Towards classification of low-level finger movements using forearm muscle activation: A comparative study based on ICA and Fractal theory

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

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

There are number of possible rehabilitation applications of surface Electromyogram (sEMG) that are currently unreliable, when the level of muscle contraction is low. This paper has experimentally analysed the features of forearm sEMG based on Independent Component Analysis (ICA) and Fractal Dimension (FD) for identification of low-level finger movements. To reduce inter-experimental variations, the normalised feature values were used as the training and testing vectors to artificial neural network. The identification accuracy using raw sEMG and FD of sEMG was 51% and 58%, respectively. The accuracy increased to 96% when the signals are separated to their independent components using ICA.

Original languageEnglish
Pages (from-to)150-162
Number of pages13
JournalInternational Journal of Biomedical Engineering and Technology
Volume6
Issue number2
DOIs
Publication statusPublished - Jul 2011
Externally publishedYes

Keywords

  • Blind source separation
  • BSS
  • FD
  • Fractal dimension
  • ICA
  • Independent component analysis
  • Low-level muscle activities
  • SEMG
  • Source separation
  • Surface electromyogram

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