EchoGlove: A Wearable Electronic Glove For Deaf And Dumb

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.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-03-28
DOI
https://doi.org/10.5281/zenodo.15102813
Primary Topic
Fractal and DNA sequence analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

EchoGlove: A Wearable Electronic Glove For Deaf And Dumb

Zenodo (CERN European Organization for Nuclear Research)
Fractal and DNA sequence analysis
article

EchoGlove: A Wearable Electronic Glove For Deaf And Dumb

article en

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.

Zenodo (CERN European Organization for Nuclear Research)
Quality Education
Openalex Percentile: Top 58%
Fractal and DNA sequence analysis
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