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应用混合深度神经网络识别新冠疫情期间失聪患者使用的手语单词。

Applying Hybrid Deep Neural Network for the Recognition of Sign Language Words Used by the Deaf COVID-19 Patients.

作者信息

Venugopalan Adithya, Reghunadhan Rajesh

机构信息

Department of Computer Science, Central University of Kerala, Kasaragod, Kerala 671320 India.

出版信息

Arab J Sci Eng. 2023;48(2):1349-1362. doi: 10.1007/s13369-022-06843-0. Epub 2022 Apr 22.

DOI:10.1007/s13369-022-06843-0
PMID:35492959
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9030689/
Abstract

The rapid spread of the novel corona virus disease (COVID-19) has disrupted the traditional clinical services all over the world. Hospitals and healthcare centers have taken extreme care to minimize the risk of exposure to the virus by restricting the visitors and relatives of the patients. The dramatic changes happened in the healthcare norms have made it hard for the deaf patients to communicate and receive appropriate care. This paper reports a work on automatic sign language recognition that can mitigate the communication barrier between the deaf patients and the healthcare workers in India. Since hand gestures are the most expressive components of a sign language vocabulary, a novel dataset of dynamic hand gestures for the Indian sign language (ISL) words commonly used for emergency communication by deaf COVID-19 positive patients is proposed. A hybrid model of deep convolutional long short-term memory network has been utilized for the recognition of the proposed hand gestures and achieved an average accuracy of 83.36%. The model performance has been further validated on an alternative ISL dataset as well as a benchmarking hand gesture dataset and obtained average accuracies of and , respectively.

摘要

新型冠状病毒病（COVID-19）的迅速传播扰乱了全球传统临床服务。医院和医疗保健中心已采取极其谨慎的措施，通过限制患者的访客和亲属来尽量降低接触病毒的风险。医疗规范发生的巨大变化使聋哑患者难以沟通并获得适当护理。本文报告了一项关于自动手语识别的工作，该工作可以减轻印度聋哑患者与医护人员之间的沟通障碍。由于手势是手语词汇中最具表现力的组成部分，因此提出了一个新的动态手势数据集，用于聋哑COVID-19阳性患者在紧急通信中常用的印度手语（ISL）词汇。一种深度卷积长短期记忆网络的混合模型已被用于识别所提出的手势，并实现了83.36%的平均准确率。该模型性能在另一个ISL数据集以及一个基准手势数据集上得到了进一步验证，分别获得了[具体准确率1]和[具体准确率2]的平均准确率。

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本文引用的文献

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Multi-Modal Deep Hand Sign Language Recognition in Still Images Using Restricted Boltzmann Machine.使用受限玻尔兹曼机的静止图像多模态深度手语识别
Entropy (Basel). 2018 Oct 23;20(11):809. doi: 10.3390/e20110809.
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Hand gestures for emergency situations: A video dataset based on words from Indian sign language.紧急情况下的手势：基于印度手语词汇的视频数据集
Data Brief. 2020 Jul 11;31:106016. doi: 10.1016/j.dib.2020.106016. eCollection 2020 Aug.
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Sensors (Basel). 2020 Apr 27;20(9):2467. doi: 10.3390/s20092467.
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Dynamic Hand Gesture Recognition Using 3DCNN and LSTM with FSM Context-Aware Model.基于 3DCNN 和 LSTM 的 FSM 上下文感知模型的动态手势识别。
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