Athlete performance real-time monitoring and optimizing system and using method thereof

Abstract

The invention discloses an athlete performance real-time monitoring and optimizing system and a using method thereof, and belongs to the field of digital processing of physical exercise data. The system mainly comprises a control system, and further comprises a multi-dimensional biosensor module and a data acquisition and transmission module, and the multi-dimensional biosensor module transmits acquired sensor data to the control system through the data acquisition and transmission module. The control system comprises a real-time data analysis module, a machine learning and mode recognition module and a personalized suggestion generation module; the real-time data analysis module is connected to the machine learning and mode recognition module, and the machine learning and mode recognition module is connected with the personalized suggestion generation module. According to the system, the physiological and psychological states of the athletes can be monitored in real time, scientific and personalized training and recovery suggestions can be provided, and meanwhile, the safety and privacy of data and the compatibility with existing training equipment are ensured.

Description

Technical Field

[0001] The present invention relates to the field of digital processing of sports data, and in particular to a real-time monitoring and optimization system for athlete performance and a method for using the system. Background Art

[0002] With the development of science and technology, some advanced biosensors and data analysis technologies have begun to be applied to athlete training monitoring. These technologies can provide richer data, such as muscle activity, skin electrical response, etc., providing more dimensions for athlete performance analysis. However, these technologies still have shortcomings in real-time data processing, personalized recommendation generation, and user interaction experience.

[0003] In the current field of sports training, athlete performance monitoring and optimization is a key link, which directly affects the training effect and competition results of athletes. However, traditional monitoring methods often rely on the subjective judgment of coaches and the self-reporting of athletes, which are not only inefficient but also lack accuracy and objectivity. In addition, in the prior art, an athlete data monitoring method, system and device disclosed in the application with publication number CN113457107A, the monitoring equipment of which can only provide limited physiological index data, such as heart rate and blood pressure, but ignores the evaluation of the athlete's psychological state, which is crucial for a comprehensive understanding of the athlete's overall performance. Summary of the invention

[0004] The purpose of the present invention is to provide a real-time monitoring and optimization system for athlete performance and a method of using the same, which can not only monitor the athlete's physiological and psychological state in real time, but also provide scientific and personalized training and recovery suggestions while ensuring the security and privacy of the data, as well as compatibility with existing training equipment.

[0005] The present invention is achieved through the following technical solutions:

[0006] A real-time monitoring and optimization system for athlete performance includes a control system, a multi-dimensional biosensor module and a data acquisition and transmission module. The multi-dimensional biosensor module captures the athlete's physiological and psychological data in real time, and the multi-dimensional biosensor module transmits the acquired sensor data to the control system through the data acquisition and transmission module. The control system includes a real-time data analysis module, a machine learning and pattern recognition module and a personalized suggestion generation module; the real-time data analysis module is connected to the machine learning and pattern recognition module, the machine learning and pattern recognition module identifies the athlete's performance pattern and optimization potential, the machine learning and pattern recognition module is connected to the personalized suggestion generation module, and the personalized suggestion generation module generates customized training suggestions and recovery plans for feedback.

[0007] Furthermore, the multi-dimensional biosensor module comprises:

[0008] Heart rate monitors, which continuously track an athlete’s heart rate and rhythm;

[0009] Blood pressure monitor, used to measure and record the athlete's blood pressure changes in real time to assess the status of the cardiovascular system;

[0010] Muscle activity sensors, which are used to capture electrical signals of muscle contraction and relaxation, reflecting muscle activity intensity and fatigue level;

[0011] Galvanic skin response sensors are used to detect changes in skin conductivity caused by emotional changes, providing data for evaluating the psychological state of athletes;

[0012] Facial expression recognition sensors that analyze the movement of facial muscles to identify an athlete’s emotional state;

[0013] Heart rate variability analysis sensor to measure heart rate changes to assess athletes' autonomic nervous system activity and stress levels.

