GESTURE CONTROLLED HOME AUTOMATION FOR DIFFERENTLY CHALLENGED PEOPLE
Author’s Name : R Prabhuraj | B Saravanakumar
Volume 01 Issue 02 Year 2014 ISSN No: 2349-252X Page no: 1-6
Abstract -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 EMG 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. Finger bending data is transmitted via zigbee and given to driver circuit to control the home appliances.
Keywords –Accelerometer, Artificial Neural Network, Electromyography, Flex Sensors, Sign Language Recognition