Yamasinghe, NRanasinghe, YDissanayake, YWijekoon, J.LPanchendrarajan, R2022-11-292022-11-292022-09-08N. Yamasinghe, Y. Ranasinghe, Y. Dissanayake, J. L. Wijekoon and R. Panchendrarajan, "iMask: An IoT-based Intelligent Mask to Identify and Track COVID-19 Suspects," 2022 IEEE International Conference on Smart Internet of Things (SmartIoT), 2022, pp. 7-14, doi: 10.1109/SmartIoT55134.2022.00011.2770-2677https://rda.sliit.lk/handle/123456789/3082COVID-19 has become a global health concern, and wearing masks is a key measure to curb COVID-19 from rapidly spreading. While COVID-19 patients can be accurately determined using Rapid Antigen and PCR tests, these tests are costly, time-consuming, invasive, and uncomfortable. Further, they should be performed in a specialized environment despite showing the COVID-19 symptoms such as fever, cough, rapid heart rate, shortness of breath, and low blood oxygen saturation level. To this end, this study aims to automatically identify, and track the COVID-19 suspects in real-time by embedding smart sensors to face masks. The mask was developed to gather the data related to five major symptoms of COVID-19: body temperature, cough, heart rate, breathing pattern, and blood oxygen level. Data collected using smart sensors were used to identify and track COVID-19 suspects using Deep Neural Networks, the Internet of Things (IoT), and Artificial Intelligence (AI). Yielded results showed the proposed mask can identify COVID-19 suspects 92% accurately.eniMaskIoT-based IntelligentIdentifyTrack COVID-19SuspectsArticle10.1109/SmartIoT55134.2022.00011