Chuma Euclides Lourenco, Iano Yuzo
School of Electrical and Computer EngineeringUniversity of Campinas (UNICAMP) Campinas 13083-970 Brazil.
School of Electrical and Computer EngineeringUniversity of Campinas Campinas 13083-970 Brazil.
IEEE Sens J. 2020 Oct 5;21(3):2921-2928. doi: 10.1109/JSEN.2020.3028494. eCollection 2021 Feb 1.
The 2019 coronavirus disease (COVID-19) pandemic has contaminated millions of people, resulting in high fatality rates. Recently emerging artificial intelligence technologies like the convolutional neural network (CNN) are strengthening the power of imaging tools and can help medical specialists. CNN combined with other sensors creates a new solution to fight COVID-19 transmission. This paper presents a novel method to detect coughs (an important symptom of COVID-19) using a K-band continuous-wave Doppler radar with most popular CNNs architectures: AlexNet, VGG-19, and GoogLeNet. The proposed method has cough detection test accuracy of 88.0% using AlexNet CNN with people 1 m away from the microwave radar sensor, test accuracy of 80.0% with people 3 m away from the radar sensor, and test accuracy of 86.5% with a single mixed dataset with people 1 m and 3 m away from the radar sensor. The K-band radar sensor is inexpensive, completely camera-free and collects no personally-identifying information, allaying privacy concerns while still providing in-depth public health data on individual, local, and national levels. Additionally, the measurements are conducted without human contact, making the process proposed in this work safe for the investigation of contagious diseases such as COVID-19. The proposed cough detection system using microwave radar sensor has environmental robustness and dark/light-independence, unlike traditional cameras. The proposed microwave radar sensor can be used alone or in group with other sensors in a fusion sensor system to create a robust system to detect cough and other movements, mainly if using CNNs.
2019冠状病毒病（COVID-19）大流行已感染数百万人，导致高死亡率。最近出现的人工智能技术，如卷积神经网络（CNN），正在增强成像工具的能力，并有助于医学专家。CNN与其他传感器相结合，为抗击COVID-19传播创造了一种新的解决方案。本文提出了一种使用K波段连续波多普勒雷达与最流行的CNN架构（AlexNet、VGG-19和GoogLeNet）来检测咳嗽（COVID-19的一个重要症状）的新方法。所提出的方法在使用AlexNet CNN且人员距离微波雷达传感器1米时咳嗽检测测试准确率为88.0%，人员距离雷达传感器3米时测试准确率为80.0%，在包含人员距离雷达传感器1米和3米的单一混合数据集时测试准确率为86.5%。K波段雷达传感器价格低廉，完全无需摄像头，且不收集个人身份信息，在提供个人、地方和国家层面深入公共卫生数据的同时消除了隐私担忧。此外，测量过程无需人与人接触，使得本文提出的方法对于诸如COVID-19等传染病的调查是安全的。与传统摄像头不同，所提出的使用微波雷达传感器的咳嗽检测系统具有环境鲁棒性且不受明暗影响。所提出的微波雷达传感器可以单独使用，也可以与融合传感器系统中的其他传感器组合使用，以创建一个强大的系统来检测咳嗽和其他动作，特别是在使用CNN时。
