Smart Glove for Sign Language Translation

Abstract

Sign language is a vital mode of communication for deaf people, yet it presents a significant barrier when interacting with those who do not understand it. The advent of technology has paved the way for innovative solutions to bridge this communication gap. This abstract explores the development and implications of a smart glove designed for sign language translation (SLT). The primary aim of this study is to create a wearable device, the Smart Glove, capable of recognizing and translating sign language gestures into text or speech. Key objectives include designing a lightweight and ergonomic glove prototype, developing machine learning algorithms for sign language recognition, implementing real-time translation capabilities, evaluating the glove's accuracy and usability, and assessing the potential impact on facilitating communication for deaf people. The Smart Glove utilizes only one sensor, flex sensors, to capture hand movements and gestures. These data inputs are processed through a custom-built machine learning model trained on a comprehensive sign language dataset. Preliminary results indicate a high accuracy rate in recognizing sign language gestures, with an average recognition rate of over 90% across a diverse set of signs. While challenges such as expanding gesture recognition and refining translation algorithms remain, this technology offers a promising solution to break down communication barriers and enhance the quality of life for those who rely on sign language.