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Validity of neural networks in determining lower limb kinematics in stationary cycling

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posted on 2024-03-08, 01:25 authored by Rodrigo Rico-BiniRodrigo Rico-Bini, VB Nascimento, Aiden NibaliAiden Nibali
Purpose: Increasing access to marker-less technology has enabled practitioners to obtain kinematic data more quickly. However, the validation of many of these methods is lacking. Therefore, the validity of pre-trained neural networks was explored in this study compared to reflective marker tracking from sagittal plane cycling motion. Methods: Twenty-six cyclists were assessed during stationary cycling at self-selected cadence and moderate intensity exercise. Standard video from their sagittal plane was obtained to extract joint kinematics. Hip, knee, and ankle angles were calculated from marker digitisation and from two deep learning-based approaches (TransPose and MediaPipe). Results: Typical errors ranged between 1 and 10° for TransPose and 3–9° for MediaPipe. Correlations between joint angles calculated from TransPose and marker digitalization were stronger (0.47–0.98) than those from MediaPipe (0.25–0.96). Conclusion: TransPose seemed to perform better than MediaPipe but both methods presented poor performance when tracking the foot and ankle. This seems to be associated with the low frame rate and image resolution when using standard video mode.

History

Publication Date

2024-03-01

Journal

Sport Sciences for Health

Volume

20

Pagination

127–136

Publisher

Springer Nature

ISSN

1824-7490

Rights Statement

© The Author(s) 2023 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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