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ABSTRACT: Background
Compensatory movements frequently emerge in the process of motor recovery after a stroke. Given their potential for unfavorable long-term effects, it is crucial to assess and document compensatory movements throughout rehabilitation. However, clinically applicable assessment tools are currently limited. Deep learning methods have shown promising potential for assessing movement quality and addressing this gap. A crucial prerequisite for developing an accurate measurement tool is ensuring reliability in assessing compensatory movements, which is essential for establishing a valid ground truth.Objective
The study aimed to assess inter- and intra-rater reliability of occupational and physical therapists' visual assessment of compensatory movements based on video ana
SUBMITTER: Sauerzopf L
PROVIDER: S-EPMC11660698 | biostudies-literature | 2024 Dec
REPOSITORIES: biostudies-literature