This chapter provides an overview of recent and promising Machine Learning applications, i.e. pose estimation, feature estimation, event detection, data exploration & clustering, and automated classification, in gait (walking and running) and sports biomechanics. It explores the potential of Machine Learning methods to address challenges in biomechanical workflows, highlights central limitations, i.e. data and annotation availability and explainability, that need to be addressed, and emphasises the importance of interdisciplinary approaches for fully harnessing the potential of Machine Learning in gait and sports biomechanics.
View on arXiv@article{dindorf2025_2503.03717,
title={ Machine Learning in Biomechanics: Key Applications and Limitations in Walking, Running, and Sports Movements },
author={ Carlo Dindorf and Fabian Horst and Djordje Slijepčević and Bernhard Dumphart and Jonas Dully and Matthias Zeppelzauer and Brian Horsak and Michael Fröhlich },
journal={arXiv preprint arXiv:2503.03717},
year={ 2025 }
}