Research on Personalized Track and Field Training Programs Based on Advanced Artificial Intelligence Models

Modern track and field, which is practiced in many different disciplines but under the cover of individualized training, is based on the particular physical condition and competitive goals of the athlete as well as performance statistics. Conventional methods fail to consider the dynamic variations of athletes since they are mostly based on fixed data and coach-oriented intuitive approach. In order to solve these difficulties, we provide in this work a fresh plan formulation strategy using multimodal fusion techniques and cross-modal learning. The model develops a customized training schedule using the powerful NLP capacity of LLMs by bridging several kinds of data (the physiological signals, motion trajectories, and text input) into a single structure. Against conventional approaches, experimental data show that the suggested approach significantly improves training optimization, planning flexibility and fusion efficiency. Results of this study highlight the revolutionary relevance of AI-based best running practices that can help to improve the accuracy and efficiency of track and field training approaches.