• DocumentCode
    84740
  • Title

    Surface Versus Untargeted Intramuscular EMG Based Classification of Simultaneous and Dynamically Changing Movements

  • Author

    Kamavuako, Ernest N. ; Rosenvang, Jakob Celander ; Horup, Ronnie ; Jensen, W. ; Farina, Dario ; Englehart, Kevin B.

  • Author_Institution
    Dept. of HST, Aalborg Univ., Aalborg, Denmark
  • Volume
    21
  • Issue
    6
  • fYear
    2013
  • fDate
    Nov. 2013
  • Firstpage
    992
  • Lastpage
    998
  • Abstract
    The pattern recognition-based myoelectric control scheme is in the process of being implemented in clinical settings, but it has been mainly tested on sequential and steady state data. This paper investigates the ability of pattern recognition to resolve movements that are simultaneous and dynamically changing and compares the use of surface and untargeted intramuscular EMG signals for this purpose. Ten able-bodied subjects participated in the study. Both EMG types were recorded concurrently from the right forearm. The subjects were instructed to track dynamic contraction profiles using single and combined degrees of freedom in three trials. During trials one and two, the amplitude and the frequency of the profile were kept constant (nonmodulated data), and during trial three, the two parameters were modulated (modulated data). The results showed that the performance was up to 93% for nonmodulated tasks, but highly depended on the nature of the data used. Surface and untargeted intramuscular EMG had equal performance for data of similar nature (nonmodulated), but the performance of intramuscular EMG decreased, compared to surface, when tested on modulated data. However, the results of intramuscular recordings obtained in this study are promising for future use of implantable electrodes, because, besides the value added in terms of potential chronic implantation, the performance is theoretically the same as for surface EMG provided that enough information is captured in the recordings. Nevertheless, care should be taken when training the system since data obtained from selective recordings probably need more training data to generalize to new signals.
  • Keywords
    biomedical electrodes; electromyography; medical control systems; medical signal processing; pattern recognition; signal classification; chronic implantation; dynamic changing movements; dynamic contraction profiles; implantable electrodes; intramuscular EMG based classilication; intramuscular recordings; nonmodulated tasks; pattern recognition; pattern recognition-based myoelectric control scheme; selective recordings; simultaneous changing movements; steady state data; surface EMG signals; surface intramuscular EMG based classilication; training data; untargeted intramuscular EMG based classilication; Educational institutions; Electrodes; Electromyography; Muscles; Pattern recognition; Torque; Training; Classifiers; dynamic movement; intramuscular electromyography (EMG); pattern recognition; simultaneous movement; Adaptation, Physiological; Adult; Algorithms; Electromyography; Female; Humans; Male; Muscle Contraction; Muscle, Skeletal; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Young Adult;
  • fLanguage
    English
  • Journal_Title
    Neural Systems and Rehabilitation Engineering, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1534-4320
  • Type

    jour

  • DOI
    10.1109/TNSRE.2013.2248750
  • Filename
    6476027