Machine Learning in Biomechanics: Key Applications and Limitations in Walking, Running, and Sports Movements
It addresses challenges in biomechanical workflows for researchers and practitioners, but is incremental as it provides an overview rather than new findings.
This chapter reviews machine learning applications in gait and sports biomechanics, covering pose estimation, event detection, and classification, while highlighting limitations such as data availability and explainability.
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.