结合可见光和红外成像，在便携式设备上有效检测呼吸道感染，如COVID-19
Combining Visible Light and Infrared Imaging for Efficient Detection of Respiratory Infections such as COVID-19 on Portable Device
摘要：
2019冠状病毒病（COVID-19）在过去几个月已成为严重的全球流行病，并在全球范围内给人类社会造成巨大损失。对于如此大规模的疫情，及早发现和隔离潜在的病毒携带者对遏制疫情蔓延至关重要。最近的研究表明，COVID-19的一个重要特征是病毒感染引起的异常呼吸状态。疫情期间，很多人倾向于戴口罩，以降低患病风险。因此，在本文中，我们提出了一种便携式非接触的方法来筛选戴口罩的人的健康状况，通过分析的呼吸特性。该设备主要由一个FLIR热像仪和一个Android手机组成。这可能有助于在实际情况下识别潜在的COVID-19患者，例如在学校和医院进行预检。在这项工作中，我们通过结合从双模式相机和深度学习架构获得的RGB和热视频来执行健康筛查。我们首先使用人脸识别来实现戴口罩的人的呼吸数据捕获技术。然后，将具有注意力机制的双向GRU神经网络应用于呼吸数据以获得健康筛查结果。验证实验结果表明，该模型在真实数据集上的识别准确率为83.7%.异常呼吸数据和部分正常呼吸数据均来自上海交通大学医学院附属瑞金医院。其他正常的呼吸数据是从我们研究人员周围的健康人那里获得的。这项工作表明，拟议的便携式智能健康筛查设备可用作呼吸道感染的预扫描方法，这可能有助于抗击当前的COVID-19疫情。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
