AI-Driven Insights in Exercise Physiology: Enhancing Training Optimization and Fatigue Management for Athletes

Abstract

Background: Artificial intelligence (AI) has become a transformative tool in optimizing athletic training and managing fatigue. AI-driven models enable precise analysis of physiological responses, predictive fatigue monitoring, and personalized training programs tailored to individual athletes. Methods: This review literature examined recent studies on AI applications in exercise physiology and fatigue management using databases such as PubMed, Web of Science, Google Scholar and Elicit. The selected studies implemented AI methodologies, analyzing 50 papers and extracting 60 key data points to assess training optimization effectiveness. Results: Findings indicate that AI models effectively process complex physiological data in real time, provide instant feedback on training intensity, and adjust workout plans to individual capabilities. Studies demonstrated AI-driven training approaches improving endurance, reducing perceived exertion, and refining biomechanical assessments. Additionally, reinforcement learning-based virtual coaching yielded training outcomes comparable to or better than human-designed plans. AI systems also enhanced fatigue prediction, offering optimized recovery strategies to prevent overtraining. Conclusion: AI-based solutions play a crucial role in enhancing athletic performance, offering personalized and adaptive training while effectively managing fatigue and stress. The increasing integration of AI into exercise physiology underscores its potential to revolutionize sports training by refining performance metrics, minimizing injury risks, and maximizing efficiency across different disciplines.

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