Muscle activity onset detection using energy detectors

Ghulam Rasoo, Kamran Iqbal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Muscle activity detection is important for clinical investigations leading to the identification of neuromuscular disorders. Myoelectric signal recorded via electrodes placed at skin surface can reveal important muscle excitation information about underlying limb movement. However, a primary difficulty in the detection of muscle activity period from myoelectric signals lies in the inherent variability of these signals and the noise added during the collection process. In the literature, the double threshold detector has been commonly used for detection of the muscle activity periods from myoelectric signals. In this study, we propose a new scheme based on the log-likelihood ratio test to detect muscle activity periods accurately. This scheme uses energy information contained in the myoelectric signal, which increases with the start of the activity. We demonstrate the viability of energy detection scheme via successful detection performed on synthetic as well as clinical myoelectric signals.

Original languageEnglish (US)
Title of host publication2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012
Pages3094-3097
Number of pages4
DOIs
StatePublished - Dec 14 2012
Externally publishedYes
Event34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012 - San Diego, CA, United States
Duration: Aug 28 2012Sep 1 2012

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012
Country/TerritoryUnited States
CitySan Diego, CA
Period8/28/129/1/12

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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