Intelligent Optimization and Recommendation System Design for Personalized Training Programs for Marathon Athletes based on Machine Learning

Abstract

This research focuses on developing an innovative machine learning-based intelligent optimization and recommendation system for marathon runners' personalized training schemes. The system aims to provide accurate and dynamically adjusted training guidance for athletes through real-time monitoring, training effect evaluation and intelligent recommendation. First, the system uses advanced wearable technology to achieve real-time monitoring of multiple physiological and athletic data during athlete training, including but not limited to heart rate variability, lactate threshold, and gait analysis. This data forms the basis of a personalized training program. Secondly, the support vector machine (SVM) algorithm is used to evaluate the training effect of the collected data. Finally, the system combines individual characteristics and the historical performance of athletes and generates personalized training plans through optimization strategies such as support vector machines. This process not only considers the short-term training goals but also considers the long-term sports career planning. Through algorithm modeling and computer simulation, it is found that the system can realize the continuous optimization of the training scheme in the process of continuous iteration. The intelligent system significantly improves athletes' training efficiency and competition performance compared to traditional training methods. This study provides a new perspective and practice path for intellectualization in sports training.