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arXiv Open Access 2025

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

Carlo Dindorf Fabian Horst Djordje Slijepčević Bernhard Dumphart Jonas Dully +3 lainnya
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Abstrak

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.

Topik & Kata Kunci

cs.AI

Penulis (8)

C

Carlo Dindorf

F

Fabian Horst

D

Djordje Slijepčević

B

Bernhard Dumphart

J

Jonas Dully

M

Matthias Zeppelzauer

B

Brian Horsak

M

Michael Fröhlich

Format Sitasi

APA MLA BibTeX
Dindorf, C., Horst, F., Slijepčević, D., Dumphart, B., Dully, J., Zeppelzauer, M. et al. (2025). Machine Learning in Biomechanics: Key Applications and Limitations in Walking, Running, and Sports Movements. https://arxiv.org/abs/2503.03717

Akses Cepat

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Informasi Jurnal
Tahun Terbit
2025
Bahasa
en
Sumber Database
arXiv
Akses
Open Access ✓