[0014] Furthermore, the data acquisition and transmission module adopts wireless communication technology, and the wireless communication technology includes Wi-Fi 6 technology and Bluetooth 5.0 technology.

[0015] Furthermore, the real-time data analysis module is used for data processing, and the processing method thereof comprises the following steps:

[0016] S1. Preprocess the sensor data and preliminarily clean the sensor data to remove noise and outliers;

[0017] S2, convert sensor data into a standard format through a standardized algorithm;

[0018] S3, feature value extraction, identifying key frequency components and trends in the data through fast Fourier transform technology.

[0019] Furthermore, the machine learning and pattern recognition module uses a convolutional neural network to perform feature learning on sensor data and extract key features; the machine learning and pattern recognition module uses a recurrent neural network to analyze sensor data and capture dynamic trends and periodic patterns of athlete performance; the machine learning and pattern recognition module uses a clustering algorithm to perform cluster analysis on the athlete's sensor data and discover data distribution characteristics under different training states.

[0020] Furthermore, the machine learning and pattern recognition module is integrated with a support vector machine classifier to classify the psychological state of the athletes; the machine learning and pattern recognition module evaluates the performance trend and psychological state of the athletes.

[0021] Furthermore, the personalized suggestion generation module receives the processed sensor data, athlete performance trends and psychological state assessment, combines the athlete's personal data, performs optimization calculations through an algorithm model, and generates training suggestions or recovery plans.

[0022] Furthermore, it also includes a user interaction interface module, which includes a coach view and an athlete view. The user interaction interface module feeds back the customized training and recovery plan to the coach and the athlete.

[0023] A method for using a real-time monitoring and optimization system for athlete performance comprises the following steps:

[0024] S1. Install and calibrate the multi-dimensional biosensor module: Install the sensor on the athlete or his sports equipment and ensure the measurement accuracy through the calibration process;

[0025] S2, start the data acquisition and transmission module: activate the wireless communication technology and set the transmission parameters;

[0026] S3. Use the real-time data analysis module to perform data preprocessing and feature extraction: set the data cleaning threshold;

[0027] S4, Applied Machine Learning and Pattern Recognition Module: Configure deep learning network structure;

[0028] S5. Generate training suggestions or recovery plans through the personalized suggestion generation module: input athlete personal information and historical performance data, and set generator algorithm parameters.

[0029] S6. Display data, suggestions and receive feedback in the user interface module: configure the interface display parameters and analyze athlete performance patterns.

[0030] S7. Adjust and optimize system performance based on feedback from athletes and coaches: collect feedback information and evaluate system performance indicators.

[0031] Furthermore, it also includes regularly updating machine learning models to adapt to changes in athlete performance.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. The present invention uses a high-precision multi-dimensional biosensor module to capture athletes' physiological indicators and psychological states in real time, uses a heart rate monitor to accurately record athletes' heart rate changes, and uses a skin galvanic response sensor to capture athletes' emotional fluctuations. The real-time collection and analysis of these precise data makes training suggestions more tailored to athletes' immediate states, thereby significantly improving the accuracy and effectiveness of training.

[0034] 2. The present invention has built-in machine learning and pattern recognition modules, and adopts various learning network structures, which can dynamically adjust training suggestions based on the athletes' real-time performance and historical training data.

[0035] 3. The present invention comprehensively analyzes the real-time physiological data and historical training records of athletes through a personalized suggestion generation module to dynamically generate personalized training suggestions; the personalized suggestion generation module receives the data processed by the real-time data analysis module, including key physiological indicators such as heart rate, blood pressure, muscle activity, and data obtained from the machine learning module; the personalized suggestion generation module uses these data, combined with personal information such as the athlete's age, weight, exercise habits and historical performance, and performs optimization calculations through a built-in algorithm model, such as a genetic algorithm or a simulated annealing algorithm.

