ABSTRACT Stroke is a leading cause of long‐term disability, with 80% of survivors experiencing acute upper‐limb impairment. Although vision‐based and wearable sensor technologies have the potential to improve rehabilitation, a thorough analysis of their comparative advantages, technical limitations and clinical readiness is still lacking. This systematic review provides a methodologically rigorous analysis of the peer‐reviewed literature from 2005 to 2025, synthesising and critically evaluating vision‐based and wearable sensor technologies for post‐stroke hand rehabilitation. Following PRISMA guidelines, we searched PubMed, Scopus and Web of Science. We analysed 132 included studies to identify a trend towards deep learning‐based computer vision and hybrid wearable systems. However, quantitative synthesis exposed critical gaps: technical benchmarks (e.g., latency and computational cost) were reported in fewer than 5% of studies, and the median sample size was only 17 participants. Methodological quality was low to moderate, with only 12% of studies being randomised controlled trials. We present a new taxonomy classifying systems by sensing modality and maturity, which reveals a lab‐to‐clinic gap. Although innovation is rapid, a lack of standardised benchmarking hinders clinical translation. We propose a decision‐making framework to guide future research and implementation.
Kamal et al. (2026) studied this question.