EchoGlove: A Wearable Electronic Glove For Deaf And Dumb
Year: 2025
Type: article
Abstract: The global percentage of people who are deaf or have significant hearing loss is estimated to be around 5 percent of the world’s population (approximately 430 million people) as per the World Health Organization (WHO). To tackle the communication issues faced by the deaf and dumb communities of the present world a wearable glove system is created employing flex sensors to record hand movements and categorize gestures using a hybrid Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) model in order to overcome the difficulty of real-time gesture detection for sign language. Through a web-based interface with speech output, the system offers real-time feedback along with effective and precise gesture recognition. The usage of flex sensors, which use machine learning models that record both temporal and spatial data, is a significant design element that lowers hardware complexity and cost while retaining good recognition performance. The technology is expected to have multilingual speech recognition as part of its future scope, which would allow acknowledged gestures to be translated into spoken words in a variety of languages. Additionally, the system will be connected with smart devices and virtual assistants, enhancing communication accessibility for individuals with speech or hearing impairments through a voice-enabled, multilingual interface.
Author: Kevin Joseph Paul, Chinnu Abey, Drisya Maria Siby, Jeena Joy, Shabiya M I
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: green
OpenAlex ID: W6968436114
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