• DocumentCode
    714760
  • Title

    Recognition of sign language numbers via electromyography signals

  • Author

    Ketenci, Seniha ; Kayikcioglu, Temel ; Gangal, Ali

  • Author_Institution
    Elektrik-Elektron. Muhendisligi Bolumu, Karadeniz Teknik Univ., Trabzon, Turkey
  • fYear
    2015
  • fDate
    16-19 May 2015
  • Firstpage
    2593
  • Lastpage
    2596
  • Abstract
    Muscle signal is widely utilized in recognition of hand gesture, prosthetics and rehabilitation. Some study is available to recognize the hand gesture via EMG for some sign languages. Sign language performed generally with hand movement is developed for deaf. According to our research, any study is not available for numbers of between 0 and 9 in Turkish sign language. In this paper, surface electrodes put on fore arm were used to record EMG signals in order to determine these numbers. Features were extracted using root mean square, variance, waveform length, Fourier transform coefficients and proposed standard deviation of crosscorrelation coefficients after preprocessing. It caused that performance of linear discriminant analysis increased highly.
  • Keywords
    Fourier transforms; correlation methods; electromyography; feature extraction; sign language recognition; EMG signals; Fourier transform coefficients; Turkish sign language; crosscorrelation coefficients; deaf; electromyography signals; feature extraction; hand gesture recognition; hand movement; linear discriminant analysis; muscle signal; prosthetics; rehabilitation; root mean square; sign language number recognition; Assistive technology; Electromyography; Gesture recognition; Linear discriminant analysis; Muscles; Prosthetics; Surface treatment; electromyogram; hand gesture; linear discriminant analysis; sign language;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing and Communications Applications Conference (SIU), 2015 23th
  • Conference_Location
    Malatya
  • Type

    conf

  • DOI
    10.1109/SIU.2015.7130416
  • Filename
    7130416