Machine Learning in Biomechanics: Key Applications and Limitations in Walking, Running and Sports Movements

Carlo Dindorf (), Fabian Horst (), Djordje Slijepčević (), Bernhard Dumphart (), Jonas Dully (), Matthias Zeppelzauer (), Brian Horsak () and Michael Fröhlich ()
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Carlo Dindorf: University of Kaiserslautern-Landau (RPTU)
Fabian Horst: Johannes Gutenberg-University Mainz
Djordje Slijepčević: St. Pölten University of Applied Sciences
Bernhard Dumphart: St. Pölten University of Applied Sciences
Jonas Dully: University of Kaiserslautern-Landau (RPTU)
Matthias Zeppelzauer: St. Pölten University of Applied Sciences
Brian Horsak: St. Pölten University of Applied Sciences
Michael Fröhlich: University of Kaiserslautern-Landau (RPTU)

A chapter in Artificial Intelligence, Optimization, and Data Sciences in Sports, 2025, pp 91-148 from Springer

Abstract: Abstract This chapter provides an overview of recent and promising Machine Learning applications, i.e. pose estimation, feature estimation, event detection, data exploration and 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.

Keywords: Pose estimation; Feature estimation; Event detection; Clustering; Classification; Gait analysis; Sports; Explainability; Kinematics (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-031-76047-1_4

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DOI: 10.1007/978-3-031-76047-1_4

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