DocumentCode :
1408604
Title :
Investigation of the Hammerstein hypothesis in the modeling of electrically stimulated muscle
Author :
Hunt, Kenneth J. ; Munih, Marko ; Donaldson, N.de.N. ; Barr, Fiona M D
Author_Institution :
Dept. of Mech. Eng., Glasgow Univ., UK
Volume :
45
Issue :
8
fYear :
1998
Firstpage :
998
Lastpage :
1009
Abstract :
To restore functional use of paralyzed muscles by automatically controlled stimulation, an accurate quantitative model of the stimulated muscles is desirable. The most commonly used model for isometric muscle has had a Hammerstein structure, in which a linear dynamic block is preceded by a static nonlinear function. To investigate the accuracy of the Hammerstein model, the responses to a pseudo-random binary sequence (PRES) excitation of normal human plantarflexors, stimulated with surface electrodes, were used to identify a Hammerstein model but also four local models which describe the responses to small signals at different mean levels of activation. Comparison of the local models with the linearized Hammerstein model showed that the Hammerstein model concealed a fivefold variation in the speed of response. Also, the small-signal gain of the Hammerstein model was in error by factors up to three. We conclude that, despite the past widespread use of the Hammerstein model, it is not an accurate representation of isometric muscle. On the other hand, local models, which are more accurate predictors, can be identified from the responses to short PRES sequences. The utility of local models for controller design is discussed.
Keywords :
bioelectric phenomena; biological effects of fields; muscle; neurophysiology; patient treatment; physiological models; Hammerstein hypothesis; Hammerstein structure; accurate quantitative model; automatically controlled stimulation; controller design; electrically stimulated muscle; functional use; isometric muscle; linear dynamic block; linearized Hammerstein model; local models; modeling; normal human plantarflexors; paralyzed muscles; pseudo-random binary sequence excitation; small signals; small-signal gain; static nonlinear function; surface electrodes; Automatic control; Binary sequences; Biological system modeling; Electrodes; Humans; Muscles; Nerve fibers; Nonlinear dynamical systems; Parameter estimation; Recruitment; Adult; Algorithms; Ankle Joint; Computer Simulation; Electric Stimulation; Fourier Analysis; Humans; Isometric Contraction; Linear Models; Male; Models, Biological; Nonlinear Dynamics; Reference Values; Regression Analysis;
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9294
Type :
jour
DOI :
10.1109/10.704868
Filename :
704868
Link To Document :
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