Research on Intelligent Monitoring and Adjustment Algorithm of Training Load of College Basketball Players

The study selected 15 male university basketball players with at least two years of professional training experience as subjects. Polar OH10 heart rate monitors and Actigraph GT3X triaxial accelerometers were used to monitor changes in heart rate and exercise intensity, calculating the Training Impulse (TRIMP) values to quantify training load. Through a longitudinal study design, athletes were monitored for heart rate throughout an 8-week mid-cycle and four matches, analyzing trends in training load changes and comparing training loads with match loads. The study developed an intelligent monitoring and adjustment algorithm based on heart rate characteristics, using a Random Forest (RF) model to assess athletes' fatigue and recovery status, and designing a Training Adjustment Index (TAI) to guide adjustments in training plans. The results show that TRIMP values accurately reflect the changes in load during different training phases and matches. The intelligent monitoring algorithm effectively controls the accumulation of athlete fatigue and optimizes training load through real-time tracking and analysis of heart rate features. This study provides empirical evidence for the scientific training of university basketball players, emphasizing the importance of personalized and dynamic training adjustments, and offers a reference for the intelligent development of sports training.