Detection of Respiratory Infections Using RGB-Infrared Sensors on Portable Device

被引:49
作者
Jiang, Zheng [1 ,2 ]
Hu, Menghan [2 ,3 ]
Gao, Zhongpai [1 ,2 ]
Fan, Lei [1 ,2 ]
Dai, Ranran [4 ]
Pan, Yaling [5 ]
Tang, Wei [6 ]
Zhai, Guangtao [1 ,2 ]
Lu, Yong [5 ]
机构
[1] Shanghai Jiao Tong Univ, Inst Image Commun & Informat Proc, Shanghai 200240, Peoples R China
[2] Minist Educ, Key Lab Artificial Intelligence, Shanghai 200240, Peoples R China
[3] East China Normal Univ, Shanghai Key Lab Multidimens Informat Proc, Shanghai 200062, Peoples R China
[4] Shanghai Jiao Tong Univ, Sch Med, Ruijin Hosp, Dept Pulm & Crit Care Med, Shanghai 200240, Peoples R China
[5] Shanghai Jiao Tong Univ, Sch Med, Ruijin Hosp, Dept Radiol,Luwan Branch, Shanghai 200240, Peoples R China
[6] Shanghai Jiao Tong Univ, Sch Med, Ruijin Hosp, Dept Resp Dis, Shanghai 200240, Peoples R China
基金
中国国家自然科学基金;
关键词
Cameras; Face; Videos; Feature extraction; COVID-19; Sensors; Deep learning; pandemic; deep learning; dual-mode tomography; health screening; recurrent neural network; respiratory state; SARS-CoV-2; thermal imaging;
D O I
10.1109/JSEN.2020.3004568
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Coronavirus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronaviruses 2 (SARS-CoV-2) has become a serious global pandemic in the past few months and caused huge loss to human society worldwide. For such a large-scale pandemic, early detection and isolation of potential virus carriers is essential to curb the spread of the pandemic. Recent studies have shown that one important feature of COVID-19 is the abnormal respiratory status caused by viral infections. During the pandemic, many people tend to wear masks to reduce the risk of getting sick. Therefore, in this paper, we propose a portable non-contact method to screen the health conditions of people wearing masks through analysis of the respiratory characteristics from RGB-infrared sensors. We first accomplish a respiratory data capture technique for people wearing masks by using face recognition. Then, a bidirectional GRU neural network with an attention mechanism is applied to the respiratory data to obtain the health screening result. The results of validation experiments show that our model can identify the health status of respiratory with 83.69% accuracy, 90.23% sensitivity and 76.31% specificity on the real-world dataset. This work demonstrates that the proposed RGB-infrared sensors on portable device can be used as a pre-scan method for respiratory infections, which provides a theoretical basis to encourage controlled clinical trials and thus helps fight the current COVID-19 pandemic. The demo videos of the proposed system are available at: https://doi.org/10.6084/m9.figshare.12028032.
引用
收藏
页码:13674 / 13681
页数:8
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