[0036] 4. The user interaction interface module of the present invention is provided with a coach view and an athlete view to realize customized information display and control options; the interactive feedback mechanism allows the coach and the athlete to jointly participate in the adjustment of the training plan and optimize the training effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a system block diagram of the present invention;

[0038] Figure 2 is a flow chart of the method of using the present invention; DETAILED DESCRIPTION

[0039] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is clearly and completely described below in conjunction with the accompanying drawings of the present invention. Based on the embodiments in this application, other similar embodiments obtained by ordinary technicians in this field without making creative work should all fall within the scope of protection of this application. In addition, the directional words mentioned in the following embodiments, such as "up", "down", "left", "right", etc., are only reference to the directions of the accompanying drawings. Therefore, the directional words used are used to illustrate rather than limit the invention.

[0040] The present invention will be further described below in conjunction with the accompanying drawings.

[0041] Embodiment 1: A system for real-time monitoring and optimization of athlete performance, such as Figure 1As shown, it includes a control system, a multi-dimensional biosensor module and a data acquisition and transmission module. The multi-dimensional biosensor module captures the athlete's physiological and psychological data in real time, such as heart rate, blood pressure, muscle activity and emotional state. The multi-dimensional biosensor module transmits the acquired sensor data to the control system through the data acquisition and transmission module. The control system includes a real-time data analysis module, a machine learning and pattern recognition module and a personalized suggestion generation module; the real-time data analysis module is connected to the machine learning and pattern recognition module, and the real-time data analysis module quickly cleans, standardizes and extracts features of the received data to ensure data quality and lay a solid foundation for subsequent analysis. The machine learning and pattern recognition module identifies the athlete's performance mode and optimization potential. The machine learning and pattern recognition module is connected to the personalized suggestion generation module. The analysis results of the machine learning and pattern recognition module are transmitted to the personalized suggestion generation module, and the personalized suggestion generation module generates customized training suggestions and recovery plans for feedback.

[0042] Embodiment 2: A system for real-time monitoring and optimization of athlete performance, wherein the multi-dimensional biosensor module comprises:

[0043] Heart rate monitors that can record an athlete's heart rate in real time, in seconds, with an accuracy of ±1 bpm; used to continuously track an athlete's heart rate and rhythm;

[0044] Blood pressure monitor, used to measure and record the athlete's blood pressure changes in real time, with a measurement range of systolic pressure 90-180 mmHg and diastolic pressure 60-120 mmHg, with an accuracy of ±3 mmHg, to assess the state of the cardiovascular system;

[0045] Muscle activity sensor, used to capture the electrical signals of muscle contraction and relaxation, capture electromyographic signals, with a frequency response range of 10Hz-500Hz, and adjustable gain to suit different muscle groups, reflecting muscle activity intensity and fatigue level;

[0046] Galvanic skin response sensor, used to detect changes in skin conductivity caused by emotional changes, measuring conductivity changes of 0.01-10μS with a resolution of 0.01μS, providing data for evaluating the psychological state of athletes;

[0047] Facial expression recognition sensors, which use high-resolution cameras to capture subtle facial changes and analyze facial muscle movements to identify athletes’ emotional states;

[0048] Heart Rate Variability Analysis Sensor, used to measure heart rate variability and analyze the natural fluctuations in heart rate with a time resolution of 1ms and detect changes as small as 0.1ms to assess an athlete’s autonomic nervous system activity and stress level.

[0049] The data acquisition and transmission module uses wireless communication technology, operates in the IEEE 802.15.4 standard of the 2.4GHz frequency band, provides a data transmission rate of up to 250kbps, and ensures low-power communication within a range of 10 meters; for data transmission over longer distances, Bluetooth 5.0 technology is used, which can achieve a data transmission rate of up to 2Mbps within a range of 100 meters, and has advanced error correction and data packet retransmission mechanisms to ensure the integrity and stability of data transmission. For application scenarios that require higher bandwidth, the data acquisition and transmission module uses Wi-Fi 6 technology, which supports a data transmission rate of up to 9.6Gbps and improved signal penetration, as well as the ability to connect multiple users concurrently in high-density environments.

