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IRDC-Net: An Inception Network with a Residual Module and Dilated Convolution for Sign Language Recognition Based on Surface Electromyography

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Název: IRDC-Net: An Inception Network with a Residual Module and Dilated Convolution for Sign Language Recognition Based on Surface Electromyography
Autoři: Xiangrui Wang, Lu Tang, Qibin Zheng, Xilin Yang, Zhiyuan Lu
Zdroj: Sensors, Vol 23, Iss 13, p 5775 (2023)
Informace o vydavateli: MDPI AG
Rok vydání: 2023
Sbírka: Directory of Open Access Journals: DOAJ Articles
Témata: sign language recognition, surface electromyogram, inception network, residual module, dilated convolution, Chemical technology, TP1-1185
Popis: Deaf and hearing-impaired people always face communication barriers. Non-invasive surface electromyography (sEMG) sensor-based sign language recognition (SLR) technology can help them to better integrate into social life. Since the traditional tandem convolutional neural network (CNN) structure used in most CNN-based studies inadequately captures the features of the input data, we propose a novel inception architecture with a residual module and dilated convolution (IRDC-net) to enlarge the receptive fields and enrich the feature maps, applying it to SLR tasks for the first time. This work first transformed the time domain signal into a time–frequency domain using discrete Fourier transformation. Second, an IRDC-net was constructed to recognize ten Chinese sign language signs. Third, the tandem CNN networks VGG-net and ResNet-18 were compared with our proposed parallel structure network, IRDC-net. Finally, the public dataset Ninapro DB1 was utilized to verify the generalization performance of the IRDC-net. The results showed that after transforming the time domain sEMG signal into the time–frequency domain, the classification accuracy (acc) increased from 84.29% to 91.70% when using the IRDC-net on our sign language dataset. Furthermore, for the time–frequency information of the public dataset Ninapro DB1, the classification accuracy reached 89.82%; this value is higher than that achieved in other recent studies. As such, our findings contribute to research into SLR tasks and to improving deaf and hearing-impaired people’s daily lives.
Druh dokumentu: article in journal/newspaper
Jazyk: English
Relation: https://www.mdpi.com/1424-8220/23/13/5775; https://doaj.org/toc/1424-8220; https://doaj.org/article/3b2b3373faec4f0bbd5c30335b2b2465
DOI: 10.3390/s23135775
Dostupnost: https://doi.org/10.3390/s23135775
https://doaj.org/article/3b2b3373faec4f0bbd5c30335b2b2465
Přístupové číslo: edsbas.2E34B0F2
Databáze: BASE
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  – Url: https://doi.org/10.3390/s23135775#
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  Data: IRDC-Net: An Inception Network with a Residual Module and Dilated Convolution for Sign Language Recognition Based on Surface Electromyography
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  Data: <searchLink fieldCode="AR" term="%22Xiangrui+Wang%22">Xiangrui Wang</searchLink><br /><searchLink fieldCode="AR" term="%22Lu+Tang%22">Lu Tang</searchLink><br /><searchLink fieldCode="AR" term="%22Qibin+Zheng%22">Qibin Zheng</searchLink><br /><searchLink fieldCode="AR" term="%22Xilin+Yang%22">Xilin Yang</searchLink><br /><searchLink fieldCode="AR" term="%22Zhiyuan+Lu%22">Zhiyuan Lu</searchLink>
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  Data: Sensors, Vol 23, Iss 13, p 5775 (2023)
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  Data: MDPI AG
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  Data: 2023
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  Data: <searchLink fieldCode="DE" term="%22sign+language+recognition%22">sign language recognition</searchLink><br /><searchLink fieldCode="DE" term="%22surface+electromyogram%22">surface electromyogram</searchLink><br /><searchLink fieldCode="DE" term="%22inception+network%22">inception network</searchLink><br /><searchLink fieldCode="DE" term="%22residual+module%22">residual module</searchLink><br /><searchLink fieldCode="DE" term="%22dilated+convolution%22">dilated convolution</searchLink><br /><searchLink fieldCode="DE" term="%22Chemical+technology%22">Chemical technology</searchLink><br /><searchLink fieldCode="DE" term="%22TP1-1185%22">TP1-1185</searchLink>
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  Data: Deaf and hearing-impaired people always face communication barriers. Non-invasive surface electromyography (sEMG) sensor-based sign language recognition (SLR) technology can help them to better integrate into social life. Since the traditional tandem convolutional neural network (CNN) structure used in most CNN-based studies inadequately captures the features of the input data, we propose a novel inception architecture with a residual module and dilated convolution (IRDC-net) to enlarge the receptive fields and enrich the feature maps, applying it to SLR tasks for the first time. This work first transformed the time domain signal into a time–frequency domain using discrete Fourier transformation. Second, an IRDC-net was constructed to recognize ten Chinese sign language signs. Third, the tandem CNN networks VGG-net and ResNet-18 were compared with our proposed parallel structure network, IRDC-net. Finally, the public dataset Ninapro DB1 was utilized to verify the generalization performance of the IRDC-net. The results showed that after transforming the time domain sEMG signal into the time–frequency domain, the classification accuracy (acc) increased from 84.29% to 91.70% when using the IRDC-net on our sign language dataset. Furthermore, for the time–frequency information of the public dataset Ninapro DB1, the classification accuracy reached 89.82%; this value is higher than that achieved in other recent studies. As such, our findings contribute to research into SLR tasks and to improving deaf and hearing-impaired people’s daily lives.
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  Data: 10.3390/s23135775
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    Subjects:
      – SubjectFull: sign language recognition
        Type: general
      – SubjectFull: surface electromyogram
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      – SubjectFull: inception network
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      – SubjectFull: dilated convolution
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