Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
3-2021
Abstract
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.
Keywords
intelligent agent, motion analysis, stroke rehabilitation
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems
Research Areas
Data Science and Engineering; Intelligent Systems and Optimization
Publication
24th International Conference on Intelligent User Interfaces. IUI ’19.
ISBN
9781450362726
Identifier
10.1145/3301275.3302273
Publisher
Association for Computing Machinery
City or Country
Marina del Ray, California
Citation
LEE, Min Hun; SIEWIOREK, Daniel P.; SMAILAGIC, Asim; BERNARDINO, Alexandre; and BADIA, Sergi Bermúdez i.
Learning to assess the quality of stroke rehabilitation exercises. (2021). 24th International Conference on Intelligent User Interfaces. IUI ’19..
Available at: https://ink.library.smu.edu.sg/sis_research/6881
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
