The Application of AI Physical Fitness in the Physical Training of High-level Table Tennis Athletes

The integration of artificial intelligence (AI) into physical fitness training has emerged as a transformative approach to enhancing the performance of high-level table tennis athletes. This study investigates the application of AI-driven systems in optimizing both health-related (e.g., cardiorespiratory endurance, body composition, flexibility) and skill-related (e.g., speed, reaction time, agility) physical fitness components. By leveraging advanced technologies such as wearable sensors, machine learning algorithms, and virtual reality (VR), AI enables real-time monitoring, personalized training program adjustments, and predictive injury prevention. For instance, AI-optimized high-intensity interval training (HIIT) improved athletes’ VO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> max by 14.3%, while machine learning-driven agility training enhanced directional transition efficiency by $\mathbf{2 4. 5 \%}$. Case studies from national teams in China, Japan, and Germany demonstrated the efficacy of AI in identifying biomechanical deficiencies and reducing injury risks by up to 34%. Despite challenges such as data privacy concerns and technical complexity, the study highlights AI’s potential to revolutionize traditional training paradigms through precision, adaptability, and scientific rigor. The findings underscore the critical role of AI in advancing athletic performance, extending career longevity, and promoting interdisciplinary innovation in sports science.