• DocumentCode
    3436547
  • Title

    Development of two degree of freedom (DoF) bionic hand for below elbow amputee

  • Author

    Srinivasa, P.L. ; Nagananda, S.N. ; Kadambi, Govind R. ; Hariharan, R. ; Shankpal, P. ; Shankapal, S.R.

  • Author_Institution
    M.S. Ramaiah Sch. of Adv. Studies, Bangalore, India
  • fYear
    2013
  • fDate
    17-19 Jan. 2013
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    The Human hand is versatile in its interaction. Imitating the versatilities and human dexterity in robotic design is a huge challenge, and requires a great depth of understanding of the human upper limb physiology and its robotic equivalent. A huge amount of research worldwide is being carried out in order to develop a human hand like prosthetic which can provide natural haptic functionality. This paper provides an insight to the development of a bionic hand which performs hand opposition and reposition actions (clasp and release) based on real EMG signals from a below elbow amputee. It also provides an understanding of design involved in development of the robotic model of the hand, the electronics behind it and finally the signal processing technique behind classification of EMG signals to make the bionic hand perform the desired haptic function. The paper indicates that Artificial Neural Network (ANN) and Random Forest method is the best technique that can be used to classify actions indicated through real time EMG signals.
  • Keywords
    dexterous manipulators; electromyography; medical signal processing; neural nets; prosthetics; signal classification; ANN; artificial neural network; below elbow amputee; hand opposition; human dexterity; human hand like prosthetic; human upper limb physiology; natural haptic functionality; random forest method; real EMG signal classification; reposition actions; robotic design; signal processing technique; two degree of freedom bionic hand; Artificial neural networks; Classification algorithms; Elbow; Electromyography; Feature extraction; Humans; Vegetation; Artificial Neural Network (ANN); Autoregressive Model (AR); Data Acqusistion System (DAQ); Degree of Freedom (DoF); Mean Absolute Value (MAV); Principal Componenet Analysis (PCA); Random Forest; Root Mean Square (RMS); Waveform Length (WL);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electronics, Computing and Communication Technologies (CONECCT), 2013 IEEE International Conference on
  • Conference_Location
    Bangalore
  • Print_ISBN
    978-1-4673-4609-2
  • Type

    conf

  • DOI
    10.1109/CONECCT.2013.6469296
  • Filename
    6469296