Beyond Training: A Personalized Holistic Injury Prediction in Triathletes

Triathlon training combines swimming, cycling, and running, often at high volumes, to prepare athletes for long-distance events. The highly intense physical demand puts athletes at significant risk of overuse injuries. While wearable devices provide continuous, high-frequency insights into an athlete's physiological response to training, extracting meaningful, actionable patterns remains a challenge - especially for everyday users. Understanding these metrics and their relationship with injury risk is critical to optimizing training strategies and preventing injuries before they occur. This work proposes a learning model to identify patterns that indicate an increased risk of injury, allowing proactive adjustments to training loads. However, building a generalizable model in sports science and healthcare presents a key challenge: the need for large, high-quality labelled datasets, which are often limited by privacy concerns. To address this limitation, this work also explores the generation and application of a highly realistic synthetic dataset that ensures robust model training while mitigating privacy constraints.