• DocumentCode
    2779735
  • Title

    Hybrid EMG Recognition System by MDA and PCA

  • Author

    Matsumura, Yuji ; Fukumi, Minoru ; Mitsukura, Yasue

  • Author_Institution
    Tokushima Univ., Tokushima
  • fYear
    0
  • fDate
    0-0 0
  • Firstpage
    5294
  • Lastpage
    5300
  • Abstract
    In this paper, we propose a recognition system of wrist operation by focusing on ElectroMyoGram (EMG), that is, the living body signal generated with movement of a subject. In previous research, we only performed pattern recognition by Neural Network (NN) and Fast Fourier Transform (FFT). In contrast, in proposal research, we try to improve recognition accuracy and reduce learning-time of system by combining Multi Discriminant Analysis (MDA) and gradual Principal Component Analysis (PCA) based on the PCA result of EMG data. From results of computer simulation, it is shown that our approach is effective for improvement in recognition accuracy and speed.
  • Keywords
    electromyography; fast Fourier transforms; medical signal processing; neural nets; pattern recognition; principal component analysis; Electromyogram; MDA; PCA; computer simulation; fast Fourier transform; hybrid EMG recognition system; multidiscriminant analysis; neural network; principal component analysis; Agricultural engineering; Electromyography; Fast Fourier transforms; Neural networks; Optical fiber devices; Pattern recognition; Position measurement; Principal component analysis; Signal generators; Wrist;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2006. IJCNN '06. International Joint Conference on
  • Conference_Location
    Vancouver, BC
  • Print_ISBN
    0-7803-9490-9
  • Type

    conf

  • DOI
    10.1109/IJCNN.2006.247285
  • Filename
    1716836