Khan Muhammad Bilal, Zhang Zhiya, Li Lin, Zhao Wei, Hababi Mohammed Ali Mohammed Al, Yang Xiaodong, Abbasi Qammer H
School of Electronic Engineering, Xidian University, Xi'an 710071, China.
Department of Electrical and Computer Engineering, COMSATS University Islamabad, Attock Campus, Islamabad 43600, Pakistan.
Micromachines (Basel). 2020 Sep 30;11(10):912. doi: 10.3390/mi11100912.
The rapid spread of the novel coronavirus disease, COVID-19, and its resulting situation has garnered much effort to contain the virus through scientific research. The tragedy has not yet fully run its course, but it is already clear that the crisis is thoroughly global, and science is at the forefront in the fight against the virus. This includes medical professionals trying to cure the sick at risk to their own health; public health management tracking the virus and guardedly calling on such measures as social distancing to curb its spread; and researchers now engaged in the development of diagnostics, monitoring methods, treatments and vaccines. Recent advances in non-contact sensing to improve health care is the motivation of this study in order to contribute to the containment of the COVID-19 outbreak. The objective of this study is to articulate an innovative solution for early diagnosis of COVID-19 symptoms such as abnormal breathing rate, coughing and other vital health problems. To obtain an effective and feasible solution from existing platforms, this study identifies the existing methods used for human activity and health monitoring in a non-contact manner. This systematic review presents the data collection technology, data preprocessing, data preparation, features extraction, classification algorithms and performance achieved by the various non-contact sensing platforms. This study proposes a non-contact sensing platform for the early diagnosis of COVID-19 symptoms and monitoring of the human activities and health during the isolation or quarantine period. Finally, we highlight challenges in developing non-contact sensing platforms to effectively control the COVID-19 situation.
新型冠状病毒疾病（COVID-19）的迅速传播及其引发的状况促使人们通过科学研究来大力遏制该病毒。这场悲剧尚未完全结束，但已然清晰的是，这场危机具有彻头彻尾的全球性，而科学处于抗击该病毒的前沿阵地。这包括冒着自身健康风险努力治愈患者的医学专业人员；追踪病毒并谨慎呼吁采取诸如保持社交距离等措施以遏制其传播的公共卫生管理部门；以及目前致力于开发诊断方法、监测手段、治疗方法和疫苗的研究人员。为改善医疗保健而在非接触传感方面取得的最新进展是本研究的动机，旨在为遏制COVID-19疫情做出贡献。本研究的目的是阐明一种创新解决方案，用于早期诊断COVID-19症状，如呼吸速率异常、咳嗽及其他重要健康问题。为了从现有平台获得有效且可行的解决方案，本研究确定了以非接触方式用于人类活动和健康监测的现有方法。本系统综述展示了各种非接触传感平台所采用的数据收集技术、数据预处理、数据准备、特征提取、分类算法及取得的性能。本研究提出了一个非接触传感平台，用于早期诊断COVID-19症状以及在隔离或检疫期间监测人类活动和健康状况。最后，我们强调了在开发非接触传感平台以有效控制COVID-19疫情方面所面临的挑战。
