Combining Visible Light and Infrared Imaging for Efficient Detection of Respiratory Infections such as COVID-19 on Portable Device

TL;DR

This paper presents a portable, non-contact device combining visible and thermal imaging with deep learning to efficiently screen for respiratory infections like COVID-19, especially for mask-wearers, achieving 83.7% accuracy.

Contribution

It introduces a novel dual-mode camera system and deep learning approach for respiratory health screening in masked individuals, suitable for rapid pre-inspection scenarios.

Findings

Achieved 83.7% accuracy in respiratory health status classification.

Developed a respiratory data capture technique for masked individuals.

Validated the system with real-world data from hospitals and healthy subjects.

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.