Pattern classification of Myo-Electrical signal during different maximum voluntary contractions: A study using BSS techniques

Ganesh R. Naik, Dinesh K. Kumar, Sridhar P. Arjunan

Research output: Contribution to journalReview articlepeer-review

29 Citations (Scopus)

Abstract

The presence of noise and cross-talk from closely located and simultaneously active muscles is exaggerated when the level of muscle contraction is very low. Due to this the current applications of surface electromyogram (sEMG) are infeasible and unreliable in pattern classification. This research reports a new technique of sEMG using Independent Component Analysis (ICA). The technique uses blind source separation (BSS) methods to classify the patterns of Myo-electrical signals during different Maximum Voluntary Contraction (MVCs) at different low level finger movements. The results of the experiments indicate that patterns using ICA of sEMG is a reliable (p<0.001) measure of strength of muscle contraction even when muscle activity is only 20% MVC. The authors propose that ICA is a useful indicator of muscle properties and is a useful indicator of the level of muscle activity.

Original languageEnglish
Pages (from-to)1-6
Number of pages6
JournalMeasurement Science Review
Volume10
Issue number1
DOIs
Publication statusPublished - 15 Feb 2010
Externally publishedYes

Bibliographical note

The non-commercial use of the article will be governed by the Creative Commons Attribution-NonCommercialNoDerivs

Keywords

  • Blind source separation
  • Gestures
  • Independent component Analysis
  • Maximum Voluntary Contractions
  • Motor Unit Action Potential
  • RMS
  • Surface Electromyography

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