[0050] The real-time data analysis module realizes rapid response and efficient processing of sensor data by adjusting parameters. The real-time data analysis module is used for data processing, and its processing method includes the following steps:

[0051] S1, the real-time data analysis module starts the data cleaning process within 50 milliseconds after the sensor data arrives, pre-processes the sensor data, and initially cleans the sensor data to remove noise and outliers;

[0052] S2, convert sensor data into a standard format through a standardized algorithm;

[0053] S3, feature value extraction, through the fast Fourier transform technology, identify the key frequency components in the data and capture the subtle changes in the athlete's physiological state.

[0054] The real-time data analysis module adopts a parallel processing architecture, and the engine can process up to 100,000 data points in a short time to meet the real-time analysis needs of high-frequency data streams. The real-time data analysis module is equipped with an adaptive filter, which dynamically identifies valid signals in the data stream, improves the signal-to-noise ratio, and significantly enhances the signal quality.

[0055] The machine learning and pattern recognition module uses a convolutional neural network to learn the features of the sensor data and extract key features, wherein the convolutional neural network contains at least three convolutional layers and pooling layers, each convolutional layer has 64 3x3 convolution kernels, and the ReLU activation function is used to introduce nonlinearity; the machine learning and pattern recognition module uses a recurrent neural network to analyze the sensor data, especially a long short-term memory network, which consists of 50 neurons and can process data sequences up to 100 time steps to capture the dynamic trends and periodic patterns of the athlete's performance; the machine learning and pattern recognition module uses a clustering algorithm, such as K-means, setting the number of clusters to k=5, the number of iterations to 100, and the convergence threshold to 0.001, to cluster the athlete's sensor data and find the data distribution characteristics under different training states. The machine learning and pattern recognition module integrates a support vector machine classifier, the support vector machine classifier, sets the C parameter to 1.0, the kernel function to RBF, and the γ parameter to be determined by cross-validation to optimize the classification performance and classify the athlete's psychological state; the machine learning and pattern recognition module evaluates the athlete's performance trend and psychological state. The training process is accelerated on a system equipped with an NVIDIA GTX 1080Ti GPU, using its 11GB GDDR5X video memory to complete a training task containing 5 million samples in 5 minutes. The model evaluation uses 10-fold cross validation, with an accuracy of more than 90%, and a recall rate and F1 score of more than 85%, ensuring the high generalization ability and accuracy of the model.

[0056] The personalized suggestion generation module dynamically generates personalized training suggestions by comprehensively analyzing the athlete's real-time physiological data and historical training data. The personalized suggestion generation module receives the processed sensor data, the athlete's performance trend and psychological state evaluation, combines the athlete's personal data, and performs optimization calculations through an algorithm model to generate training suggestions or recovery plans.

[0057] The personalized suggestion generation module first receives the data processed by the real-time data analysis module, including but not limited to key physiological indicators of heart rate (60-180bpm), blood pressure (systolic pressure 90-180mmHg, diastolic pressure 60-120mmHg), muscle activity (electromyographic signal amplitude 0-10mV), and athlete performance trends and psychological state assessments provided by the machine learning module. Combined with the athlete's personal information, such as age, weight, and exercise habits, the generator uses an optimization algorithm, such as setting the population size of the genetic algorithm to 100, the number of iterations to 30, the crossover rate and mutation rate to 0.8 and 0.01 respectively, and the initial temperature of the simulated annealing algorithm to 1000 and the cooling rate to 0.98, to perform optimization calculations on the training plan. The generated training suggestions are specific to exercise intensity (such as 60%-85% of maximum heart rate), training time (30-90 minutes), rest interval (1-5 minutes), and nutritional supplementation suggestions (such as 0.8-1.2 grams of carbohydrates per kilogram of body weight). For example, if the athlete's heart rate takes more than 2 minutes to return to resting heart rate, the personalized suggestion generation module will automatically adjust the training intensity, reduce the exercise load by 10%, and increase the rest time by 5%. The personalized suggestion generation module also automatically adjusts the algorithm parameters based on the athlete's response to the training suggestions and the coach's feedback, such as setting the convergence threshold of the optimization algorithm to 0.0001, to ensure the continued effectiveness and personalization of the training plan.

