Abstract
In order to obtain high accuracy recognition of hand motions using surface electromyogram (SEMG), individual differences of SEMG are important issue. To solve this problem, we propose a channel selection method of the suitable measurement channels for the recognition of motions. We use a 96-channel matrix-type (6×16) surface electrode attached to the forearm in order to measure the SEMG generated from many active muscles during hand motions. From those 96 electrodes, our system decided the number of measurement channels and the position of measurement channels. This can be achieved by using the Monte Carlo method. Eignt normal subjects were experimentally tested using our system. We were able to distinguish all the motions (18 hand motions, including 10 finger movements), and the average recognition rate in the real-time experiment was measured to be greater than 95%. And the number of selected channels ranged from 4 to 7.
