Development of Sign Language Motion Recognition System for Hearing-Impaired People Using Electromyography Signal

Sign languages are developed around the world for hearing-impaired people to communicate with others who understand them. Different grammar and alphabets limit the usage of sign languages between different sign language users. Furthermore, training is required for hearing-intact people to communicat...

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Main Authors: Shigeyuki Tateno, Hongbin Liu, Junhong Ou
Format: Article
Language:English
Published: MDPI AG 2020-10-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/20/20/5807
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spelling doaj-bf88a27824d0498583a90571e3bcf0fb2020-11-25T03:58:29ZengMDPI AGSensors1424-82202020-10-01205807580710.3390/s20205807Development of Sign Language Motion Recognition System for Hearing-Impaired People Using Electromyography SignalShigeyuki Tateno0Hongbin Liu1Junhong Ou2Graduate School of Information, Production and Systems, Waseda University, Kitakyushu 808-0135, JapanGraduate School of Information, Production and Systems, Waseda University, Kitakyushu 808-0135, JapanGraduate School of Information, Production and Systems, Waseda University, Kitakyushu 808-0135, JapanSign languages are developed around the world for hearing-impaired people to communicate with others who understand them. Different grammar and alphabets limit the usage of sign languages between different sign language users. Furthermore, training is required for hearing-intact people to communicate with them. Therefore, in this paper, a real-time motion recognition system based on an electromyography signal is proposed for recognizing actual American Sign Language (ASL) hand motions for helping hearing-impaired people communicate with others and training normal people to understand the sign languages. A bilinear model is applied to deal with the on electromyography (EMG) data for decreasing the individual difference among different people. A long short-term memory neural network is used in this paper as the classifier. Twenty sign language motions in the ASL library are selected for recognition in order to increase the practicability of the system. The results indicate that this system can recognize these twenty motions with high accuracy among twenty participants. Therefore, this system has the potential to be widely applied to help hearing-impaired people for daily communication and normal people to understand the sign languages.https://www.mdpi.com/1424-8220/20/20/5807motion recognitionelectromyographylong short-term memory neural networkbilinear modelsign language
collection DOAJ
language English
format Article
sources DOAJ
author Shigeyuki Tateno
Hongbin Liu
Junhong Ou
spellingShingle Shigeyuki Tateno
Hongbin Liu
Junhong Ou
Development of Sign Language Motion Recognition System for Hearing-Impaired People Using Electromyography Signal
Sensors
motion recognition
electromyography
long short-term memory neural network
bilinear model
sign language
author_facet Shigeyuki Tateno
Hongbin Liu
Junhong Ou
author_sort Shigeyuki Tateno
title Development of Sign Language Motion Recognition System for Hearing-Impaired People Using Electromyography Signal
title_short Development of Sign Language Motion Recognition System for Hearing-Impaired People Using Electromyography Signal
title_full Development of Sign Language Motion Recognition System for Hearing-Impaired People Using Electromyography Signal
title_fullStr Development of Sign Language Motion Recognition System for Hearing-Impaired People Using Electromyography Signal
title_full_unstemmed Development of Sign Language Motion Recognition System for Hearing-Impaired People Using Electromyography Signal
title_sort development of sign language motion recognition system for hearing-impaired people using electromyography signal
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2020-10-01
description Sign languages are developed around the world for hearing-impaired people to communicate with others who understand them. Different grammar and alphabets limit the usage of sign languages between different sign language users. Furthermore, training is required for hearing-intact people to communicate with them. Therefore, in this paper, a real-time motion recognition system based on an electromyography signal is proposed for recognizing actual American Sign Language (ASL) hand motions for helping hearing-impaired people communicate with others and training normal people to understand the sign languages. A bilinear model is applied to deal with the on electromyography (EMG) data for decreasing the individual difference among different people. A long short-term memory neural network is used in this paper as the classifier. Twenty sign language motions in the ASL library are selected for recognition in order to increase the practicability of the system. The results indicate that this system can recognize these twenty motions with high accuracy among twenty participants. Therefore, this system has the potential to be widely applied to help hearing-impaired people for daily communication and normal people to understand the sign languages.
topic motion recognition
electromyography
long short-term memory neural network
bilinear model
sign language
url https://www.mdpi.com/1424-8220/20/20/5807
work_keys_str_mv AT shigeyukitateno developmentofsignlanguagemotionrecognitionsystemforhearingimpairedpeopleusingelectromyographysignal
AT hongbinliu developmentofsignlanguagemotionrecognitionsystemforhearingimpairedpeopleusingelectromyographysignal
AT junhongou developmentofsignlanguagemotionrecognitionsystemforhearingimpairedpeopleusingelectromyographysignal
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