By Topic

Functional Separation of EMG Signals via ARMA Identification Methods for Prosthesis Control Purposes

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Graupe, D. ; Department of Electrical Engineering, Colorado State University, Fort Collins, Colo. 80521. ; Cline, William K.

Multifunctional control of artificial limbs via electromyographic (EMG) actuation requires means for reliably recognizing or distinguishing between the various functions on the basis of the recorded EMG data. Furthermore, constraints of weight, cost, and computation time on practical prosthesis application must be satisfied. An approach to the aforementioned recognition problem is given in terms of deriving a fast parametric-recognition algorithm whereby the autoregressive-moving-average (ARMA) parameters and the Kalman filter parameters of the EMG time series are identified. It is shown that the resulting identified parameters yield sufficient information to discriminate between a small number of upper extremity functions. Problems involved in practical prosthesis control via the present approach and problems of hardware realization are discussed to illustrate the validity of the approach.

Published in:

Systems, Man and Cybernetics, IEEE Transactions on  (Volume:SMC-5 ,  Issue: 2 )