RECORD
Combining Visible Light and Infrared Imaging for Efficient Detection of Respiratory Infections such as COVID-19 on Portable Device
Computer Vision and Pattern Recognition · Submitted 15 Apr 2020
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim This work demonstrates that the proposed portable and intelligent health screening device can be used as a pre-scan method for respiratory infections, which may help fight the current COVID-19 epidemic.
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
Open the compiled record
The abstract, sentence by sentence
Coronavirus Disease 2019 (COVID-19) has become a serious global epidemic in the past few months and caused huge loss to human society worldwide. For such a large-scale epidemic, early detection and isolation of potential virus carriers is essential to curb the spread of the epidemic. Recent studies have shown that one important feature of COVID-19 is the abnormal respiratory status caused by viral infections. During the epidemic, 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 condition of people wearing masks through analysis of the respiratory characteristics. The device mainly consists of a FLIR one thermal camera and an Android phone. This may help identify those potential patients of COVID-19 under practical scenarios such as pre-inspection in schools and hospitals. In this work, we perform the health screening through the combination of the RGB and thermal videos obtained from the dual-mode camera and deep learning architecture.We first accomplish a respiratory data capture technique for people wearing masks by using face recognition. Then, a bidirectional GRU neural network with 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 on respiratory with the accuracy of 83.7\% on the real-world dataset. The abnormal respiratory data and part of normal respiratory data are collected from Ruijin Hospital Affiliated to The Shanghai Jiao Tong University Medical School. Other normal respiratory data are obtained from healthy people around our researchers. This work demonstrates that the proposed portable and intelligent health screening device can be used as a pre-scan method for respiratory infections, which may help fight the current COVID-19 epidemic.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Coronavirus Disease 2019 (COVID-19) has become a serious global epidemic in the past few months and caused huge loss to human society worldwide. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | For such a large-scale epidemic, early detection and isolation of potential virus carriers is essential to curb the spread of the epidemic. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s2 | Recent studies have shown that one important feature of COVID-19 is the abnormal respiratory status caused by viral infections. | background 1.00 | 0.04 | 0.02 | asserted 0.97 |
| s3 | During the epidemic, many people tend to wear masks to reduce the risk of getting sick. | background 0.98 | 0.04 | 0.01 | asserted 0.94 |
| s4 | Therefore, in this paper, we propose a portable non-contact method to screen the health condition of people wearing masks through analysis of the respiratory characteristics. | contribution 1.00 | 0.57 | 0.08 | asserted 0.96 |
| s5 | The device mainly consists of a FLIR one thermal camera and an Android phone. | method 0.99 | 0.57 | 0.02 | asserted 1.00 |
| s6 | This may help identify those potential patients of COVID-19 under practical scenarios such as pre-inspection in schools and hospitals. | interpretation 0.83 | 0.12 | 0.10 | hedged possibility 1.00 |
| s7 | In this work, we perform the health screening through the combination of the RGB and thermal videos obtained from the dual-mode camera and deep learning architecture.We first accomplish a respiratory data capture technique for people wearing masks by using face recognition. | method 0.98 | 0.94 | 0.05 | asserted 0.99 |
| s8 | Then, a bidirectional GRU neural network with attention mechanism is applied to the respiratory data to obtain the health screening result. | method 1.00 | 0.94 | 0.04 | asserted 0.99 |
| s9 | The results of validation experiments show that our model can identify the health status on respiratory with the accuracy of 83.7\% on the real-world dataset. | result 1.00 | 0.97 | 0.24 | asserted 1.00 |
| s10 | The abnormal respiratory data and part of normal respiratory data are collected from Ruijin Hospital Affiliated to The Shanghai Jiao Tong University Medical School. | method 1.00 | 0.86 | 0.02 | asserted 1.00 |
| s11 | Other normal respiratory data are obtained from healthy people around our researchers. | method 1.00 | 0.87 | 0.02 | asserted 1.00 |
| s12 | This work demonstrates that the proposed portable and intelligent health screening device can be used as a pre-scan method for respiratory infections, which may help fight the current COVID-19 epidemic. | interpretation 0.95 | 0.76 | 0.90 | asserted 0.72 |
What was examined, and what was not
- arXiv listing abstract v1
- full text
- figures and tables
- references and citing sentences
- relations to other papers
- P abstract
- S sections
- T body sentences
- C citations
- R cross-paper relations
The paper
Original abstract
Coronavirus Disease 2019 (COVID-19) has become a serious global epidemic in the past few months and caused huge loss to human society worldwide. For such a large-scale epidemic, early detection and isolation of potential virus carriers is essential to curb the spread of the epidemic. Recent studies have shown that one important feature of COVID-19 is the abnormal respiratory status caused by viral infections. During the epidemic, 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 condition of people wearing masks through analysis of the respiratory characteristics. The device mainly consists of a FLIR one thermal camera and an Android phone. This may help identify those potential patients of COVID-19 under practical scenarios such as pre-inspection in schools and hospitals. In this work, we perform the health screening through the combination of the RGB and thermal videos obtained from the dual-mode camera and deep learning architecture.We first accomplish a respiratory data capture technique for people wearing masks by using face recognition. Then, a bidirectional GRU neural network with 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 on respiratory with the accuracy of 83.7\% on the real-world dataset. The abnormal respiratory data and part of normal respiratory data are collected from Ruijin Hospital Affiliated to The Shanghai Jiao Tong University Medical School. Other normal respiratory data are obtained from healthy people around our researchers. This work demonstrates that the proposed portable and intelligent health screening device can be used as a pre-scan method for respiratory infections, which may help fight the current COVID-19 epidemic.
Human review
How the literature uses it
2 arXiv papers cite this in 3 sentences
What it cites · 2 sentences without a confident label
Cites as background 2 papers
-
GRU is a simplified LSTM which merges three doors of LSTM (forget, input and output) into two doors (update and reset) [ref] .
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation · BiGRU-AT Neural Network · 0.84
-
The bidirectional RNN has been widely used in natural language processing [ref] .
Neural Machine Translation by Jointly Learning to Align and Translate · BiGRU-AT Neural Network · 0.91
Discussion
Know something about this paper? Leave a comment, or highlight any passage above to comment on it.