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
Link To Document :
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