[0058] The user interface module includes a coach view and an athlete view, providing customized information display and control options; the user interface module feeds back customized training and recovery plans to coaches and athletes.

[0059] The coaching view has advanced data monitoring functions, which can track the athlete's heart rate (60-180bpm), blood pressure (systolic 90-180mmHg, diastolic 60-120mmHg) and exercise intensity (maximum oxygen uptake expressed as a percentage) in real time. It also provides a detailed review of historical training records and adjustment functions for training plans, allowing coaches to adjust training parameters based on the athlete's real-time performance, such as setting the heart rate training threshold to 60% to 85% of the maximum heart rate.

[0060] The athlete view focuses on displaying personal training data, using easy-to-understand charts and progress bars to clearly display the current training status, including exercise intensity and estimated recovery time. This view provides personalized training suggestions, such as adjusting exercise intensity to 70% of maximum heart rate to optimize fat burning, and a simple interface to provide feedback after training. The interface design takes into account the convenience of operation during exercise, the size of buttons and sliders is suitable for touch operation, and the response time is less than 100 milliseconds to ensure instant feedback.

[0061] Both the coach view and the athlete view support multi-device access, and the interface elements use high-contrast color schemes and adjustable font sizes to accommodate users with different vision levels.

[0062] The user interface module also includes an intelligent notification system that integrates multiple prompts, including visual prompts (such as flashing icons or color changes) and auditory prompts (such as prompts of different tones) to ensure that attention can be drawn even in noisy environments. When the athlete's training intensity exceeds the preset threshold, the system will issue a warning through the interface within 3 seconds and use sound to prompt the athlete to reduce the intensity of exercise.

[0063] The rest is the same as Example 1.

[0064] The application also includes a data security and privacy protection module, which uses encryption and anonymization technology and access control mechanism to ensure the security of data during transmission and storage; the module uses AES-256-bit symmetric encryption algorithm with a key length of 256 bits, providing strong data encryption capabilities to ensure that data during wireless transmission is difficult to crack even if it is intercepted. For data storage on the server side, RSA-2048-bit asymmetric encryption algorithm is used with a key length of 2048 bits to provide a higher level of security protection.

[0065] In terms of anonymization technology, the module uses the SHA-256 hash function to generate a unique hash value of 256 bits in length to desensitize the athletes' personal information, ensuring that the personal information cannot be reversely identified during data analysis and storage.

[0066] The data security and privacy protection module uses tokenization technology to generate a 32-bit random encrypted token for each athlete, replacing their identity information for data exchange within the system.

[0067] In terms of access control mechanism, the data security and privacy protection module implements role-based access control to ensure that different roles access corresponding resources according to their permission levels. All data access behaviors will be recorded in the access log, and the log retention period is set to no less than 6 months to facilitate post-audit and tracking. Security maintenance measures include security vulnerability scanning and penetration testing at least once a quarter to promptly discover and fix potential security issues.

[0068] This application also includes a system integration and compatibility module, which achieves seamless integration with different devices by supporting multiple communication protocols and data formats; the module supports IEEE 802.11a / b / g / n / ac wireless communication standards, ensuring that data transmission rates on the 2.4GHz and 5GHz frequency bands can reach 600Mbps and 1300Mbps respectively; it is also compatible with Bluetooth 5.0, providing a maximum data transmission rate of 2Mbps and a transmission range of 100 meters; it also supports the ANT+ protocol, designed specifically for sports and fitness equipment, and provides stable data transmission.

[0069] In terms of data format, the module supports JSON format, which allows flexible data structure and easy-to-read syntax; XML format, which provides rich data description capabilities; and CSV format, which is suitable for simple data recording and exchange. For the field of sports training, the module also supports professional data formats such as TCX, FIT and HRM to ensure the accuracy and professionalism of training data.

