Real-time Artificial Intelligence Text Analysis for Identifying Burnout Syndromes in High-Performance Athletes

Predicting burnout from texts by using AI necessitates an interdisciplinary approach, combining sports science, psychology, and machine learning. This research is essential to understanding the psychological signs of burnout and the methods to gauge it, coupled with expertise in NLP and sentiment analysis techniques. Collecting athletes’ textual data, like logs or interviews, is crucial, along with possible metrics of stamina or fatigue. The role of AI, in particular significantly advanced models like BERT or GPT-3, is to analyze these texts, discerning underlying sentiments that may indicate burnout. AI can identify impending burnout patterns by correlating text-derived sentiments with physical metrics. This study investigated the feasibility of an AI-driven approach to proactive intervention, allowing timely measures to prevent burnout. Additionally, it allows athletes to customize their training to their mental and physical health, thus extending their careers. AI promises to prioritize athletes’ physical and mental well-being, improving performance and team dynamics. A series of experiments were conducted on two datasets to validate this model. The results are promising.