• DocumentCode
    776355
  • Title

    A Comparison of Surface and Intramuscular Myoelectric Signal Classification

  • Author

    Hargrove, Levi J. ; Englehart, K. ; Hudgins, B.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., New Brunswick Univ., Fredericton, NB
  • Volume
    54
  • Issue
    5
  • fYear
    2007
  • fDate
    5/1/2007 12:00:00 AM
  • Firstpage
    847
  • Lastpage
    853
  • Abstract
    The surface myoelectric signal (MES) has been used as an input to controllers for powered prostheses for many years. As a result of recent technological advances it is reasonable to assume that there will soon be implantable myoelectric sensors which will enable the internal MES to be used as input to these controllers. An internal MES measurement should have less muscular crosstalk allowing for more independent control sites. However, it remains unclear if this benefit outweighs the loss of the more global information contained in the surface MES. This paper compares the classification accuracy of six pattern recognition-based myoelectric controllers which use multi-channel surface MES as inputs to the same controllers which use multi-channel intramuscular MES as inputs. An experiment was designed during which surface and intramuscular MES were collected simultaneously for 10 different classes of isometric contraction. There was no significant difference in classification accuracy as a result of using the intramuscular MES measurement technique when compared to the surface MES measurement technique. Impressive classification accuracy (97%) could be achieved by optimally selecting only three channels of surface MES
  • Keywords
    electromyography; medical control systems; medical signal processing; pattern recognition; prosthetics; signal classification; controllers; implantable myoelectric sensors; intramuscular myoelectric signal classification; isometric contraction; pattern recognition; powered prostheses; surface myoelectric signal classification; Biomedical signal processing; Control systems; Data mining; Elbow; Feature extraction; Measurement techniques; Pattern classification; Pattern recognition; Prosthetics; Technological innovation; Classification; EMG; intramuscular; myoelectric; pattern recognition; prostheses; Adult; Electric Impedance; Electrodes; Electromyography; Forearm; Hand; Humans; Isometric Contraction; Male; Models, Biological; Muscle, Skeletal; Pattern Recognition, Physiological; Signal Processing, Computer-Assisted; Software; Wrist;
  • fLanguage
    English
  • Journal_Title
    Biomedical Engineering, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9294
  • Type

    jour

  • DOI
    10.1109/TBME.2006.889192
  • Filename
    4154997