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Applications and limitations of current markerless motion capture methods for clinical gait biomechanics

BACKGROUND: Markerless motion capture has the potential to perform movement analysis with reduced data collection and processing time compared to marker-based methods. This technology is now starting to be applied for clinical and rehabilitation applications and therefore it is crucial that users of...

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Autores principales: Wade, Logan, Needham, Laurie, McGuigan, Polly, Bilzon, James
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8884063/
https://www.ncbi.nlm.nih.gov/pubmed/35237469
http://dx.doi.org/10.7717/peerj.12995
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author Wade, Logan
Needham, Laurie
McGuigan, Polly
Bilzon, James
author_facet Wade, Logan
Needham, Laurie
McGuigan, Polly
Bilzon, James
author_sort Wade, Logan
collection PubMed
description BACKGROUND: Markerless motion capture has the potential to perform movement analysis with reduced data collection and processing time compared to marker-based methods. This technology is now starting to be applied for clinical and rehabilitation applications and therefore it is crucial that users of these systems understand both their potential and limitations. This literature review aims to provide a comprehensive overview of the current state of markerless motion capture for both single camera and multi-camera systems. Additionally, this review explores how practical applications of markerless technology are being used in clinical and rehabilitation settings, and examines the future challenges and directions markerless research must explore to facilitate full integration of this technology within clinical biomechanics. METHODOLOGY: A scoping review is needed to examine this emerging broad body of literature and determine where gaps in knowledge exist, this is key to developing motion capture methods that are cost effective and practically relevant to clinicians, coaches and researchers around the world. Literature searches were performed to examine studies that report accuracy of markerless motion capture methods, explore current practical applications of markerless motion capture methods in clinical biomechanics and identify gaps in our knowledge that are relevant to future developments in this area. RESULTS: Markerless methods increase motion capture data versatility, enabling datasets to be re-analyzed using updated pose estimation algorithms and may even provide clinicians with the capability to collect data while patients are wearing normal clothing. While markerless temporospatial measures generally appear to be equivalent to marker-based motion capture, joint center locations and joint angles are not yet sufficiently accurate for clinical applications. Pose estimation algorithms are approaching similar error rates of marker-based motion capture, however, without comparison to a gold standard, such as bi-planar videoradiography, the true accuracy of markerless systems remains unknown. CONCLUSIONS: Current open-source pose estimation algorithms were never designed for biomechanical applications, therefore, datasets on which they have been trained are inconsistently and inaccurately labelled. Improvements to labelling of open-source training data, as well as assessment of markerless accuracy against gold standard methods will be vital next steps in the development of this technology.
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spelling pubmed-88840632022-03-01 Applications and limitations of current markerless motion capture methods for clinical gait biomechanics Wade, Logan Needham, Laurie McGuigan, Polly Bilzon, James PeerJ Data Mining and Machine Learning BACKGROUND: Markerless motion capture has the potential to perform movement analysis with reduced data collection and processing time compared to marker-based methods. This technology is now starting to be applied for clinical and rehabilitation applications and therefore it is crucial that users of these systems understand both their potential and limitations. This literature review aims to provide a comprehensive overview of the current state of markerless motion capture for both single camera and multi-camera systems. Additionally, this review explores how practical applications of markerless technology are being used in clinical and rehabilitation settings, and examines the future challenges and directions markerless research must explore to facilitate full integration of this technology within clinical biomechanics. METHODOLOGY: A scoping review is needed to examine this emerging broad body of literature and determine where gaps in knowledge exist, this is key to developing motion capture methods that are cost effective and practically relevant to clinicians, coaches and researchers around the world. Literature searches were performed to examine studies that report accuracy of markerless motion capture methods, explore current practical applications of markerless motion capture methods in clinical biomechanics and identify gaps in our knowledge that are relevant to future developments in this area. RESULTS: Markerless methods increase motion capture data versatility, enabling datasets to be re-analyzed using updated pose estimation algorithms and may even provide clinicians with the capability to collect data while patients are wearing normal clothing. While markerless temporospatial measures generally appear to be equivalent to marker-based motion capture, joint center locations and joint angles are not yet sufficiently accurate for clinical applications. Pose estimation algorithms are approaching similar error rates of marker-based motion capture, however, without comparison to a gold standard, such as bi-planar videoradiography, the true accuracy of markerless systems remains unknown. CONCLUSIONS: Current open-source pose estimation algorithms were never designed for biomechanical applications, therefore, datasets on which they have been trained are inconsistently and inaccurately labelled. Improvements to labelling of open-source training data, as well as assessment of markerless accuracy against gold standard methods will be vital next steps in the development of this technology. PeerJ Inc. 2022-02-25 /pmc/articles/PMC8884063/ /pubmed/35237469 http://dx.doi.org/10.7717/peerj.12995 Text en ©2022 Wade et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Data Mining and Machine Learning
Wade, Logan
Needham, Laurie
McGuigan, Polly
Bilzon, James
Applications and limitations of current markerless motion capture methods for clinical gait biomechanics
title Applications and limitations of current markerless motion capture methods for clinical gait biomechanics
title_full Applications and limitations of current markerless motion capture methods for clinical gait biomechanics
title_fullStr Applications and limitations of current markerless motion capture methods for clinical gait biomechanics
title_full_unstemmed Applications and limitations of current markerless motion capture methods for clinical gait biomechanics
title_short Applications and limitations of current markerless motion capture methods for clinical gait biomechanics
title_sort applications and limitations of current markerless motion capture methods for clinical gait biomechanics
topic Data Mining and Machine Learning
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8884063/
https://www.ncbi.nlm.nih.gov/pubmed/35237469
http://dx.doi.org/10.7717/peerj.12995
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