Development of the human interface equipment based on surface EMG employing channel selection method.
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.
Nagata
Kentaro
K
Dept. of Electr. & Electron. Eng., Tokai Univ., Kanagawa, Japan. nagata@lachesis.ep.u-tokai.ac.jp
Ando
Keiichi
K
Nakano
Shinji
S
Nakajima
Hideaki
H
Yamada
Masafumi
M
Magatani
Kazushige
K
D000465:Algorithms; D003198:Computer Simulation; D004566:Electrodes; D004576:Electromyography / Q000295:instrumentation* / Q000379:methods*; D004867:Equipment Design; D006801:Humans; D008962:Models, Theoretical; D009010:Monte Carlo Method; D009068:Movement; D009119:Muscle Contraction; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors