  The electromyography (EMG) signal is the electrical manifestation of a
neuromuscular activation that provides access to physiological processes which
cause the muscle to generate force and produce movement. Non invasive
prostheses use such signals detected by the electrodes placed on the user's
stump, as input to generate hand posture movements according to the intentions
of the prosthesis wearer. The aim of this pilot study is to explore the
repeatability 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 from
the forearm of a single subject. We find that Support Vector Machine (SVM)
classification results are high enough to guarantee a correct classification of
more than 10 postures in each moment of the considered time span.
