mlsanityData to 14 Oct 2026

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

arXiv 2503.03717 ↗First posted 5 Mar 2025cs.AIcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 27 of its 264 references that are in the map. First posted on arXiv 5 Mar 2025.

Builds on

27 of 264 references.

References · 27

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15 more
  1. 2020Monocular Human Pose Estimation: A Survey of Deep Learning-based Methodscited by 59
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Citing papers per year: 2025: 1.

1 paper.

  1. 2025Outlier Detection in Plantar Pressure: Human-Centered Comparison of Statistical Parametric Mapping and Explainable Machine Learningcited by 0