Recognition of Radar-Based Deaf Sign Language Using Convolution Neural Network
The 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...
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my.uniten.dspace-344822024-10-14T11:20:05Z Recognition of Radar-Based Deaf Sign Language Using Convolution Neural Network Malik M.D.H.D. Mansor W. Rashid N.E.A. Rahman M.Z.U. 57204590565 16175247200 57219237806 57220046684 classification deep learning gestures Radar Short-Time Fourier Transform (STFT) The 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 inputs segmented 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 Office Final 2024-10-14T03:20:05Z 2024-10-14T03:20:05Z 2023 Article 10.30880/ijie.2023.15.03.012 2-s2.0-85170289448 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170289448&doi=10.30880%2fijie.2023.15.03.012&partnerID=40&md5=d0f8ac3d6f1c1f0dd4921c42d60d3248 https://irepository.uniten.edu.my/handle/123456789/34482 15 3 124 130 All Open Access Bronze Open Access Penerbit UTHM Scopus |
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classification deep learning gestures Radar Short-Time Fourier Transform (STFT) Malik M.D.H.D. Mansor W. Rashid N.E.A. Rahman M.Z.U. Recognition of Radar-Based Deaf Sign Language Using Convolution Neural Network |
description |
The 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 inputs |
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57204590565 |
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57204590565 Malik M.D.H.D. Mansor W. Rashid N.E.A. Rahman M.Z.U. |
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Article |
author |
Malik M.D.H.D. Mansor W. Rashid N.E.A. Rahman M.Z.U. |
author_sort |
Malik M.D.H.D. |
title |
Recognition of Radar-Based Deaf Sign Language Using Convolution Neural Network |
title_short |
Recognition of Radar-Based Deaf Sign Language Using Convolution Neural Network |
title_full |
Recognition of Radar-Based Deaf Sign Language Using Convolution Neural Network |
title_fullStr |
Recognition of Radar-Based Deaf Sign Language Using Convolution Neural Network |
title_full_unstemmed |
Recognition of Radar-Based Deaf Sign Language Using Convolution Neural Network |
title_sort |
recognition of radar-based deaf sign language using convolution neural network |
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Penerbit UTHM |
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2024 |
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1814061122285731840 |
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13.214268 |