Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams-A feasibility study.

ABSTRACT: As societies age, the number of individuals experiencing stroke increases, necessitating more effective rehabilitation strategies. Over half of stroke survivors suffer from upper limb impairments, making assessments of sensory-motor function crucial for both improving interventions and tracking progress. Ideally, such assessments could also be performed at home without requiring a therapist's presence. Advances in computer vision and human pose estimation allow for human movement analysis using consumer-grade cameras. This study investigates whether a single webcam, combined with human pose estimation and deep learning algorithms, can automatically detect compensatory movements in persons with stroke performing a drinking task. Twenty participants with stroke with mild to moderate upper li

SUBMITTER: Unger T 

PROVIDER: S-EPMC12660197 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature