Abstract
In recent years, increasing of concern about social welfare, artificial limbs are asked for not only their appearance but also their functions. We developed 'Training Simulator for EMG Prosthetic Hand' using artificial neural network so that it was operated by the EMG of survival muscles. Because the EMG dose not consistent depending on day and muscle conditions, etc., and activities of muscle are synergetic. In this study the EMG data during a short period were gathered through 2 or 4 channels and averaged to improve the recognition rates. We made the neural network learn a stop and three motion patterns with a good recognition rate, about 5%. Human interfaces including control between severed and artificial hands are indispensable to move the artificial hand unconsciously.
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