Identification of COVID-19 Type Respiratory Disorders Using Channel State Analysis of Wireless Communications Links
Identification of COVID-19 Type Respiratory Disorders Using Channel State Analysis of Wireless Communications Links
复制标题
使用无线通信链路的信道状态分析识别 COVID-19 类型呼吸系统疾病
DOI：
10.1109/embc46164.2021.9630016
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Lubecke, Victor M.
中科院分区：
文献类型：
--
作者：
Lubecke, Lana C.;Ishmael, Khaldoon;Zheng, Yao;Boric-Lubecke, Olga;Lubecke, Victor M.
COVID-19的一个致命方面是，感染者往往在表现出明显症状之前就具有传染性。虽然体温检查和鼻窦拭子等方法有助于早期发现，但前者并不总是提供COVID-19的可靠指标，后者是侵入性的，需要大量的人力和物质资源来管理。本文介绍了一种利用商用现货无线通信设备实现的非侵入性COVID-19早期筛查系统。该系统利用多普勒雷达原理监测与呼吸有关的胸部运动，并识别表明COVID-19感染的呼吸频率。基于为5G NR无线通信设计的软件定义无线电（sdr）开发了一个原型，并使用模拟人类呼吸的机器人移动器和实际呼吸来评估系统性能，结果一致的呼吸频率准确性优于每分钟一次呼吸，超过了普通医疗实践中使用的呼吸频率。临床相关性：这确立了基于无线通信的雷达在识别COVID-19等呼吸系统疾病方面的潜在功效。
One deadly aspect of COVID-19 is that those infected can often be contagious before exhibiting overt symptoms. While methods such as temperature checks and sinus swabs have aided with early detection, the former does not always provide a reliable indicator of COVID-19, and the latter is invasive and requires significant human and material resources to administer. This paper presents a non-invasive COVID-19 early screening system implementable with commercial off-the-shelf wireless communications devices. The system leverages the Doppler radar principle to monitor respiratory-related chest motion and identifies breathing rates that indicate COVID-19 infection. A prototype was developed from software-defined radios (SDRs) designed for 5G NR wireless communications and system performance was evaluated using a robotic mover simulating human breathing, and using actual breathing, resulting in a consistent respiratory rate accuracy better than one breath per minute, exceeding that used in common medical practice.Clinical Relevance—This establishes the potential efficacy of wireless communications based radar for recognizing respiratory disorders such as COVID-19.
呼吸急促。
影响因子：
1.3
作者：
Diane E. Bloomfield
通讯作者：
Diane E. Bloomfield
应用和未来研究
DOI：
--
发表时间：
2016
期刊：
影响因子：
--
作者：
Aditya Singh;V. Lubecke
通讯作者：
V. Lubecke
DOI：
--
发表时间：
2020-02
期刊：
ArXiv
影响因子：
--
作者：
Yunlu Wang;Menghan Hu;Qingli Li;Xiao-Ping Zhang;Guangtao Zhai;Nan Yao
通讯作者：
Yunlu Wang;Menghan Hu;Qingli Li;Xiao-Ping Zhang;Guangtao Zhai;Nan Yao