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Application of machine learning to the identification of joint degrees of freedom involved in abnormal movement during upper limb prosthesis use

To evaluate movement quality of upper limb (UL) prosthesis users, performance-based outcome measures have been developed that examine the normalcy of movement as compared to a person with a sound, intact hand. However, the broad definition of “normal movement” and the subjective nature of scoring ca...

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Autores principales: Wang, Sophie L., Bloomer, Conor, Civillico, Gene, Kontson, Kimberly
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7877744/
https://www.ncbi.nlm.nih.gov/pubmed/33571311
http://dx.doi.org/10.1371/journal.pone.0246795
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author Wang, Sophie L.
Bloomer, Conor
Civillico, Gene
Kontson, Kimberly
author_facet Wang, Sophie L.
Bloomer, Conor
Civillico, Gene
Kontson, Kimberly
author_sort Wang, Sophie L.
collection PubMed
description To evaluate movement quality of upper limb (UL) prosthesis users, performance-based outcome measures have been developed that examine the normalcy of movement as compared to a person with a sound, intact hand. However, the broad definition of “normal movement” and the subjective nature of scoring can make it difficult to know which areas of the body to evaluate, and the expected magnitude of deviation from normative movement. To provide a more robust approach to characterizing movement differences, the goals of this work are to identify degrees of freedom (DOFs) that will inform abnormal movement for several tasks using unsupervised machine learning (clustering methods) and elucidate the variations in movement approach across two upper-limb prosthesis devices with varying DOFs as compared to healthy controls. 24 participants with no UL disability or impairment were recruited for this study and trained on the use of a body-powered bypass (n = 6) or the DEKA limb bypass (n = 6) prosthetic devices or included as normative controls. 3D motion capture data were collected from all participants as they performed the Jebsen-Taylor Hand Function Test (JHFT) and targeted Box and Blocks Test (tBBT). Range of Motion, peak angle, angular path length, mean angle, peak angular velocity, and number of zero crossings were calculated from joint angle data for the right/left elbows, right/left shoulders, torso, and neck and fed into a K-means clustering algorithm. Results show right shoulder and torso DOFs to be most informative in distinguishing between bypass user and norm group movement. The JHFT page turning task and the seated tBBT elicit movements from bypass users that are most distinctive from the norm group. Results can be used to inform the development of movement quality scoring methodology for UL performance-based outcome measures. Identifying tasks across two different devices with known variations in movement can inform the best tasks to perform in a rehabilitation setting that challenge the prosthesis user’s ability to achieve normative movement.
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spelling pubmed-78777442021-02-19 Application of machine learning to the identification of joint degrees of freedom involved in abnormal movement during upper limb prosthesis use Wang, Sophie L. Bloomer, Conor Civillico, Gene Kontson, Kimberly PLoS One Research Article To evaluate movement quality of upper limb (UL) prosthesis users, performance-based outcome measures have been developed that examine the normalcy of movement as compared to a person with a sound, intact hand. However, the broad definition of “normal movement” and the subjective nature of scoring can make it difficult to know which areas of the body to evaluate, and the expected magnitude of deviation from normative movement. To provide a more robust approach to characterizing movement differences, the goals of this work are to identify degrees of freedom (DOFs) that will inform abnormal movement for several tasks using unsupervised machine learning (clustering methods) and elucidate the variations in movement approach across two upper-limb prosthesis devices with varying DOFs as compared to healthy controls. 24 participants with no UL disability or impairment were recruited for this study and trained on the use of a body-powered bypass (n = 6) or the DEKA limb bypass (n = 6) prosthetic devices or included as normative controls. 3D motion capture data were collected from all participants as they performed the Jebsen-Taylor Hand Function Test (JHFT) and targeted Box and Blocks Test (tBBT). Range of Motion, peak angle, angular path length, mean angle, peak angular velocity, and number of zero crossings were calculated from joint angle data for the right/left elbows, right/left shoulders, torso, and neck and fed into a K-means clustering algorithm. Results show right shoulder and torso DOFs to be most informative in distinguishing between bypass user and norm group movement. The JHFT page turning task and the seated tBBT elicit movements from bypass users that are most distinctive from the norm group. Results can be used to inform the development of movement quality scoring methodology for UL performance-based outcome measures. Identifying tasks across two different devices with known variations in movement can inform the best tasks to perform in a rehabilitation setting that challenge the prosthesis user’s ability to achieve normative movement. Public Library of Science 2021-02-11 /pmc/articles/PMC7877744/ /pubmed/33571311 http://dx.doi.org/10.1371/journal.pone.0246795 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication.
spellingShingle Research Article
Wang, Sophie L.
Bloomer, Conor
Civillico, Gene
Kontson, Kimberly
Application of machine learning to the identification of joint degrees of freedom involved in abnormal movement during upper limb prosthesis use
title Application of machine learning to the identification of joint degrees of freedom involved in abnormal movement during upper limb prosthesis use
title_full Application of machine learning to the identification of joint degrees of freedom involved in abnormal movement during upper limb prosthesis use
title_fullStr Application of machine learning to the identification of joint degrees of freedom involved in abnormal movement during upper limb prosthesis use
title_full_unstemmed Application of machine learning to the identification of joint degrees of freedom involved in abnormal movement during upper limb prosthesis use
title_short Application of machine learning to the identification of joint degrees of freedom involved in abnormal movement during upper limb prosthesis use
title_sort application of machine learning to the identification of joint degrees of freedom involved in abnormal movement during upper limb prosthesis use
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7877744/
https://www.ncbi.nlm.nih.gov/pubmed/33571311
http://dx.doi.org/10.1371/journal.pone.0246795
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