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Smart Glove for Sign Language Translation

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...

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Main Authors: Ahmad Imran, Mohd Thaim, Norazlianie, Sazali, Kadirgama, Kumaran, Ahmad Shahir, Jamaludin, Faiz, Mohd Turan, Norhaida, Ab. Razak
Format: Article
Language:English
Published: Semarak Ilmu Publishing 2023
Subjects:
T Technology (General)
TJ Mechanical engineering and machinery
TS Manufactures
Online Access:http://umpir.ump.edu.my/id/eprint/39884/1/Smart%20Glove%20for%20Sign%20Language%20Translation.pdf
http://umpir.ump.edu.my/id/eprint/39884/
https://doi.org/10.37934/aram.112.1.8087
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spelling my.ump.umpir.398842024-01-07T12:25:16Z http://umpir.ump.edu.my/id/eprint/39884/ Smart Glove for Sign Language Translation Ahmad Imran, Mohd Thaim Norazlianie, Sazali Kadirgama, Kumaran Ahmad Shahir, Jamaludin Faiz, Mohd Turan Norhaida, Ab. Razak T Technology (General) TJ Mechanical engineering and machinery TS Manufactures 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. Semarak Ilmu Publishing 2023 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/39884/1/Smart%20Glove%20for%20Sign%20Language%20Translation.pdf Ahmad Imran, Mohd Thaim and Norazlianie, Sazali and Kadirgama, Kumaran and Ahmad Shahir, Jamaludin and Faiz, Mohd Turan and Norhaida, Ab. Razak (2023) Smart Glove for Sign Language Translation. Journal of Advanced Research in Applied Mechanics, 112 (1). pp. 80-87. ISSN 2289-7895. (Published) https://doi.org/10.37934/aram.112.1.8087 10.37934/aram.112.1.8087
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic T Technology (General)
TJ Mechanical engineering and machinery
TS Manufactures
spellingShingle T Technology (General)
TJ Mechanical engineering and machinery
TS Manufactures
Ahmad Imran, Mohd Thaim
Norazlianie, Sazali
Kadirgama, Kumaran
Ahmad Shahir, Jamaludin
Faiz, Mohd Turan
Norhaida, Ab. Razak
Smart Glove for Sign Language Translation
description 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.
format Article
author Ahmad Imran, Mohd Thaim
Norazlianie, Sazali
Kadirgama, Kumaran
Ahmad Shahir, Jamaludin
Faiz, Mohd Turan
Norhaida, Ab. Razak
author_facet Ahmad Imran, Mohd Thaim
Norazlianie, Sazali
Kadirgama, Kumaran
Ahmad Shahir, Jamaludin
Faiz, Mohd Turan
Norhaida, Ab. Razak
author_sort Ahmad Imran, Mohd Thaim
title Smart Glove for Sign Language Translation
title_short Smart Glove for Sign Language Translation
title_full Smart Glove for Sign Language Translation
title_fullStr Smart Glove for Sign Language Translation
title_full_unstemmed Smart Glove for Sign Language Translation
title_sort smart glove for sign language translation
publisher Semarak Ilmu Publishing
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/39884/1/Smart%20Glove%20for%20Sign%20Language%20Translation.pdf
http://umpir.ump.edu.my/id/eprint/39884/
https://doi.org/10.37934/aram.112.1.8087
_version_ 1822924033213071360
score 13.235362

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