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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.

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

  • 章节

  • 发表日期：

    2025年

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  • Carlo Dindorf,
  • Fabian Horst,
  • Djordje Slijepčević,
  • Bernhard Dumphart,
  • Jonas Dully,
  • Matthias Zeppelzauer,
  • Brian Horsak,
  • Michael Fröhlich

摘要

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.