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Assessment of a markerless motion analysis system for manual wheelchair application
BACKGROUND: Wheelchair biomechanics research advances accessibility and clinical care for manual wheelchair users. Standardized outcome assessments are vital tools for tracking progress, but there is a strong need for more quantitative methods. A system offering kinematic, quantitative detection, wi...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6219189/ https://www.ncbi.nlm.nih.gov/pubmed/30400917 http://dx.doi.org/10.1186/s12984-018-0444-1 |
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author | Rammer, Jacob Slavens, Brooke Krzak, Joseph Winters, Jack Riedel, Susan Harris, Gerald |
author_facet | Rammer, Jacob Slavens, Brooke Krzak, Joseph Winters, Jack Riedel, Susan Harris, Gerald |
author_sort | Rammer, Jacob |
collection | PubMed |
description | BACKGROUND: Wheelchair biomechanics research advances accessibility and clinical care for manual wheelchair users. Standardized outcome assessments are vital tools for tracking progress, but there is a strong need for more quantitative methods. A system offering kinematic, quantitative detection, with the ease of use of a standardized outcome assessment, would be optimal for repeated, longitudinal assessment of manual wheelchair users’ therapeutic progress, but has yet to be offered. RESULTS: This work evaluates a markerless motion analysis system for manual wheelchair mobility in clinical, community, and home settings. This system includes Microsoft® Kinect® 2.0 sensors, OpenSim musculoskeletal modeling, and an automated detection, processing, and training interface. The system is designed to be cost-effective, easily used by caregivers, and capable of detecting key kinematic metrics involved in manual wheelchair propulsion. The primary technical advancements in this research are the software components necessary to detect and process the upper extremity kinematics during manual wheelchair propulsion, along with integration of the components into a complete system. The study defines and evaluates an adaptable systems methodology for processing kinematic data using motion capture technology and open-source musculoskeletal models to assess wheelchair propulsion pattern and biomechanics, and characterizes its accuracy, sensitivity and repeatability. Inter-trial repeatability of spatiotemporal parameters, joint range of motion, and musculotendon excursion were all found to be significantly correlated (p < 0.05). CONCLUSIONS: The system is recommended for use in clinical settings for frequent wheelchair propulsion assessment, provided the limitations in precision are considered. The motion capture-model software bridge methodology could be applied in the future to any motion-capture system or specific application, broadening access to detailed kinematics while reducing assessment time and cost. |
format | Online Article Text |
id | pubmed-6219189 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-62191892018-11-16 Assessment of a markerless motion analysis system for manual wheelchair application Rammer, Jacob Slavens, Brooke Krzak, Joseph Winters, Jack Riedel, Susan Harris, Gerald J Neuroeng Rehabil Methodology BACKGROUND: Wheelchair biomechanics research advances accessibility and clinical care for manual wheelchair users. Standardized outcome assessments are vital tools for tracking progress, but there is a strong need for more quantitative methods. A system offering kinematic, quantitative detection, with the ease of use of a standardized outcome assessment, would be optimal for repeated, longitudinal assessment of manual wheelchair users’ therapeutic progress, but has yet to be offered. RESULTS: This work evaluates a markerless motion analysis system for manual wheelchair mobility in clinical, community, and home settings. This system includes Microsoft® Kinect® 2.0 sensors, OpenSim musculoskeletal modeling, and an automated detection, processing, and training interface. The system is designed to be cost-effective, easily used by caregivers, and capable of detecting key kinematic metrics involved in manual wheelchair propulsion. The primary technical advancements in this research are the software components necessary to detect and process the upper extremity kinematics during manual wheelchair propulsion, along with integration of the components into a complete system. The study defines and evaluates an adaptable systems methodology for processing kinematic data using motion capture technology and open-source musculoskeletal models to assess wheelchair propulsion pattern and biomechanics, and characterizes its accuracy, sensitivity and repeatability. Inter-trial repeatability of spatiotemporal parameters, joint range of motion, and musculotendon excursion were all found to be significantly correlated (p < 0.05). CONCLUSIONS: The system is recommended for use in clinical settings for frequent wheelchair propulsion assessment, provided the limitations in precision are considered. The motion capture-model software bridge methodology could be applied in the future to any motion-capture system or specific application, broadening access to detailed kinematics while reducing assessment time and cost. BioMed Central 2018-11-06 /pmc/articles/PMC6219189/ /pubmed/30400917 http://dx.doi.org/10.1186/s12984-018-0444-1 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Rammer, Jacob Slavens, Brooke Krzak, Joseph Winters, Jack Riedel, Susan Harris, Gerald Assessment of a markerless motion analysis system for manual wheelchair application |
title | Assessment of a markerless motion analysis system for manual wheelchair application |
title_full | Assessment of a markerless motion analysis system for manual wheelchair application |
title_fullStr | Assessment of a markerless motion analysis system for manual wheelchair application |
title_full_unstemmed | Assessment of a markerless motion analysis system for manual wheelchair application |
title_short | Assessment of a markerless motion analysis system for manual wheelchair application |
title_sort | assessment of a markerless motion analysis system for manual wheelchair application |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6219189/ https://www.ncbi.nlm.nih.gov/pubmed/30400917 http://dx.doi.org/10.1186/s12984-018-0444-1 |
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