Predicting Robot-Based Tracking Performance After Stroke Using Lesion Features and Clinical Assessments: A Pilot Study

Background: Following stroke, motor and cognitive impairments often limit functional independence. Personalized rehabilitation requires comprehensive clinical assessments, which are costly and time-consuming. Imaging scans are routinely collected, but their predictive value in determining post-stroke function remains unclear. Objectives: To determine the ability of clinical assessments, lesion features with medical chart information, and a combined clinical and biomedical model to predict kinematic outcomes from a robot-based tracking task in chronic stroke individuals. Methods: Eighteen parti

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