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利用非接触式传感的机器学习助力新冠病毒肺炎患者监测:全面综述

Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review.

作者信息

Saeed Umer, Shah Syed Yaseen, Ahmad Jawad, Imran Muhammad Ali, Abbasi Qammer H, Shah Syed Aziz

机构信息

Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5FB, UK.

School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, G4 0BA, UK.

出版信息

J Pharm Anal. 2022 Apr;12(2):193-204. doi: 10.1016/j.jpha.2021.12.006. Epub 2022 Jan 4.

Abstract

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.

摘要

导致2019冠状病毒病(COVID-19)大流行的严重急性呼吸综合征冠状病毒2(SARS-CoV-2)已影响全球超过4亿人。随着最近新的德尔塔和奥密克戎变种的出现,疫苗的有效性已成为一个重要问题。各种研究的目标是通过利用无线传感技术来防止人际互动,特别是医护人员之间的互动,从而限制病毒的传播。在本文中,我们讨论了有关侵入性/接触式和非侵入性/非接触式技术(包括Wi-Fi、雷达和软件定义无线电)的现有文献,这些技术已被有效地用于检测、诊断和监测人类活动以及与COVID-19相关的症状,如呼吸不规则。此外,我们重点关注了前沿的机器学习算法(如生成对抗网络、随机森林、多层感知器、支持向量机、极端随机树和k近邻)及其在智能医疗系统中的重要作用。此外,本研究强调了与非侵入性技术相关的局限性以及未来研究方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a9ca/9091936/40e7c7d33fc7/ga1.jpg

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