Context-Aware Decision Support System for Athlete Training Optimization via Multimodal Attention Fusion

Traditional athletic training relies on coaches' subjective experience and single-dimensional physiological data. This study builds a context aware decision support system (DSS) using multimodal fusion and attention-based deep learning to address these drawbacks. It integrates multi-source athlete signals across physiology, behavior, psychology, and environment; uses an attention module to identify core indicators; and runs a closed loop pipeline for real-time training load adjustment. The authors conducted a 12-week experiment with 62 provincial athletes.

Results: show that, relative to the control group, DSS yields an additional 7.1% improvement in explosive power and a 3.1-point higher fatigue-recovery score. Compared with single modal models, the fusion framework increases the state-recognition area under the receiver operating characteristic curve by 16.3% while keeping misjudgment low under overload risks. This work expands the use of intelligent DSS in competitive sports and offers a replicable paradigm for precision athletic training.