Sign Language Recognition (SLR) system is a novel method that allows hard of hearing to communicate with general society. In this study, American Sign Language (ASL) recognition system was proposed by using the surface Electromyography (sEMG). The objective of this study is to recognize the American Sign Language alphabet letters and allow users to spell words and sentences. For this purpose, sEMG data are acquired from subject right forearm for twenty-seven American Sign Language gestures of twenty-six English alphabets and one for home position. Time and frequency domain (band power) information used in the feature extraction process. As a classification method, Support Vector Machine and Ensemble Learning algorithm were used and their performances are compared with tabulated results.
In conclusion, the results of this study show that sEMG signal can be used for SLR systems
