Accepted for/Published in: JMIR Serious Games
Date Submitted: Jan 29, 2021
Date Accepted: Oct 12, 2021
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Data-driven classification of human movements in virtual reality toward effective telerehabilitation: A Pre-Clinical Study
ABSTRACT
Background:
Human interaction with machines is essential for the success of telerehabilitation programs. Telerehabilitation devices are designed for use by individuals whose behavior is atypical due to motor impairment. In order to implement optimal control strategies for human-machine interactions that are intuitive and safe, the machine must “gain an understanding” of the user's actions.
Objective:
This study seeks to demonstrate the possibility of classifying bimanual movements in telerehabilitation using machine learning, toward automatic assessment of motor performance.
Methods:
Nine healthy individuals interacted with a commercial virtual reality gaming system within an engaging citizen science project. Time series of their movement were recorded by the sensors embedded in the device as they performed scientific tasks. We performed principal component analysis to identify salient features of movements, and then applied a bagged trees ensemble classifier to classify the movements.
Results:
Classification achieved exceptionally high performance, reaching 99.9% accuracy. Among the movements, elbow flexion was most accurately classified (99.2%) and horizontal shoulder abduction to the right side of the body was most misclassified (98.8%).
Conclusions:
Coordinated bimanual movements in virtual reality can be classified with extraordinary high accuracy. Our findings lay the foundation for the development of motion analysis algorithms in virtual reality-mediated telerehabilitation.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.