A Covid-19 viral transmission prevention system for embedded devices utilising deep learning

The coronavirus pandemic (COVID-19) has created an urgent need for different monitoring systems to prevent viral transmission because of its severity and contagious aspect. This paper proposes design and implementation of a hardware-software solution that uses supervised machine learning algorithms to examine an individual and determine if he/she poses a viral transmission danger. The solution proposed was developed utilising an ARM embedded device along with different sensors to detect and monitor COVID-19 symptoms and, at the same time, to enforce wearing of a mask by using deep learning computer vision. © 2021 IEEE.