[0070] The system integration and compatibility module uses dynamic protocol selection technology, which can select the optimal communication protocol within 100ms based on device capabilities and network environment. The built-in adapters and converters of the system integration and compatibility module can complete the conversion between different data formats within 50ms, ensuring the instant interoperability of data. The module provides a comprehensive SDK and API, including more than 200 function call interfaces, covering the entire process from data collection to analysis, and providing rich integration options for third-party developers. The module design reserves interfaces for software upgrades and hardware expansions, and supports firmware updates via OTA to ensure that the system can adapt to future technological developments and support for new devices. Through these comprehensive designs and parameter settings, the system integration and compatibility module provides the system with powerful scalability and compatibility, ensuring the long-term stable operation and technological leadership of the system.

[0071] A method for using a real-time athlete performance monitoring and optimization system, such as Figure 2 As shown, it includes the following steps:

[0072] S1. Install and calibrate the multi-dimensional biosensor module: Install the sensor on the athlete or his sports equipment and ensure the measurement accuracy through the calibration process, such as calibrating the heart rate monitor to ±1bpm accuracy and the blood pressure monitor to ±3mmHg accuracy.

[0073] S2. Start the data acquisition and transmission module: activate wireless communication technology and set transmission parameters, such as IEEE802.11a / b / g / n / ac standards, to ensure data transmission rate and stability.

[0074] S3. Use the real-time data analysis module to perform data preprocessing and feature extraction: set data cleaning thresholds, such as removing data points that exceed ±2 standard deviations, perform standardization transformation, and apply fast Fourier transform for feature extraction.

[0075] S4, Applied Machine Learning and Pattern Recognition Module: Configure the deep learning network structure, such as the number of CNN and LSTM layers and neurons, set training parameters, such as learning rate 0.001 and batch size 32, and perform model training and verification.

[0076] S5. Generate training and recovery recommendations through the personalized recommendation generation module: input athlete personal information and historical performance data, set generator algorithm parameters, such as the population size and number of iterations of the optimization algorithm, and generate a personalized training plan.

[0077] S6. Display data, suggestions and receive feedback in the user interface module: Configure interface display parameters, such as chart type and data refresh frequency, to ensure that coaches and athletes can intuitively understand data and suggestions and analyze athlete performance patterns.

[0078] S7. Ensure data security through the data security and privacy protection module: set encryption algorithm parameters, such as AES-256 bit key length, and implement data desensitization and access control strategies.

[0079] S8. Integrate with existing equipment using the System Integration and Compatibility module: Configure communication protocols and data format converters to ensure compatibility with third-party equipment or systems.

[0080] S9. Adjust and optimize system performance based on feedback from athletes and coaches: Collect feedback information, evaluate system performance indicators such as response time and user satisfaction, and make necessary system adjustments.

[0081] S10. Update machine learning models regularly to adapt to changes in athlete performance: Set a model update cycle, such as once a quarter, retrain the model based on the latest data, and optimize the algorithm parameters to capture the latest trends in athlete performance.

[0082] The present invention has been described in detail above. The above description is only a preferred embodiment of the present invention and should not limit the scope of implementation of the present invention. That is, all equivalent changes and modifications made within the scope of this application should still fall within the scope of the present invention.

Claims

1. A real-time monitoring and optimization system for athlete performance, including a control system, characterized in that: It also includes a multi-dimensional biosensor module and a data acquisition and transmission module. The multi-dimensional biosensor module captures the athlete's physiological and psychological data in real time. The multi-dimensional biosensor module transmits the acquired sensor data to the control system through the data acquisition and transmission module. The control system includes a real-time data analysis module, a machine learning and pattern recognition module, and a personalized suggestion generation module; the real-time data analysis module is connected to the machine learning and pattern recognition module, the machine learning and pattern recognition module identifies the athlete's performance pattern and optimization potential, the machine learning and pattern recognition module is connected to the personalized suggestion generation module, and the personalized suggestion generation module generates customized training suggestions and recovery plans for feedback.

