Learning to assess the quality of stroke rehabilitation exercises

• Main Authors: LEE, Min Hun, SIEWIOREK, Daniel P., SMAILAGIC, Asim, BERNARDINO, Alexandre, BADIA, Sergi Bermúdez i
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: intelligent agent; motion analysis; stroke rehabilitation; Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/6881; https://ink.library.smu.edu.sg/context/sis_research/article/7884/viewcontent/Learning_to_Assess_the_Quality_of_Stroke_Rehabilitation_Exercises.pdf
• Institution: Singapore Management University

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.