An algorithm for detecting the onset of muscle contraction by EMG signal processing

The Generalized Likelihood Ratio (GLR) test is proposed for estimating the time instant of muscle contraction onset via electromyographic (EMG) signal processing. In contrast to commonly used threshold-based estimation methods, the proposed algorithm proves to be reasonably accurate even for low lev...

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Published in:	Medical engineering & physics Vol. 20; no. 3; pp. 211 - 215
Main Authors:	Micera, Silvestro, Sabatini, Angelo M., Dario, Paolo
Format:	Journal Article
Language:	English
Published:	Oxford Elsevier Ltd 01.04.1998 Elsevier Science
Subjects:	Algorithms Biological and medical sciences Computer Simulation Control of assistive devices Electrodiagnosis. Electric activity recording Electromyography EMG signal Humans Investigative techniques, diagnostic techniques (general aspects) Likelihood Functions Male Medical sciences Miscellaneous. Technology Muscle Contraction - physiology Signal Processing, Computer-Assisted Statistical signal processing EMG signal Statistical signal processing Control of assistive devices Human Electrodiagnosis Onset time Computer simulation Threshold detection Likelihood ratio Signal processing Electromyography Algorithm Muscle contraction Comparative study
ISSN:	1350-4533, 1873-4030
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Abstract	The Generalized Likelihood Ratio (GLR) test is proposed for estimating the time instant of muscle contraction onset via electromyographic (EMG) signal processing. In contrast to commonly used threshold-based estimation methods, the proposed algorithm proves to be reasonably accurate even for low levels of EMG activity; the improved behaviour of the GLR test comes with just a modest increase in the computational complexity. A set of computer simulation experiments are presented, where the proposed algorithm and two different threshold-based estimators are studied; also, we discuss the results of analysing the EMG recordings of selected proximal muscles of the upper limb in a hemiparetic subject.
AbstractList	The Generalized Likelihood Ratio (GLR) test is proposed for estimating the time instant of muscle contraction onset via electromyographic (EMG) signal processing. In contrast to commonly used threshold-based estimation methods, the proposed algorithm proves to be reasonably accurate even for low levels of EMG activity; the improved behaviour of the GLR test comes with just a modest increase in the computational complexity. A set of computer simulation experiments are presented, where the proposed algorithm and two different threshold-based estimators are studied; also, we discuss the results of analysing the EMG recordings of selected proximal muscles of the upper limb in a hemiparetic subject. The Generalized Likelihood Ratio (GLR) test is proposed for estimating the time instant of muscle contraction onset via electromyographic (EMG) signal processing. In contrast to commonly used threshold-based estimation methods, the proposed algorithm proves to be reasonably accurate even for low levels of EMG activity; the improved behaviour of the GLR test comes with just a modest increase in the computational complexity. A set of computer simulation experiments are presented, where the proposed algorithm and two different threshold-based estimators are studied; also, we discuss the results of analysing the EMG recordings of selected proximal muscles of the upper limb in a hemiparetic subject.The Generalized Likelihood Ratio (GLR) test is proposed for estimating the time instant of muscle contraction onset via electromyographic (EMG) signal processing. In contrast to commonly used threshold-based estimation methods, the proposed algorithm proves to be reasonably accurate even for low levels of EMG activity; the improved behaviour of the GLR test comes with just a modest increase in the computational complexity. A set of computer simulation experiments are presented, where the proposed algorithm and two different threshold-based estimators are studied; also, we discuss the results of analysing the EMG recordings of selected proximal muscles of the upper limb in a hemiparetic subject.
Author	Dario, Paolo Sabatini, Angelo M. Micera, Silvestro
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Keywords	EMG signal Statistical signal processing Control of assistive devices Human Electrodiagnosis Onset time Computer simulation Threshold detection Likelihood ratio Signal processing Electromyography Algorithm Muscle contraction Comparative study
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SubjectTerms	Algorithms Biological and medical sciences Computer Simulation Control of assistive devices Electrodiagnosis. Electric activity recording Electromyography EMG signal Humans Investigative techniques, diagnostic techniques (general aspects) Likelihood Functions Male Medical sciences Miscellaneous. Technology Muscle Contraction - physiology Signal Processing, Computer-Assisted Statistical signal processing
Title	An algorithm for detecting the onset of muscle contraction by EMG signal processing
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