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Machine Learning-Driven Vision System for Detecting and Evaluating Compensatory Movements in Robot-Based Stroke Rehabilitation.
Rel. Danilo Demarchi, Paolo Bonato, Giulia Corniani. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2024
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Abstract
A stroke, also known as a cerebrovascular accident (CVA), occurs when blood flow to a part of the brain is interrupted or when a blood vessel in the brain bursts. Stroke remains the second-leading cause of death and the third-leading cause of death and disability combined in the world. Only 12% of stroke survivors achieve complete upper limb functional recovery. Robotic rehabilitation has become a pivotal tool in improving the recovery process for stroke survivors, offering targeted support to enhance motor function. However, many patients develop compensatory movements—unintended patterns that allow them to work around mobility limitations but reinforce incorrect motor behaviors.
This thesis aims to develop a machine learning framework to detect and evaluate compensatory movements in stroke patients using 3D skeletal data extracted from video frames
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