Sign Language Glove

Abstract

Communication barriers for speech-impaired and hearing-impaired people persist due to the time and effort to learn different sign languages. To address this, we develop sign language gloves, allowing sign language users to communicate with others without extensive training. Our approach begins with American Sign Language (ASL), utilising open-source somatosensory gloves to capture Metacarpophalangeal (MCP) joint movements and flex sensors to detect Proximal interphalangeal (PIP) joint movements. The collected data are processed and trained using deep learning techniques to improve recognition accuracy. This system aims to enhance accessibility and facilitate interaction between sign language users and non-sign language users.