Document Detail


Adaptive whitening of the electromyogram to improve amplitude estimation.
MedLine Citation:
PMID:  10833845     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
E A Clancy; K A Farry
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Publication Detail:
Type:  Comparative Study; Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  IEEE transactions on bio-medical engineering     Volume:  47     ISSN:  0018-9294     ISO Abbreviation:  IEEE Trans Biomed Eng     Publication Date:  2000 Jun 
Date Detail:
Created Date:  2000-06-15     Completed Date:  2000-06-15     Revised Date:  2009-11-11    
Medline Journal Info:
Nlm Unique ID:  0012737     Medline TA:  IEEE Trans Biomed Eng     Country:  UNITED STATES    
Other Details:
Languages:  eng     Pagination:  709-19     Citation Subset:  IM    
Affiliation:
Raytheon Co., Framingham, MA 01701, USA. ted.clancy@alum.wpi.edu
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MeSH Terms
Descriptor/Qualifier:
Adaptation, Physiological*
Adult
Aged
Algorithms
Artifacts
Electromyography / instrumentation,  methods*,  statistics & numerical data
Female
Humans
Male
Middle Aged
Models, Biological
Muscle Contraction / physiology
Muscles / physiology*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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