Application of LSTM-based Body Fatigue Detection Algorithm in Tai Chi Training

Abstract

Tai Chi training sessions are often lengthy, and athletes are prone to experiencing fatigue during the process. Timely detection of body fatigue can help athletes prevent injuries caused by excessive fatigue. This study combines Long Short-Term Memory (LSTM) networks with facial muscle activity detection models to propose a novel fatigue detection algorithm. In this algorithm, the limitations of LSTM networks in capturing future information are addressed by introducing an improved LSTM network model and combining attention mechanisms to highlight the important features of physical fatigue. This study utilizes hyperspectral imaging technology to extract real-time muscle fatigue signals from the faces of subjects in the dataset. Performance validation of the proposed model shows that it effectively extracts facial fatigue features with a detection accuracy of 97.67% and a recall rate of 96.78%, outperforming other existing models in this field. The model constructed through research has excellent performance and has broad application prospects in current and future technological development due to its high flexibility and adaptability, providing support and innovation momentum for different industries.