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The electromyography (EMG) signal is the electrical manifestation of aneuromuscular activation that provides access to physiological processes whichcause the muscle to generate force and produce movement. Non invasiveprostheses use such signals detected by the electrodes placed on the user'sstump, as input to generate hand posture movements according to the intentionsof the prosthesis wearer. The aim of this pilot study is to explore therepeatability issue, i.e. the ability to classify 17 different hand postures,represented by EMG signal, across a time span of days by a control algorithm.Data collection experiments lasted four days and signals were collected fromthe forearm of a single subject. We find that Support Vector Machine (SVM)classification results are high enough to guarantee a correct classification ofmore than 10 postures in each moment of the considered time span.

Studying the control of non invasive prosthetic hands over large time spans