AI-Based Kinematic Analysis for Track Athletes

This paper explores the critical task of analyzing running postures and recognizing their key roles in enhancing athletic performance and averting injuries. Our approach inte-grates AI technologies, with video pose estimation and kinematic analysis, to offer comprehensive and detailed feedback on a runner's posture and technique. Leveraging MediaPipe, an ad-vanced deep learning framework, we conduct thorough kinematic analyses by tracking essential body landmarks and analyzing four fundamental postures: toe-off, maximal vertical projection, touch-down, and full-support. The classification of these postures is facilitated by a random forest tree classifier, which consistently demonstrates high accuracy, and is validated by experienced coaches. Our system goes beyond simple classification; it provides detailed feedback, highlights distinctions in form and technique, and assists runners and coaches in refining performance. This methodology not only optimizes athletic prowess but also analyzes real-world track running data, as opposed to unnatural data collected by laboratory-based systems.