Giuseppe Parisi
Assessment of the performance of regression-based machine learning techniques for offline translation of EMG signal to hand kinematics.
Rel. Taian Martins, Giovanni Rolandino. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2023
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Abstract
Introduction: The loss of upper limb functionality is a prevalent issue, impacting millions and showing an increasing trend over time. This condition significantly affects the quality of life of individuals, necessitating urgent solutions. Despite substantial progress in the field, current prosthetic control solutions have limitations, are cumbersome, and are difficult to use, leading to a high rejection rate of prosthetic devices, a well-recognized indicator of patient satisfaction. Additionally, there is a marked disparity between commercial and academic solutions: commercial devices often lack dexterity and use unnatural, non-intuitive control strategies, whereas academic devices typically depend on complex and time-consuming algorithms, challenging real-time applications.
Addressing these issues requires a new control system capable of functioning in near real-time across multiple Degrees of Freedom (DoFs), and controlling them proportionally, simultaneously, independently, and continuously
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