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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Vydané v:Medical engineering & physics Ročník 20; číslo 3; s. 211 - 215
Hlavní autori: Micera, Silvestro, Sabatini, Angelo M., Dario, Paolo
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Elsevier Ltd 01.04.1998
Elsevier Science
Predmet:
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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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1350-4533
1873-4030
DOI:10.1016/S1350-4533(98)00017-4