy and depth of the feedback, guiding the athlete to develop to a higher level.

[0033] In this embodiment, the result statistics module includes: Data aggregation unit: collects all data during the training process, including action data and scores; Result display unit: displays the summarized data to users in the form of charts and reports; Historical Comparison Unit: Provides comparative analysis of athletes’ historical training data to observe progress and trends.

[0034] It should be noted that: after the training, the data aggregation unit collects all the data during the training process, including motion capture data, performance evaluation data, and feedback records, organizes and classifies these data, establishes the athlete's training data file, and stores the hitting accuracy and reaction speed data of each training in chronological order to facilitate subsequent query and analysis; Result display unit: displays the aggregated data to users in the form of intuitive charts and detailed reports. The charts include line charts, bar charts, and pie charts, which are used to show the changing trends of athletes' performance indicators over time and the comparison between different training items. The reports contain basic training information (such as training time and training items), detailed analysis of various performance indicators, existing problems and improvement suggestions. Users can easily view and download these result display files through the user interface; Historical comparison unit: retrieve the athlete's historical training data from the database and compare and analyze it with the current training results. By comparing the hitting accuracy and reaction speed indicators in different periods, observe the athlete's improvement trend. If it is found that the athlete's forehand hitting accuracy has improved significantly in recent times, but the reaction speed has slightly decreased, the training plan can be adjusted in a targeted manner to strengthen the reaction speed training. At the same time, the historical comparison unit can also analyze the changes in the athlete's technical characteristics at different training stages, and provide a reference for formulating long-term training plans.

[0035] In this embodiment, the standard demonstration module includes: Video library management unit: manage the standard demonstration video library to ensure the update and accuracy of video content; Video playback unit: provides an interface for athletes to watch and learn standard demonstration videos; Personalized Recommendation Unit: Based on the athlete’s training performance, relevant standard demonstration videos are recommended to help them improve their techniques.

[0036] It should be noted that: the video library management unit establishes a video library containing a large number of standard table tennis action demonstration videos. The videos in the video library are recorded by professional table tennis coaches or athletes, covering all aspects from basic actions to advanced skills. The video library is regularly updated and maintained to ensure the accuracy and timeliness of the video content. According to the development trend of table tennis technology and the latest competition rules, the demonstration videos of serving and receiving actions are updated in a timely manner; Video playback unit: In the rest area of ​​the training venue or through mobile devices, it provides athletes with an interface for conveniently watching standard demonstration videos. Athletes can choose to watch different demonstration videos according to their needs. The video playback interface supports pause, fast forward, and rewind operations, which is convenient for athletes to carefully observe the details of the movements. At the same time, text annotations and action decomposition diagrams can be added during video playback to help athletes understand the key points of the movements; Personalized recommendation unit: Based on the athlete's performance data during training, such as action recognition results and performance evaluation data, the personalized recommendation unit analyzes the athlete's technical weaknesses and areas for improvement. Then, relevant standard demonstration videos are selected from the video library and recommended to the athlete. If the athlete has poor rotation control when hitting the backhand ball, the personalized recommendation unit will recommend some demonstration videos on backhand spin ball techniques to help the athlete improve his technique in a targeted manner.

[0037] An intelligent sports and physical training evaluation method, using the above-mentioned intelligent sports and physical training evaluation system, is characterized by comprising the following steps: Step 1: First, the hardware installation module is responsible for installing the necessary hardware facilities, including sensors and cameras, at the table tennis training venue to ensure that the athletes' movement data can be captured; Step 2: Next, the software usage module provides a user-friendly interface that allows users to select specific training items and set parameters to personalize the training experience; Step 3: Then, the facial recognition module is activated to ensure the accuracy and personalization of the training data by identifying and verifying the identity of the athlete; Step 4: Next, the motion capture module captures the athlete’s movements in real time through deployed cameras and sensors, providing raw data for subsequent analysis; Step 5: Then, the data analysis module conducts in-depth analysis of the captured motion data to evaluate the athlete’s technical performance and physical condition; Step 6: Next, the real-time feedback module provides athletes with immediate feedback and suggestions based on the data analysis results, helping them to make immediate adjustments and improvements; Step 7: After the training, the result statistics module is responsible for summarizing and displaying the training results, including action scores and violation statistics, to provide comprehensive feedback to athletes; Step 8: Finally, the standard demonstration module provides standard action demonstration videos for athletes to learn and refer to.

[0038] It should be understood that the above specific embodiments of the present invention are only used to illustrate or explain the principles of the present invention, and do not constitute a limitation of the present invention. Therefore, any modifications, equivalent substitutions, improvements, etc. made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. In addition, the appended claims of the present invention are intended to cover all changes and modifications that fall within the scope and boundaries of the appended claims, or the equivalent forms of such scope and boundaries.

