AI-based Prevention Embedded System Against COVID-19 in Daily Life

Since the prevalence of COVID-19, the virus has spread all over the world. A large number of people have been infected and died, and countries all over the world have experienced the most severe crisis. Vaccination can effectively resist the virus. However, it does not mean that vaccination can suppress virus spread completely. Hence, wearing a mask correctly and keeping the social distance become emergency methods for reducing the risk of infection. This paper proposes an AI-based prevention embedded system against COVID-19 in daily life by keeping the function of the emergency method. The system consists of two functions, mask-wearing-status detection, and social-distance measurement. Mask-wearing-status detection employs YOLO and realizes the detection and classification of three mask-wearing-status, corrected-wearing, non-corrected-wearing, and without-wearing. Social-distance measurement equips a depth camera for measuring the distance between humans. The system gives an alert when people do not wear a mask correctly or do not keep their social distance. The system has been implemented on Jetson-nano, a compact embedded board, and achieves 6 f ps. The experimental results also show that the mask-wearing-status detection accuracy archives at 93.21% and the error of social-distance measurement are within 3 cm, which have proved the effectiveness of the system.