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Hybrid hidden Markov model-neural network system for EMG signals recognition

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3 Author(s)
Jangwoo Kwon ; Dept. of Electron. Eng., Inha Univ., Inchon, South Korea ; Hongki Min ; Seunghong Hong

Describes an approach for classifying electromyographic (EMG) signals using a multilayer perceptrons (MLPs) and hidden Markov models (HMMs) hybrid classifier. Instead of using MLP's as probability generators for HMMs the authors propose to use MLPs as the second classifiers to increase discrimination rates of myoelectric patterns. This strategy is proposed to overcome weak discrimination and to consider dynamic properties of EMG signals. Two discrimination strategies (HMM, and HMM with three subnet MLPs) for discriminating signals representative of 6 primitive class of motions are described and compared. The proposed strategy increase the discrimination results considerably. Results are presented to support this approach

Published in:

Engineering in Medicine and Biology Society, 1996. Bridging Disciplines for Biomedicine. Proceedings of the 18th Annual International Conference of the IEEE  (Volume:4 )

Date of Conference:

31 Oct-3 Nov 1996