Lucrezia Troiani
Machine Learning Approaches for Detecting and Assessing Compensatory Movements in Robot-Assisted Stroke Upper-limb Rehabilitation - PostureCheck.
Rel. Danilo Demarchi, Paolo Bonato, Giulia Corniani. Politecnico di Torino, Master of science program in Biomedical Engineering, 2025
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- Thesis
Restricted to: Only staff users fino al 4 December 2028 (data di embargo). Licence: Creative Commons Attribution Non-commercial No Derivatives. Download (6MB) |
Abstract
Stroke is one of the leading causes of motor disability worldwide, especially regarding upper limb impairments. As stroke incidence continues to rise and the shortage of physical therapists (PTs) able to assist individuals with disabilities worsens, the burden on healthcare systems is projected to intensify significantly. In this context, robotic technologies for stroke rehabilitation have gained increasing attention as effective tools to enhance patients’ recovery. However, even with robotic assistance, PT supervision remains essential to ensure patients perform exercises safely and correctly. To alleviate the supervision burden, robotic group therapy has emerged as a promising option. This highlights the need for tools that can support therapist supervision during group sessions.
One such tool, developed as part of this thesis, is PostureCheck
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