  Surface electromyography is a valid tool to gather muscular contraction
signals from intact and amputated subjects. Electromyographic signals can be
used to control prosthetic devices in a noninvasive way distinguishing the
movements performed by the particular EMG electrodes activity. According to the
literature, several algorithms have been used to control prosthetic hands
through s-EMG signals. The main issue is to correctly classify the signals
acquired as the movement actually performed. This work presents a study on the
Support Vector Machine's performance in a short-time period, gained using two
different feature representation (Mean Absolute Value and Waveform Length) of
the sEMG signals. In particular, we paid close attention to the repeatability
problem, that is the capability to achieve a stable and satisfactory level of
accuracy in repeated experiments. Results on a limited setting are encouraging,
as they show an average accuracy above 73% even in the worst case scenario.
