Design of Training Load Monitoring and Adjustment Algorithm for Athletes: Based on Heart Rate Variability and Body Index Data

Abstract

The training load monitoring and adjustment algorithm for athletes, based on heart rate variability (HRV) and body index data, offers a comprehensive approach to optimizing athletic performance and minimizing the risk of injury. By leveraging HRV data, which reflects the autonomic nervous system's response to training stress, and body index measurements such as body mass index (BMI) or body fat percentage, the algorithm provides insights into athletes' physiological readiness and recovery status. The design of an effective training load monitoring and adjustment algorithm is critical for optimizing athletic performance while minimizing the risk of injury and overtraining. This paper proposes a novel approach that integrates heart rate variability (HRV) and body index data to tailor training programs to individual athlete needs. This paper presents an innovative approach to training load monitoring and adjustment for athletes, utilizing heart rate variability (HRV) and body index data. Through continuous monitoring and analysis of HRV metrics such as RMSSD and LF/HF Ratio, in conjunction with body mass index (BMI) and body fat percentage, personalized load management strategies are developed to optimize athletic performance while mitigating the risk of injury and overtraining. The optimization algorithm outlined in this study allows for real-time adjustments to training loads based on individual physiological responses, ensuring that athletes receive tailored training programs that maximize performance gains and promote long-term health and well-being. By leveraging HRV and body index data, coaches and sports scientists can enhance athletic performance outcomes and support the overall development and longevity of athletes' careers.