EMG pattern classification using SOFMs for hand signal recognition |
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| Authors: | K. H. Eom Y. J. Choi H. Sirisena |
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| Affiliation: | (1) Department of Electronic Engineering, Dongguk University, 26-3Ga Pildong Chung-gu Seoul, Korea e-mail: kihwanum@dgu.ac.kr, KR;(2) Department of Electrical and Electronic Engineering, University of Canterbury, Christchurch 8020, New Zealand e-mail: h.sirisena@elec.canterbury.ac.nz, NZ |
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| Abstract: | We propose a method of pattern classification of electromyographic (EMG) signals using a set of self- organizing feature maps (SOFMs). The proposed method is simple to apply in that the EMG signals are directly input to the SOFMs without preprocessing. Experimental results are presented that show the effectiveness of the SOFM based classifier for the recognition of the hand signal version of the Korean alphabet from EMG signal patterns. |
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| Keywords: | Electromyographic (EMG), Self-organizing feature map (SOFM), Pattern classification, Hand signal recognition |
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