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

Author

Listed:
  • 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))

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.

Suggested Citation

  • Carlo Dindorf & Fabian Horst & Djordje Slijepčević & Bernhard Dumphart & Jonas Dully & Matthias Zeppelzauer & Brian Horsak & Michael Fröhlich, 2025. "Machine Learning in Biomechanics: Key Applications and Limitations in Walking, Running and Sports Movements," Springer Optimization and Its Applications,, Springer.
    • Handle: RePEc:spr:spochp:978-3-031-76047-1_4
    DOI: 10.1007/978-3-031-76047-1_4
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