Leveraging Deep Learning and IoT for Monitoring COVID19 Safety Guidelines Within College Campus

The widespread coronavirus pandemic 2019 (COVID-19) has brought global emergency with its deadly spread to roundabout 215 countries, and about 4,448,082 Active cases along with 535,098 deaths globally as on July 5, 2020 [1]. The non-availability of any vaccine and low immunity against COVID19 upsurges the exposure of human beings to this virus. In the absence of any vaccine, WHO guidelines like social distancing, wearing masks, washing hands and using sanitizers is the only solution against this pandemic. However, there is no idea when the pandemic situation that the world is going through will come to an end, we can take a breath of relief that someday we will surely go back to our colleges. Although having students wait in line to be screened for COVID19 symptoms may prove logistically challenging. Enthused by this belief, this paper proposes an IoT and deep learning-based framework for automating the task of verifying mask protection and measuring the body temperature of all the students entering the campus. This paper provides a human-less screening solution using a deep learning model to flag no facemasks on students entering the campus and non-contact temperature sensor MLX90614 to detect elevated body temperatures to reduce the risk of exposure to COVID19.