2. The real-time monitoring and optimization system for athlete performance according to claim 1, characterized in that: The multi-dimensional biosensor module comprises: Heart rate monitors, which continuously track an athlete’s heart rate and rhythm; Blood pressure monitor, used to measure and record the athlete's blood pressure changes in real time to assess the status of the cardiovascular system; Muscle activity sensors, which are used to capture electrical signals of muscle contraction and relaxation, reflecting muscle activity intensity and fatigue level; Galvanic skin response sensors are used to detect changes in skin conductivity caused by emotional changes, providing data for evaluating the psychological state of athletes; Facial expression recognition sensors that analyze the movement of facial muscles to identify an athlete’s emotional state; Heart rate variability analysis sensor to measure heart rate changes to assess athletes' autonomic nervous system activity and stress levels.

3. The real-time monitoring and optimization system for athlete performance according to claim 1, characterized in that: The data acquisition and transmission module adopts wireless communication technology, and the wireless communication technology includes Wi-Fi 6 technology and Bluetooth 5.0 technology.

4. The real-time monitoring and optimization system for athlete performance according to claim 1, characterized in that: The real-time data analysis module is used for data processing, and the processing method thereof comprises the following steps: S1. Preprocess the sensor data and preliminarily clean the sensor data to remove noise and outliers; S2, convert sensor data into a standard format through a standardized algorithm; S3, feature value extraction, identifying key frequency components and trends in the data through fast Fourier transform technology.

5. The real-time monitoring and optimization system for athlete performance according to claim 4, characterized in that: The machine learning and pattern recognition module uses a convolutional neural network to perform feature learning on sensor data and extract key features; The machine learning and pattern recognition module uses a recurrent neural network to analyze sensor data to capture the dynamic trends and periodic patterns of athletes' performance. The machine learning and pattern recognition module uses a clustering algorithm to perform cluster analysis on athletes' sensor data to discover data distribution characteristics under different training states.

6. The real-time monitoring and optimization system for athlete performance according to claim 5, characterized in that: The machine learning and pattern recognition module is integrated with a support vector machine classifier to classify the psychological state of the athletes; the machine learning and pattern recognition module evaluates the performance trend and psychological state of the athletes.

7. The real-time monitoring and optimization system for athlete performance according to claim 6, characterized in that: The personalized suggestion generation module receives the processed sensor data, athlete performance trends and psychological state assessment, combines the athlete's personal data, performs optimization calculations through an algorithm model, and generates training suggestions or recovery plans.

8. The real-time monitoring and optimization system for athlete performance according to claim 7, characterized in that: It also includes a user interaction interface module, which includes a coach view and an athlete view. The user interaction interface module feeds back the customized training and recovery plan to the coach and the athlete.

9. A method for using a real-time athlete performance monitoring and optimization system, characterized in that: The real-time monitoring and optimization system for athlete performance according to any one of claims 1 to 8 comprises the following steps: S1. Install and calibrate the multi-dimensional biosensor module: Install the sensor on the athlete or his sports equipment and ensure the measurement accuracy through the calibration process; S2, start the data acquisition and transmission module: activate the wireless communication technology and set the transmission parameters; S3. Use the real-time data analysis module to perform data preprocessing and feature extraction: set the data cleaning threshold; S4, Applied Machine Learning and Pattern Recognition Module: Configure deep learning network structure to analyze athlete performance patterns; S5. Generate training suggestions or recovery plans through a personalized suggestion generation module: input athlete personal information and historical performance data, and set generator algorithm parameters; S6. Display data, suggestions and receive feedback in the user interaction interface module: configure interface display parameters; S7. Adjust and optimize system performance based on feedback from athletes and coaches: collect feedback information and evaluate system performance indicators.

10. The method for using the real-time monitoring and optimization system for athlete performance according to claim 9, characterized in that: This also involves regularly updating machine learning models to adapt to changes in athlete performance.

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