Use of sEMG in identification of low level muscle activities : features based on ICA and Fractal dimension

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

Research output: Chapter in Book / Conference PaperConference Paperpeer-review

11 Citations (Scopus)

Abstract

![CDATA[This paper has experimentally verified and compared features of sEMG (Surface Electromyogram) such as ICA (Independent Component Analysis) and Fractal Dimension (FD) for identification of low level forearm muscle activities. The fractal dimension was used as a feature as reported in the literature. The normalized feature values were used as training and testing vectors for an Artificial neural network (ANN), in order to reduce inter-experimental variations. The identification accuracy using FD of four channels sEMG was 58%, and increased to 96% when the signals are separated to their independent components using ICA.]]
Original languageEnglish
Title of host publicationProceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine (EMBC 2009): Minneapolis, Minnesota, USA, 3-6 September 2009
PublisherIEEE
Pages364-367
Number of pages4
ISBN (Print)9781424432967
DOIs
Publication statusPublished - 2009
EventIEEE Engineering in Medicine and Biology Society. Annual Conference -
Duration: 30 Apr 2015 → …

Conference

ConferenceIEEE Engineering in Medicine and Biology Society. Annual Conference
Period30/04/15 → …

Keywords

  • electromyography
  • fractals
  • human information processing
  • independent component analysis
  • multivariant analysis
  • muscles
  • neural networks (computer science)

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