Stroke is one of the leading causes of disability worldwide with a disproportionately high burden in low and middle-income countries. In such countries, limited access to rehabilitation centres, shortage of trained physiotherapists and socioeconomic constraints impede continuous post-stroke care resulting in poorer recovery. Telerehabilitation has emerged as a scalable and cost-effective solution enabling remote monitoring and therapy personalization. Robust vision-based rehabilitation exercise assessment systems can play a significant role in such telerehabilitation programs. This article presents a well-annotated, exercise-specific video dataset consisting of 491 videos representing four distinct upper limb muscular strengthening exercises recommended for home rehabilitation performed by ten volunteers with the data captured using conventional RGB cameras at 30 frames per second under different background and lighting conditions. This dataset can be used to develop deep learning based low-cost telerehabilitation systems enabling availability of better post-stroke care for the patients from economically disadvantaged backgrounds.
S et al. (2026) studied this question.