Real-Time Athlete Fatigue Monitoring Using Fuzzy Decision Support Systems
Aiqin Li
Abstract
Sports scientists worry about fatigue, because it affects performance, increases injury risk, and harms health. Traditional fatigue measurements may miss this complicated and ever-changing state, placing athletes at risk of injury while training. This study tests the premise that cutting-edge, real-time monitoring devices improve athlete health and performance. This research aims to reduce tiredness by developing a Fuzzy Decision Support System for Real-Time Athlete Weariness Monitoring. Fuzzy logic can handle unclear performance data, making it a more flexible and advanced alternative to standard methods. Sports athlete fatigue is complicated and dynamic, requiring improved, more precise, and real-time monitoring approaches. The FDSS-RAFM model uses fuzzy logic to account for human performance and physiology. The FDSS-RAFM model assesses athlete fatigue in a comprehensive and context-aware manner. This study’s findings can help coaches, players, and sports scientists improve training programs, reduce injury risk, and improve performance in ever-changing athletic contexts. Fuzzy decision-support systems and other cutting-edge technology can improve athletes’ health and performance, adding to sports science literature. Experimental results show that the proposed FDSS-RAFM model outperforms competing models in sensitivity (97%), specificity (89%), accuracy (96%), and dynamic adaptation error analysis (2.41%).