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