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<p>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%).</p>

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Real-Time Athlete Fatigue Monitoring Using Fuzzy Decision Support Systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月10日

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  • Aiqin Li

摘要

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%).