Multimodal Deep Learning and Reinforcement Learning Framework for Personalized Sports Training and Recovery Optimization

Abstract

In this paper, an intelligent exercise training and recovery system based on multi-modal data fusion is proposed, which aims to optimize the training plan through an AI-driven model and predict the training load and recovery requirements in real time. The system integrates athletes' physiological signals, motion images and environmental data, totaling a data set of 800 athletes. The Xception model is used to extract the spatial characteristics of the moving image, and the BiLSTM model is used to analyze the dynamic characteristics in the time series data to achieve accurate prediction of training load and recovery time. On this basis, the deep deterministic policy gradient (DDPG) reinforcement learning algorithm is used to dynamically adjust the training intensity, duration and frequency based on real-time feedback. The experimental results show that when tested under different environmental conditions, the mean square error (MSE) of the training load prediction of the system is less than 0.05, the determination coefficient (R2) is close to 0.99, and the recovery time prediction is stable between 0.018 and 0.022 in the 10-fold cross-verification. The R2 value is as high as 0.98. Compared with the traditional training system, the injury rate of athletes in this system is significantly reduced, with an average injury rate of only 0.07, much lower than the traditional system of 0.12 to 0.22. Studies have shown that the system has a wide range of application potential, especially in high-intensity training and personalised training optimisation in complex environments.