Document Type
Article
Language
eng
Publication Date
11-6-2018
Publisher
BioMed Central
Source Publication
Journal of NeuroEngineering and Rehabilitation
Source ISSN
1743-0003
Abstract
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.
Recommended Citation
Rammer, Jacob; Slavens, Brooke A.; Krzak, Joseph; Winters, Jack M.; Riedel, Susan A.; and Harris, Gerald F., "Assessment of a Markerless Motion Analysis System for Manual Wheelchair Application" (2018). Biomedical Engineering Faculty Research and Publications. 594.
https://epublications.marquette.edu/bioengin_fac/594
ADA accessible version
Comments
Published version. Journal of NeuroEngineering and Rehabilitation, Vol. 15, No. 96 (2018). DOI. © 2018 The Authors. Used with permission.
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