Research progress in exercise-induced fatigue monitoring and injury early warning based on flexible sensing textiles and deep learning.

Accurate identification of exercise induced fatigue and real time injury early warning is a core requirement for scientific training in competitive sports. Traditional laboratory based biomechanical monitoring is hindered by spatial constraints and limited ecological validity. The integration of flexible sensing textiles and deep learning has emerged as a disruptive solution. This paper reviews recent progress in flexible sensing textiles for athlete monitoring. First, the mechanical response characteristics of Frontier sensing technologies including self-powered triboelectric nanogenerators, piezoresistive or capacitive sensors, and liquid metals are analyzed for capturing microscopic biomechanical signals. Second, deep learning architectures such as CNN, Long Short-Term Memory, and Transformers are discussed for signal denoising, action phase segmentation, and fatigue feature mining. Crucially, the early warning logic based on the fatigue compensation injury causal chain is elaborated, covering real-time high-risk movement monitoring for acute injuries, cumulative load evaluation for overuse injuries, and digital twin driven individualized benchmarking. Finally, future challenges including signal robustness, washability, and multimodal data fusion are addressed. This review establishes a biomechanically informed theoretical framework for smart sports apparel, facilitating a paradigm shift toward closed loop intelligent prediction in injury prevention.