Abstrakt

Hand Talk-A Sign Language Recognition Based On Accelerometer and SEMG Data

Anetha K, Rejina Parvin J.

Everyday communication with the hearing population poses a major challenge to those with hearing loss. For this purpose, an automatic American Sign Language recognition system is developed using artificial neural network (ANN) and to translate the ASL alphabets into text and sound. A glove circuit is designed with flex sensors, 3- axis accelerometer and sEMG sensors to capture the gestures. The finger bending data is obtained from the flex sensors on each finger whereas the accelerometer provides the trajectories of the hand motion. Some local features are extracted from the ASL alphabets which are then classified using neural network. The proposed system is evaluated for both user-dependent and user-independent conditions successfully for isolated ASL recognition. The main purpose of this Hand Talk system is to provide an ease of sharing ideas, minimized communication gap and an easier collaboration for the hard of hearing people.