A System Design for Monitoring the Violation of Home Quarantine

Aydemir, F.; Cilkaya, E.

Due to the rapid spread of mutated variants of coronavirus, and the lack of complete mass vaccination all over the world, everybody needs to strict adherence to personal hygiene and social distancing. And also, contamination of infected individuals for preventing coronavirus spreading plays a vital role. Home quarantine seems to be the best isolation solution in terms of cost and psychological health of individuals who are infected or close contacts. However, the number of quarantine violations increases day by day in many parts of the world because of the long duration of the pandemic. In this paper, a potential solution is presented for monitoring the violation of home quarantine based on the Internet of Things (IoT) and cloud computing technologies. The deep learning convolutional neural network is trained on the cloud with images of quarantine subjects for face recognition. Monitoring IoT nodes expect individuals to scan their faces at regular intervals to prevent them from leaving quarantine areas. Moreover, by measuring their body temperature regularly, unexpected rises can be detected, and this situation can be reported to health authorities. IEEE