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Gesture Based Control and EMG DecompositionThis paper presents two probabilistic developments for use with Electromyograms (EMG). First described is a new-electric interface for virtual device control based on gesture recognition. The second development is a Bayesian method for decomposing EMG into individual motor unit action potentials. This more complex technique will then allow for higher resolution in separating muscle groups for gesture recognition. All examples presented rely upon sampling EMG data from a subject's forearm. The gesture based recognition uses pattern recognition software that has been trained to identify gestures from among a given set of gestures. The pattern recognition software consists of hidden Markov models which are used to recognize the gestures as they are being performed in real-time from moving averages of EMG. Two experiments were conducted to examine the feasibility of this interface technology. The first replicated a virtual joystick interface, and the second replicated a keyboard. Moving averages of EMG do not provide easy distinction between fine muscle groups. To better distinguish between different fine motor skill muscle groups we present a Bayesian algorithm to separate surface EMG into representative motor unit action potentials. The algorithm is based upon differential Variable Component Analysis (dVCA) [l], [2] which was originally developed for Electroencephalograms. The algorithm uses a simple forward model representing a mixture of motor unit action potentials as seen across multiple channels. The parameters of this model are iteratively optimized for each component. Results are presented on both synthetic and experimental EMG data. The synthetic case has additive white noise and is compared with known components. The experimental EMG data was obtained using a custom linear electrode array designed for this study.
Document ID
20060015681
Acquisition Source
Ames Research Center
Document Type
Reprint (Version printed in journal)
Authors
Wheeler, Kevin R.
(NASA Ames Research Center Moffett Field, CA, United States)
Chang, Mindy H.
(Stanford Univ. Stanford, CA, United States)
Knuth, Kevin H.
(Albany Univ. Albany, NY, United States)
Date Acquired
August 23, 2013
Publication Date
November 1, 2005
Publication Information
Publication: IEEE Transactions on Systems, Man, and Cybernetics
Volume: 1
Issue: 11
Subject Category
Cybernetics, Artificial Intelligence And Robotics
Distribution Limits
Public
Copyright
Public Use Permitted.
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