If we had protocols instead of guidelines then patients would be motivated to continue their exercises because they know a certain result will occur.
Learning to assess the quality of stroke rehabilitation exercises
| Published in: |
| · Proceeding |
| IUI '19 Proceedings of the 24th International Conference on Intelligent User Interfaces |
| Pages 218-228
Marina del Ray, California — March 17 - 20, 2019 ACM New York, NY, USA ©2019 table of contents ISBN: 978-1-4503-6272-6 doi>10.1145/3301275.3302273 |
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2019 Article
Due to the limited number of therapists, task-oriented exercises are
often prescribed for post-stroke survivors as in-home rehabilitation.
During in-home rehabilitation, a patient may become unmotivated or
confused to comply prescriptions without the feedback of a therapist. To
address this challenge, this paper proposes an automated method that
can achieve not only qualitative, but also quantitative assessment of
stroke rehabilitation exercises. Specifically, we explored a threshold
model that utilizes the outputs of binary classifiers to quantify the
correctness of a movements into a performance score. We collected
movements of 11 healthy subjects and 15 post-stroke survivors using a
Kinect sensor and ground truth scores from primary and secondary
therapists. The proposed method achieves the following agreement with
the primary therapist: 0.8436, 0.8264, and 0.7976 F1-scores on three
task-oriented exercises. Experimental results show that our approach
performs equally well or better than multi-class classification,
regression, or the evaluation of the secondary therapist. Furthermore,
we found a strong correlation (R2 = 0.95) between the sum of
computed exercise scores and the Fugl-Meyer Assessment scores,
clinically validated motor impairment index of post-stroke survivors.
Our results demonstrate a feasibility of automatically assessing stroke
rehabilitation exercises with the decent agreement levels and clinical
relevance.
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