Investigating the feasibility and acceptability of real-time visual feedback in reducing compensatory motions during self-administered stroke rehabilitation exercises: A pilot study with chronic stroke survivors

Author:

Lin Shayne1ORCID,Mann Jotvarinder23,Mansfield Avril245,Wang Rosalie H26,Harris Jocelyn E7,Taati Babak289

Affiliation:

1. Division of Engineering Science, University of Toronto, Toronto, Canada

2. Toronto Rehabilitation Institute, University Health Network, Toronto, Canada

3. Department of Kinesiology, University of Waterloo, Waterloo, Canada

4. Department of Physical Therapy, University of Toronto, Toronto, Canada

5. Evaluative Clinical Sciences, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Canada

6. Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, Canada

7. School of Rehabilitation Sciences, McMaster University, Hamilton, Canada

8. Department of Computer Science, University of Toronto, Toronto, Canada

9. Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada

Abstract

Introduction Homework-based rehabilitation programs can help stroke survivors restore upper extremity function. However, compensatory motions can develop without therapist supervision, leading to sub-optimal recovery. We developed a visual feedback system using a live video feed or an avatar reflecting users' movements so users are aware of compensations. This pilot study aimed to evaluate validity (how well the avatar characterizes different types of compensations) and acceptability of the system. Methods Ten participants with chronic stroke performed upper-extremity exercises under three feedback conditions: none, video, and avatar. Validity was evaluated by comparing agreement on compensations annotated using video and avatar images. A usability survey was administered to participants after the experiment to obtain information on acceptability. Results There was substantial agreement between video and avatar images for shoulder elevation and hip extension (Cohen's κ: 0.6–0.8) and almost perfect agreement for trunk rotation and flexion (κ: 0.80–1). Acceptability was low due to lack of corrective prompts and occasional noise with the avatar display. Most participants suggested that an automatic compensation detection feature with visual and auditory cuing would improve the system. Conclusion The avatar characterized four types of compensations well. Future work will involve increasing sensitivity for shoulder elevation and implementing a method to detect compensations.

Funder

Ontario Innovation Trust

Canadian Institutes of Health Research

Stiftelsen Promobilia

Canada Foundation for Innovation

Ministry of Research and Innovation

Publisher

SAGE Publications

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