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Design of a Sign Language Transformer to Enable the Participation of Persons with Disabilities in Remote Healthcare Systems for Ensuring Universal Healthcare Coverage

机译:设计手语转换器,使残疾人能够参与远程医疗系统,以确保全民医疗覆盖

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Poverty, Rurality and Disability are the three major burdens in achieving Universal Healthcare Coverage (UHC). The advent of Information and Communication Technologies (ICT) and remote healthcare systems play a significant role to reach the unreached communities and are addressing rurality and poverty issues. However, the persons with disability (PWDs), especially the speech and hearing impaired people find it difficult to participate in remote healthcare systems as they cannot communicate with a remote doctor. A design of a “Sign Language Transformer (SLT)” has been introduced in this paper for the patients who know sign languages to establish a communication with a remote doctor who cannot interpret such signs. The primary function of this SLT is to recognize the signs/gestures from video images and translate them into both text and speech (SLTT), and to translate doctor's speech into sign language (STSL). Sign representation of words and sentences requires hand gesture, movement, and orientation. Several technologies such as the two-stream CNN, the two-stream 3D CNN, the LSTM, the 3DCNN+ ConvLSTM, the 3D CNN and the 3D CNN + LSTM are commonly used techniques to recognize human gestures. The proposed SLT model will evaluate the performances of these technologies to transform the Bangla Sign Language and to recommend the suitable technology for designing a sign language transformer.
机译:贫困、农村和残疾是实现全民医疗覆盖(UHC)的三大负担。信息和通信技术(ICT)和远程医疗系统的出现,在接触未接触到的社区方面发挥了重要作用,并正在解决农村和贫困问题。然而,残疾人(PWDs),尤其是语言和听力受损的人,发现很难参与远程医疗系统,因为他们无法与远程医生沟通。本文介绍了一种“手语转换器(SLT)”的设计,它可以让懂手语的患者与无法理解手语的远程医生建立通信。该SLT的主要功能是从视频图像中识别符号/手势,并将其翻译成文本和语音(SLTT),以及将医生的语音翻译成手语(STSL)。单词和句子的符号表示需要手势、动作和方向。一些技术,如双流CNN、双流3D CNN、LSTM、3DCNN+CONVLSM、3D CNN和3D CNN+LSTM,是识别人类手势的常用技术。拟议的SLT模型将评估这些技术在转换孟加拉语手语方面的性能,并推荐合适的手语转换器设计技术。

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