Predicting muscle fatigue during high-intensity interval training using explainable artificial intelligence

Abstract

A hybrid deep learning framework combining convolutional neural networks and gradient boosting decision trees was studied. This framework aims to utilize multimodal physiological data to explain muscle fatigue during high-intensity interval training. In a controlled laboratory environment, participants' skin temperature, heart rate, and surface electromyography data were collected while they trained under a standardized training protocol. By using advanced denoising techniques based on empirical mode decomposition and wavelets to process the raw physiological data, participant-specific normalization and comprehensive feature extraction were performed, including spectral, temporal, and nonlinear parameters. The decision tree ensemble achieved reliable final classification and regression, while the convolutional neural network components completed hierarchical feature abstraction. By integrating Shapley additive explanations, the model becomes more transparent, making the impact of each physiological feature on fatigue progression clear. Thru systematic validation using cross-validation and independent test cohorts, the proposed framework consistently demonstrates stronger predictive and generalization capabilities compared to traditional machine learning and conventional deep learning methods. Feature contribution analysis indicates that the most important factors for predicting subject fatigue are derived from electromyography, heart rate variability, and thermal response. In order to enhance the safety and performance of athletes, this framework allows for real-time, user-friendly monitoring of high-intensity training. This work provides a detailed and interpretable approach to using data-driven sports analysis to predict muscle fatigue.