Triathlon training combines swimming, cycling, and running, often at high volumes, to prepare athletes for longdistance 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.
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