NDLI: Continuous myoelectric control for powered prostheses using hidden Markov models

Content Provider	IEEE Xplore Digital Library
Author	Chan, A.D.C. Englehart, K.B.
Copyright Year	1964
Abstract	This paper represents an ongoing investigation of dexterous and natural control of upper extremity prostheses using the myoelectric signal. The scheme described within uses a hidden Markov model (HMM) to process four channels of myoelectric signal, with the task of discriminating six classes of limb movement. The HMM-based approach is shown to be capable of higher classification accuracy than previous methods based upon multilayer perceptrons. The method does not require segmentation of the myoelectric signal data, allowing a continuous stream of class decisions to be delivered to a prosthetic device. Due to the fact that the classifier learns the muscle activation patterns for each desired class for each individual, a natural control actuation results. The continuous decision stream allows complex sequences of manipulation involving multiple joints to be performed without interruption. The computational complexity of the HMM in its operational mode is low, making it suitable for a real-time implementation. The low computational overhead associated with training the HMM also enables the possibility of adaptive classifier training while in use.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	4
File Size	126540
Starting Page	121
Ending Page	124
File Format	PDF
ISSN	00189294
Volume Number	52
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Prosthetics Hidden Markov models Wrist Signal processing Pattern recognition Nonhomogeneous media Stochastic processes Biomedical engineering Biomedical electrodes Extremities prostheses Classification electromyography hidden Markov model myoelectric signals
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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