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EMG-based embedded gesture recognition
Control of active hand prostheses is an open challenge. In fact, the advances in mechatronics made available prosthetic hands with multiple active degrees of freedom; however the predominant control strategies are still not natural for the user, enabling only few gestures, thus not exploiting the prosthesis potential. Pattern recognition and machine learning techniques can be of great help when applied to surface electromyography signals to offer a natural control based on the contraction of muscles corresponding to the real movements. The implementation of such approach for an active prosthetic system offers many challenges related to the reliability of data collected to train the classification algorithm. This project focuses on these challenges invetigating on an implementation suitable for an embedded system, based on Support Vector Machine (SVM). The work falls within a cooperation with INAIL, Prosthetic center in Vigorso (Budrio, BO, Italy), one of the main center in Europe.