Publication:
Recognition of Radar-Based Deaf Sign Language Using Convolution Neural Network

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dc.citedby1
dc.contributor.authorMalik M.D.H.D.en_US
dc.contributor.authorMansor W.en_US
dc.contributor.authorRashid N.E.A.en_US
dc.contributor.authorRahman M.Z.U.en_US
dc.contributor.authorid57204590565en_US
dc.contributor.authorid16175247200en_US
dc.contributor.authorid57219237806en_US
dc.contributor.authorid57220046684en_US
dc.date.accessioned2024-10-14T03:20:05Z
dc.date.available2024-10-14T03:20:05Z
dc.date.issued2023
dc.description.abstractThe difficulties in the communication between the deaf and normal people through sign language can be overcome by implementing deep learning in the gestures signal recognition. The use of the Convolution Neural Network (CNN) in distinguishing radar-based gesture signals of deaf sign language has not been investigated. This paper describes the recognition of gestures of deaf sign language using radar and CNN. Six gestures of deaf sign language were acquired from normal subjects using a radar system and processed. Short-time Fourier Transform was performed to extract the gestures features and the classification was performed using CNN. The performance of CNN was examined using two types of inputsen_US
dc.description.abstractsegmented and non-segmented spectrograms. The accuracy of recognising the gestures is higher (92.31%) using the non-segmented spectrograms compared to the segmented spectrogram. The radar-based deaf sign language could be recognised accurately using CNN without segmentation. � Universiti Tun Hussein Onn Malaysia Publisher�s Officeen_US
dc.description.natureFinalen_US
dc.identifier.doi10.30880/ijie.2023.15.03.012
dc.identifier.epage130
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85170289448
dc.identifier.spage124
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85170289448&doi=10.30880%2fijie.2023.15.03.012&partnerID=40&md5=d0f8ac3d6f1c1f0dd4921c42d60d3248
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34482
dc.identifier.volume15
dc.pagecount6
dc.publisherPenerbit UTHMen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofBronze Open Access
dc.sourceScopus
dc.sourcetitleInternational Journal of Integrated Engineering
dc.subjectclassification
dc.subjectdeep learning
dc.subjectgestures
dc.subjectRadar
dc.subjectShort-Time Fourier Transform (STFT)
dc.titleRecognition of Radar-Based Deaf Sign Language Using Convolution Neural Networken_US
dc.typeArticleen_US
dspace.entity.typePublication
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