典型文献
Machine learning empowered COVID-19 patient monitoring using non-contact sensing:An extensive review
文献摘要：
The severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),which caused the coronavirus disease 2019(COVID-19)pandemic,has affected more than 400 million people worldwide.With the recent rise of new Delta and Omicron variants,the efficacy of the vaccines has become an important question.The goal of various studies has been to limit the spread of the virus by utilizing wireless sensing technologies to prevent human-to-human interactions,particularly for healthcare workers.In this paper,we discuss the current literature on invasive/contact and non-invasive/non-contact technologies(including Wi-Fi,radar,and software-defined radio)that have been effectively used to detect,diagnose,and monitor human activities and COVID-19 related symptoms,such as irregular respiration.In addition,we focused on cutting-edge machine learning algorithms(such as generative adversarial networks,random forest,multilayer perceptron,support vector machine,extremely randomized trees,and k-nearest neighbors)and their essential role in intelligent healthcare systems.Furthermore,this study highlights the limitations related to non-invasive techniques and prospective research directions.
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作者姓名：
Umer Saeed;Syed Yaseen Shah;Jawad Ahmad;Muhammad Ali Imran;Qammer H.Abbasi;Syed Aziz Shah
作者机构：
Research Centre for Intelligent Healthcare,Coventry University,Coventry,CV1 5FB,UK;School of Computing,Engineering and Built Environment,Glasgow Caledonian University,Glasgow,G4 OBA,UK;School of Computing,Edinburgh Napier University,Edinburgh,EH11 4BN,UK;James Watt School of Engineering,University of Glasgow,Glasgow,G12 8QQ,UK
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引用格式：
[1]Umer Saeed;Syed Yaseen Shah;Jawad Ahmad;Muhammad Ali Imran;Qammer H.Abbasi;Syed Aziz Shah-.Machine learning empowered COVID-19 patient monitoring using non-contact sensing:An extensive review)[J].药物分析学报（英文）,2022(02):193-204
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