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Feasibility and validity of a single camera CNN driven musculoskeletal model for muscle force estimation during upper extremity strength exercises: Proof-of-concept

Muscle force analysis can be essential for injury risk estimation and performance enhancement in sports like strength training. However, current methods to record muscle forces including electromyography or marker-based measurements combined with a musculoskeletal model are time-consuming and restri...

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Autores principales: Noteboom, Lisa, Hoozemans, Marco J. M., Veeger, H. E. J., Van Der Helm, Frans C. T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9541110/
https://www.ncbi.nlm.nih.gov/pubmed/36213450
http://dx.doi.org/10.3389/fspor.2022.994221
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author Noteboom, Lisa
Hoozemans, Marco J. M.
Veeger, H. E. J.
Van Der Helm, Frans C. T.
author_facet Noteboom, Lisa
Hoozemans, Marco J. M.
Veeger, H. E. J.
Van Der Helm, Frans C. T.
author_sort Noteboom, Lisa
collection PubMed
description Muscle force analysis can be essential for injury risk estimation and performance enhancement in sports like strength training. However, current methods to record muscle forces including electromyography or marker-based measurements combined with a musculoskeletal model are time-consuming and restrict the athlete's natural movement due to equipment attachment. Therefore, the feasibility and validity of a more applicable method, requiring only a single standard camera for the recordings, combined with a deep-learning model and musculoskeletal model is evaluated in the present study during upper-body strength exercises performed by five athletes. Comparison of muscle forces obtained by the single camera driven model against those obtained from a state-of-the art marker-based driven musculoskeletal model revealed strong to excellent correlations and reasonable RMSD's of 0.4–2.1% of the maximum force (Fmax) for prime movers, and weak to strong correlations with RMSD's of 0.4–0.7% Fmax for stabilizing and secondary muscles. In conclusion, a single camera deep-learning driven model is a feasible method for muscle force analysis in a strength training environment, and first validity results show reasonable accuracies, especially for prime mover muscle forces. However, it is evident that future research should investigate this method for a larger sample size and for multiple exercises.
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spelling pubmed-95411102022-10-08 Feasibility and validity of a single camera CNN driven musculoskeletal model for muscle force estimation during upper extremity strength exercises: Proof-of-concept Noteboom, Lisa Hoozemans, Marco J. M. Veeger, H. E. J. Van Der Helm, Frans C. T. Front Sports Act Living Sports and Active Living Muscle force analysis can be essential for injury risk estimation and performance enhancement in sports like strength training. However, current methods to record muscle forces including electromyography or marker-based measurements combined with a musculoskeletal model are time-consuming and restrict the athlete's natural movement due to equipment attachment. Therefore, the feasibility and validity of a more applicable method, requiring only a single standard camera for the recordings, combined with a deep-learning model and musculoskeletal model is evaluated in the present study during upper-body strength exercises performed by five athletes. Comparison of muscle forces obtained by the single camera driven model against those obtained from a state-of-the art marker-based driven musculoskeletal model revealed strong to excellent correlations and reasonable RMSD's of 0.4–2.1% of the maximum force (Fmax) for prime movers, and weak to strong correlations with RMSD's of 0.4–0.7% Fmax for stabilizing and secondary muscles. In conclusion, a single camera deep-learning driven model is a feasible method for muscle force analysis in a strength training environment, and first validity results show reasonable accuracies, especially for prime mover muscle forces. However, it is evident that future research should investigate this method for a larger sample size and for multiple exercises. Frontiers Media S.A. 2022-09-23 /pmc/articles/PMC9541110/ /pubmed/36213450 http://dx.doi.org/10.3389/fspor.2022.994221 Text en Copyright © 2022 Noteboom, Hoozemans, Veeger and Van Der Helm. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Sports and Active Living
Noteboom, Lisa
Hoozemans, Marco J. M.
Veeger, H. E. J.
Van Der Helm, Frans C. T.
Feasibility and validity of a single camera CNN driven musculoskeletal model for muscle force estimation during upper extremity strength exercises: Proof-of-concept
title Feasibility and validity of a single camera CNN driven musculoskeletal model for muscle force estimation during upper extremity strength exercises: Proof-of-concept
title_full Feasibility and validity of a single camera CNN driven musculoskeletal model for muscle force estimation during upper extremity strength exercises: Proof-of-concept
title_fullStr Feasibility and validity of a single camera CNN driven musculoskeletal model for muscle force estimation during upper extremity strength exercises: Proof-of-concept
title_full_unstemmed Feasibility and validity of a single camera CNN driven musculoskeletal model for muscle force estimation during upper extremity strength exercises: Proof-of-concept
title_short Feasibility and validity of a single camera CNN driven musculoskeletal model for muscle force estimation during upper extremity strength exercises: Proof-of-concept
title_sort feasibility and validity of a single camera cnn driven musculoskeletal model for muscle force estimation during upper extremity strength exercises: proof-of-concept
topic Sports and Active Living
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9541110/
https://www.ncbi.nlm.nih.gov/pubmed/36213450
http://dx.doi.org/10.3389/fspor.2022.994221
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