• DocumentCode
    250374
  • Title

    Comparative EMG classification of index finger

  • Author

    Capa, Eda ; Cotur, Yasin ; Gumus, Caner ; Kaplanoglu, Erkan ; Ozkan, Mehmed

  • Author_Institution
    Biyomedikal Muhendisligi Enstitusu, Bogazici Univ., İstanbul, Turkey
  • fYear
    2014
  • fDate
    16-17 Oct. 2014
  • Firstpage
    1
  • Lastpage
    3
  • Abstract
    In this study electromyography (EMG) signals obtained from index finger movements are analysed and classified in two separated methods and results are compared as a pre-study of dexterous anthropomorphic prosthesis hand project. The index finger differs from other hand digits for its tendon structure and higher mobility capabilities. The relation between the EMG signal and movement of the index finger is a key to understanding how the finger performs the tasks such as grasping and postures. Signals are measured from two different muscle groups and classified by grasping and positions. Finite State Machine (FSM) and Artificial Neural Network (ANN) are used for classification. Also the study indicates the effects of signals from index finger motions on a myoelectric controlled prosthetic hand.
  • Keywords
    biomechanics; electromyography; finite state machines; medical control systems; medical signal processing; neural nets; prosthetics; signal classification; EMG signal classification; artificial neural network; dexterous anthropomorphic prosthesis hand project; electromyography; finite state machine; grasping tasks; index finger motions; index finger movements; muscle groups; myoelectric controlled prosthetic hand; posture tasks; tendon structure; Electromyography; Grasping; Indexes; Mathematical model; Neural networks; Reactive power; Tendons;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Biomedical Engineering Meeting (BIYOMUT), 2014 18th National
  • Conference_Location
    Istanbul
  • Type

    conf

  • DOI
    10.1109/BIYOMUT.2014.7026365
  • Filename
    7026365