THE FUTURE OF SPORTS TRAINING: INTEGRATING ARTIFICIAL INTELLIGENCE AND WEARABLE TECHNOLOGY IN PERFORMANCE ENHANCEMENT

Abstract

Artificial intelligence (AI) and wearable technology are incorporated in sports training to provide sports performance monitoring, prevention of injury, and optimization in training. Traditionally, performance evaluation is done subjectively, yet with AI-driven wearables, the real-time, data-driven insights augment the precision and efficiency with which one performs the same. The objective of this study is to examine the effects of wearing AI-integrated wearable training devices on athletic performance, physiological efficiency, and injury risk reduction. The research compares AI-assisted training with other methods and empirically provides insights into what real-time AI analytics can do to improve sports training by optimizing it. A mixed methods approach was used with structured surveys, semi-structured interviews, and a six-week experimental study. The experimental group (AI-assisted training) and the control group (traditional training) were divided. Statistical and machine learning models were applied to key performance metrics such as sprint speed, endurance, heart rate variability (HRV), oxygen utilization, and injury risk reduction using analysis. Training with AI was associated with much enhanced sprint speed (8.4 ± 0.5 m/s vs. control (7.5 ± 0.6 m/s), endurance (40.2 ± 2.5 min vs. 34.7 ± 3.2 min), HRV (65 ± 3.5 ms vs. 56 ± 4.2 ms), and injury risk reduction (31% vs. 12%). Identifying high-risk athletes, the AI-based injury prediction model was able to efficiently predict injury risk to a degree of 91%. Extremely significantly, AI-incorporated wearable technology greatly improves physiological efficiency and reduces the risk of injury in athletic training. Additional future research would better benefit multi-sport applications with long-term impacts for advancing the state of AI-driven sports science.