Haq Izaz Ul, Du Xianjun, Jan Haseeb
College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050 China.
University of Engineering and Technology, Peshawar, Pakistan.
Multimed Tools Appl. 2022;81(23):33569-33589. doi: 10.1007/s11042-022-13154-x. Epub 2022 Apr 20.
The first step to reducing the effect of viral disease is to prevent the spread which could be achieved by implementing social distancing (reducing the number of close physical interactions between peoples). Almost every viral disease whose means of communication is air, and enters through mouth or nose, definitely will affect our vocal organs which cause changes in features of our voice and could be traceable using feature analysis of voice using deep learning. The detection of an affected person using deep neural networks and tracking him would help us in the implementation of the social distancing rule in an area where it is needed. The aim of this paper is to study different solutions which help in enabling, encouraging, and even enforcing social distancing. In this paper, we implemented and analyzed scenarios on the basis of COVID-19 patient detection using cough and tracking him using smart cameras, or emerging wireless technologies with deep learning techniques for prediction and preventing the spread of disease. Thus these techniques are easy to be implemented in the initial stage of any pandemic as well and will help us in the implementation of smart social distancing (apply whenever needed).
减轻病毒性疾病影响的第一步是防止其传播，这可以通过实施社交距离（减少人与人之间密切的身体接触次数）来实现。几乎每一种通过空气传播、经口鼻进入人体的病毒性疾病，肯定都会影响我们的发声器官，导致我们声音特征发生变化，并且可以通过使用深度学习对声音进行特征分析来追踪。利用深度神经网络检测感染者并对其进行追踪，将有助于我们在需要的地区实施社交距离规则。本文的目的是研究有助于实现、鼓励甚至强制实施社交距离的不同解决方案。在本文中，我们基于使用咳嗽检测新冠患者并使用智能摄像头对其进行追踪，或者结合新兴无线技术与深度学习技术进行疾病预测和传播预防，来实施和分析各种场景。因此，这些技术在任何大流行的初始阶段也很容易实施，并将帮助我们实施智能社交距离（在需要时随时应用）。
