Acquisition and analysis of EMG signals to recognize multiple hand movements for prosthetic applications

Authors:
Giuseppina Gini;Matteo Arvetti;Ian Somlai;Michele Folgheraiter
Affiliations:
Department of Electronics and Information of Politecnico di Milano, Milan, Italy;Department of Electronics and Information of Politecnico di Milano, Milan, Italy;Department of Micro-technology and Medical Device Technology, Technical University of Munich, Munich, Germany;DFKI, Bremen, Germany
Venue:
Applied Bionics and Biomechanics - Human-Robot Interaction/Interface
Year:
2012

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Abstract

One of the main problems in developing active prosthesis is how to control them in a natural way. In order to increase the effectiveness of hand prostheses there is a need in better exploiting electromyography EMG signals. After an analysis of the movements necessary for grasping, we individuated five movements for the wrist-hand mobility. Then we designed the basic electronics and software for the acquisition and the analysis of the EMG signals. We built a small size electronic device capable of registering them that can be integrated into a hand prosthesis. Among all the numerous muscles that move the fingers, we have chosen the ones in the forearm and positioned only two electrodes. To recognize the operation, we developed a classification system, using a novel integration of Artificial Neural Networks ANN and wavelet features.