Conventional research on motion recognition using surface electromyogram (SEMG) is mainly focused on how to process with the signals for pattern recognition. However, it is of much consequence to the motion recognition that measurement channels position including useful information about SEMG pattern recognition is selected. In this paper, we present two topics for the hand motion recognition system based on SEMG. First described is the method to select the suitable measurement channels position of multichannel SEMG for the recognition of hand motion, and the second described is an applied systems based on our proposed method. About channel selection, we use a multichannel matrix-type surface electrode attached to the forearm in order to measure the SEMG generated from many active muscles during hand motions. From those electrodes, system decided the number of measurement channels and the position of measurement channels. This can be achieved by using the Monte Carlo method. The recognition experiments of 18 hand motions show that the average rate was measured to be greater than 96%. And the number of selected channels ranged from 4 to 7. About applied systems, our developed system works as an input interface for the computer (keyboard and pointing device) and a robot hand.

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In this paper, we describe the human-interface equipment using surface electromyogram (SEMG) based on optimal measurement channels for each subject. In case the SEMG is used as a control signal, individual differences of SEMG are important issue to obtain high accuracy recognition of motions. 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 x 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. Our system generates 10,000 sets of randomly selected channels, and these sets are evaluated by the recognition rate of hand motions. One set that records a highest recognition rate is selected from 10,000 sets for an optimal set of measurement channels. And the one set with the smallest number of measurement channels which fulfil the recognition rate above 90% or the maximum recognition rate above 95% is used for real-time recognition. Six normal subjects were experimentally tested using our system. The recognition rates of 18 hand motions, including 10 finger movements, were assessed for every subject. We were able to distinguish all the motions, 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.

SEMG (surface EMG) has many benefits, for example measuring SEMG is easy and a characteristic pattern of SEMG is obtained for each different movement. Therefore, SEMG that is generated by body movement is able to use as a control signal for some electric powered equipments. Our objective is the perfect control of the computer by using SEMG that is generated from forearms. In this paper, we will talk about our developed interface system that works as a keyboard of the computer.

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