SIGN LANGUAGE TRANSLATION

Abstract

The purpose of the thesis was to create a data glove that can translate ASL by reading the finger- and hand movements. Furthermore, the applicability of conductive fabric as stretch sensors was explored. To read the hand gestures stretch sensors constructed from conductive fabric were attached to each finger of the glove to distinguish how much they were bent. The hand movements were registered using a 3-axis accelerometer which was mounted on the glove. The sensor values were read by an Arduino Nano 33 IoT mounted to the wrist of the glove which processed the readings and translated them into the corresponding sign. The microcontroller would then wirelessly transmit the result to another device through Bluetooth Low Energy. The glove was able to correctly translate all the signs of the ASL alphabet with an average accuracy of 93%. It was found that signs with small differences in hand gestures such as S and T were harder to distinguish between which would result in an accuracy of 70% for these specific signs.