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A New Approach to Detection of Muscle Activation by Independent Component Analysis and Wavelet Transform

  • Bruno Azzerboni
  • Giovanni Finocchio
  • Maurizio Ipsale
  • Fabio La Foresta
  • Francesco Carlo Morabito
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2486)

Abstract

Recent works have demonstrated that the Independent Components (ICs) of simultaneously-recorded surface Electromyography (sEMG) recordings are more reliable in monitoring repetitive movements and better correspond with ongoing brain-wave activity than raw sEMG recordings. In this paper we propose to detect single muscle activation, when the arms reach a target, by means of ICs time-scale decomposition. Our analysis starts with acquisition of sEMG (surface EMG) signals; source separation is performed by a neural net-work that implements on Independent Component Analysis algorithm. In this way we obtain a signal set each representing single muscle activity. The wave-let transform, lastly, is utilised to detect muscle activation intervals.

Keywords

Surface EMG ICA 

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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Bruno Azzerboni
    • 1
  • Giovanni Finocchio
    • 1
  • Maurizio Ipsale
    • 1
  • Fabio La Foresta
    • 1
  • Francesco Carlo Morabito
    • 2
  1. 1.DFMTFA, Universitá degli Studi Di MessinaMessinaItaly
  2. 2.DIMET, Universitá MediterraneaReggio, CalabriaItaly

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