Theoretical Logic of Paradigm Shift in Physical Conditioning and Enhancement of Athletic Performance under the Background of Artificial Intelligence

Abstract

The development of artificial intelligence (AI) has driven the transformation of physical training paradigms from experience-oriented to data-driven. Traditional physical training has limitations such as poor individual adaptability, low training accuracy, and inadequate injury prevention and control. In contrast, AI, through technologies like big data analysis, machine learning, and computer vision, can achieve precise monitoring of athletes' physiological indicators and movement patterns, formulate personalized training programs, dynamically optimize the training process through real-time feedback, and effectively predict injury risks and optimize rehabilitation pathways. Although AI applications in physical training face challenges including data privacy and security risks, technical limitations, and a shortage of interdisciplinary talents, by strengthening privacy protection, advancing technological research and development, cultivating relevant talents, and adhering to the concept of human-machine collaboration, AI can still significantly improve training efficiency and athletic performance, providing strong support for the development of competitive sports.