Continuous myoelectric control for powered prostheses using hidden Markov models

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...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2005-01, Vol.52 (1), p.121-124
Main Authors: Chan, A.D.C., Englehart, K.B.
Format: Article
Language:English
Subjects:
Action Potentials
Algorithms
Biomedical electrodes
Biomedical engineering
Classification
Diagnosis, Computer-Assisted - methods
electromyography
Electromyography - methods
Extremities
hidden Markov model
Hidden Markov models
Humans
Markov Chains
Models, Neurological
Models, Statistical
Muscle, Skeletal - physiopathology
myoelectric signals
Nonhomogeneous media
Pattern recognition
Pattern Recognition, Automated - methods
prostheses
Prostheses and Implants
Prosthesis Design
Prosthetics
Reproducibility of Results
Sensitivity and Specificity
Signal processing
Stochastic processes
Wrist
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Summary: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.
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2004.836492