DocumentCode :
1340117
Title :
Adaptive whitening of the electromyogram to improve amplitude estimation
Author :
Clancy, Edward A. ; Farry, Kristin A.
Author_Institution :
Raytheon Co., Framingham, MA, USA
Volume :
47
Issue :
6
fYear :
2000
fDate :
6/1/2000 12:00:00 AM
Firstpage :
709
Lastpage :
719
Abstract :
Previous research showed that whitening the surface electromyogram (EMG) can improve EMG amplitude estimation (where EMG amplitude is defined as the time-varying standard deviation of the EMG). However, conventional whitening via a linear filter seems to fail at low EMG amplitude levels, perhaps due to additive background noise in the measured EMG. This paper describes an adaptive whitening technique that overcomes this problem by cascading a nonadaptive whitening filter, an adaptive Wiener filter, and an adaptive gain correction. These stages can be calibrated from two, five second duration, constant-angle, constant-force contractions, one at a reference level [e.g., 50% maximum voluntary contraction (MVC)] and one at 0% MVC. In experimental studies, subjects used real-time EMG amplitude estimates to track a uniform-density, band-limited random target. With a 0.25-Hz bandwidth target, either adaptive whitening or multiple channel processing reduced the tracking error roughly half-way to the error achieved using the dynamometer signal as the feedback. At the 1.00-Hz bandwidth, all of the EMG processors had errors equivalent to that of the dynamometer signal, reflecting that errors in this task were dominated by subjects´ inability to track targets at this bandwidth. Increases in the additive noise level, smoothing window length, and tracking bandwidth diminish the advantages of whitening.
Keywords :
adaptive signal processing; amplitude estimation; electromyography; medical signal processing; physiological models; spectral analysis; 0.25 Hz; 1.00 Hz; 5 s; EMG adaptive whitening; EMG processors; adaptive Wiener filter; amplitude estimation improvement; dynamometer signal; maximum voluntary contraction; multiple channel processing; real-time EMG amplitude estimates; smoothing window length; tracking bandwidth; tracking error reduction; Additive noise; Amplitude estimation; Background noise; Bandwidth; Electromyography; Noise measurement; Nonlinear filters; Signal processing; Target tracking; Wiener filter; Adaptation, Physiological; Adult; Aged; Algorithms; Artifacts; Electromyography; Female; Humans; Male; Middle Aged; Models, Biological; Muscle Contraction; Muscles;
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9294
Type :
jour
DOI :
10.1109/10.844217
Filename :
844217
Link To Document :
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