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一种基于卷积神经网络的物联网框架,用于验证新冠肺炎卫生状况并授权进入设施。

A Convolutional Neural Network-enabled IoT framework to verify COVID-19 hygiene conditions and authorize access to facilities.

作者信息

Capris Ticiana, Takagi Yuka, Figueiredo Diana, Henriques João, Pires Ivan Miguel

机构信息

Polytechnic of Viseu, Viseu, Portugal.

University of Coimbra, Coimbra, Portugal.

出版信息

Procedia Comput Sci. 2022;203:727-732. doi: 10.1016/j.procs.2022.07.108. Epub 2022 Aug 12.

Abstract

COVID-19 has infected several million of individuals while claiming numerous lives. This fact raised the need to apply the measure to prevent its transmission. The use of disinfection products, wearing masks, and avoiding touching doors are important measures to avoid its spread. Thus, this work proposes a framework supported by a Convolutional Neural Network (CNN) model checking the hygienic conditions of the individuals requiring authorization to access facilities. The experimental work takes IoT devices with sensors to check: whether the users have disinfection product in their hands and a trained model to check whether individuals are also wearing masks. The achieved results highlighted the effectiveness of the proposed framework.

摘要

新冠病毒已感染数百万人并夺走众多生命。这一事实凸显了采取措施预防其传播的必要性。使用消毒产品、佩戴口罩以及避免触摸门是避免其传播的重要措施。因此,这项工作提出了一个由卷积神经网络(CNN)模型支持的框架,用于检查需要授权进入设施的人员的卫生状况。实验工作采用带有传感器的物联网设备来检查:用户手中是否有消毒产品,以及一个经过训练的模型来检查人员是否也佩戴了口罩。所取得的结果突出了所提出框架的有效性。

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