7th International Conference on Pervasive Computing Technologies for Healthcare

Research Article

MyWalk: A Mobile App for Gait Asymmetry Rehabilitation in the Community

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  • @INPROCEEDINGS{10.4108/icst.pervasivehealth.2013.252118,
    author={Tuck-Voon How and Justin Chee and Eric Wan and Alex Mihailidis},
    title={MyWalk: A Mobile App for Gait Asymmetry Rehabilitation in the Community},
    proceedings={7th International Conference on Pervasive Computing Technologies for Healthcare},
    publisher={IEEE},
    proceedings_a={PERVASIVEHEALTH},
    year={2013},
    month={5},
    keywords={biofeedback contextual rehabilitation technologies gait outpatient rehabilitation smartphones stroke step-time asymmetry},
    doi={10.4108/icst.pervasivehealth.2013.252118}
}
  • Tuck-Voon How
    Justin Chee
    Eric Wan
    Alex Mihailidis
    Year: 2013
    MyWalk: A Mobile App for Gait Asymmetry Rehabilitation in the Community
    PERVASIVEHEALTH
    ICST
    DOI: 10.4108/icst.pervasivehealth.2013.252118
Tuck-Voon How1,*, Justin Chee2, Eric Wan3, Alex Mihailidis4
  • 1: Institute of Biomaterials & Biomedical Engineering, University of Toronto
  • 2: Graduate Department of Rehabilitation Science, University of Toronto
  • 3: Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto
  • 4: Department of Occupational Science and Occupational Therapy, University of Toronto
*Contact email: tuckvoon.how@mail.utoronto.ca

Abstract

MyWalk is a mobile app to enable gait rehabilitation in the community. Particularly, it can be used to treat and assess step-time asymmetry (STA) - which occurs when the amount of time between consecutive heel strikes is uneven. Temporal gait asymmetry, of which STA is a form, is common post-stroke and could result in difficulties such as joint degeneration or musculoskeletal pain. By enabling STA rehabilitation on a smartphone, post-stroke patients can now improve and assess their STA within the context of their everyday environments. Initial validation of MyWalk's algorithm showed minimal error (RMSE 2.20-2.67%) versus a foot-switch ground truth in detecting STA for symmetric walking conditions, but larger error (RMSE 13.82-16.34%) in asymmetric walking conditions. Future work will be to improve the accuracy of MyWalk's STA algorithm.

Keywords
biofeedback contextual rehabilitation technologies gait outpatient rehabilitation smartphones stroke step-time asymmetry
Published
2013-05-23
Publisher
IEEE
http://dx.doi.org/10.4108/icst.pervasivehealth.2013.252118
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