The Joe Gibbs Human Performance Institute (JGHPI) is at the forefront of revolutionizing biomechanical evaluations in sports. Equipped with over 80 triaxial force plates strategically embedded under our flooring, our facility captures precise athlete movement data, offering unparalleled insights into performance and injury risk. Our state-of-the-art optical and inertial tracking systems enable athletes to move freely while capturing precise kinetic and kinematic data, ensuring no detail is missed in the biomechanical analysis. Additionally, JGHPI ingests and stores in-game player tracking data, along with corresponding annotations and markings, for partners from several professional sports leagues. Machine learning, deep learning specifically, plays a significant role in not only the analysis but also the pipelines that feed the data from sources into highly usable, efficiently stored formats. This presentation delves into the work conducted by the JGHPI machine learning group, highlighting how advanced algorithms and data pipelines are transforming the way we analyze and interpret biomechanical data.

Presenter: Eddie Mitchell, Head of Machine Learning

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