Claims

1. An intelligent sports and physical training evaluation system, characterized in that: include: Hardware installation module: responsible for the installation of hardware facilities in table tennis training venues; Software usage module: provides a user interface, allowing users to select training items and parameter settings; Face recognition module: used to identify and verify the identity of athletes and ensure the accuracy of data; Motion capture module: captures the athlete's movements through cameras and sensors; Data analysis module: Analyze the captured motion data and evaluate the athlete’s performance; Real-time feedback module: Provide real-time feedback to athletes based on analysis results; Result statistics module: after the training is completed, the training results are counted and displayed; Standard Demonstration Module: Provides standard action demonstration videos for athletes to learn and refer to; The hardware installation module is communicatively connected with the motion capture module, the software use module is connected with the face recognition module through the software interface, the motion capture module is connected with the data analysis module through the data transmission interface, the data analysis module is connected with the real-time feedback module through the internal communication protocol, the real-time feedback module is synchronized with the result statistics module through the database, and the result statistics module is integrated with the standard demonstration module through the user interface.

2. The intelligent sports and physical training evaluation system according to claim 1, characterized in that: The hardware installation module includes: Sensor installation unit: responsible for installing and configuring various sensors, including motion capture sensors and pressure sensors, to capture the athlete's motion data and the interaction data between the racket and the ball; Camera deployment unit: responsible for deploying high-definition cameras at the training grounds to capture video of athletes’ movements, as well as the trajectory and speed of the ball; Network connection unit: Ensure that all hardware devices are connected to the central processing system through the network, including wireless and wired connections, to achieve real-time data transmission.

3. The intelligent sports and physical training evaluation system according to claim 2, characterized in that: The software usage modules include: User Interface Design Unit: Develop an intuitive user interface that allows users to easily select training items and parameter settings, including racket holding hand and exercise duration; Project selection unit: provides a project library from which users can select specific training action projects, including forehand and backhand shots; Parameter configuration unit: allows users to configure training parameters according to personal needs, including training difficulty, number of repetitions, and save user configurations for subsequent use.

4. The intelligent sports and physical training evaluation system according to claim 3, characterized in that: The face recognition module comprises: Face detection unit: uses the face image captured by the camera to identify the facial features of the athlete through the face detection algorithm; Identity verification unit: compares the detected face with the athlete’s facial data stored in the database to verify the athlete’s identity; Data Association Unit: Associates the identified athletes with their training data to ensure data accuracy and personalization.

5. The intelligent sports and physical training evaluation system according to claim 4, characterized in that: The motion capture module comprises: Video analysis unit: performs frame analysis on the video captured by the camera to extract the athlete's movement features; Sensor data processing unit: processes data from sensors, including racket acceleration and ball speed, to supplement the results of video analysis; Action Synchronization Unit: Synchronizes the results of video analysis and sensor data processing to obtain a complete view of the player's movements.

6. The intelligent sports and physical training evaluation system according to claim 5, characterized in that: The data analysis module includes: Action recognition unit: uses machine learning algorithms to classify and recognize athletes’ actions; Performance evaluation unit: evaluates the athlete's performance, including hitting accuracy and reaction speed, based on the action recognition results; Data Fusion Unit: Fusion of data from different sources to obtain a more comprehensive analysis of athlete performance.

7. The intelligent sports and physical training evaluation system according to claim 6, characterized in that: The real-time feedback module comprises: Feedback generation unit: Generates real-time feedback information, including action correction suggestions, based on data analysis results; User Interaction Unit: Provides an interactive interface that enables athletes to receive and understand real-time feedback; Feedback Optimization Unit: Based on athlete feedback and performance improvements, feedback content is adjusted to improve training results.

8. The intelligent sports and physical training evaluation system according to claim 7, characterized in that: The result statistics module includes: Data aggregation unit: collects all data during the training process, including action data and scores; Result display unit: displays the summarized data to users in the form of charts and reports; Historical Comparison Unit: Provides comparative analysis of athletes’ historical training data to observe progress and trends.

9. The intelligent sports and physical training evaluation system according to claim 8, characterized in that: The standard demonstration modules include: Video library management unit: manage the standard demonstration video library to ensure the update and accuracy of video content; Video playback unit: provides an interface for athletes to watch and learn standard demonstration videos; Personalized Recommendation Unit: Based on the athlete’s training performance, relevant standard demonstration videos are recommended to help them improve their techniques.

10. An intelligent sports and physical training evaluation method, using an intelligent sports and physical training evaluation system as claimed in any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: First, the hardware installation module is responsible for installing the necessary hardware facilities, including sensors and cameras, at the table tennis training venue to ensure that the athletes' movement data can be captured; Step 2: Next, the software usage module provides a user-friendly interface that allows users to select specific training programs and set parameters to personalize the training experience; Step 3: Then, the facial recognition module is activated to ensure the accuracy and personalization of the training data by identifying and verifying the identity of the athlete; Step 4: Next, the motion capture module uses the deployed cameras and sensors to capture the athlete’s movements in real time, providing raw data for subsequent analysis; Step 5: Then, the data analysis module conducts in-depth analysis of the captured motion data to evaluate the athlete’s technical performance and physical condition; Step 6: Next, the real-time feedback module provides athletes with immediate feedback and suggestions based on the data analysis results, helping them to make immediate adjustments and improvements; Step 7: After the training, the result statistics module is responsible for summarizing and displaying the training results, including action scores and violation statistics, to provide comprehensive feedback to athletes; Step 8: Finally, the standard demonstration module provides standard action demonstration videos for athletes to learn and refer to.

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