The most effective methods of preventing COVID-19 infection include maintaining physical distancing and wearing a face mask while in close contact with people in public places. However, densely populated areas have a greater incidence of COVID-19 dissemination, which is caused by people who do not comply with standard operating procedures (SOPs). This paper presents a prototype called PADDIE-C19 (Physical Distancing Device with Edge Computing for COVID-19) to implement the physical distancing monitoring based on a low-cost edge computing device. The PADDIE-C19 provides real-time results and responses, as well as notifications and warnings to anyone who violates the 1-m physical distance rule. In addition, PADDIE-C19 includes temperature screening using an MLX90614 thermometer and ultrasonic sensors to restrict the number of people on specified premises. The Neural Network Processor (KPU) in Grove Artificial Intelligence Hardware Attached on Top (AI HAT), an edge computing unit, is used to accelerate the neural network model on person detection and achieve up to 18 frames per second (FPS). The results show that the accuracy of person detection with Grove AI HAT could achieve 74.65% and the average absolute error between measured and actual physical distance is 8.95 cm. Furthermore, the accuracy of the MLX90614 thermometer is guaranteed to have less than 0.5 °C value difference from the more common Fluke 59 thermometer. Experimental results also proved that when cloud computing is compared to edge computing, the Grove AI HAT achieves the average performance of 18 FPS for a person detector (kmodel) with an average 56 ms execution time in different networks, regardless of the network connection type or speed.
Keywords: COVID-19; computer vision; edge computing; physical distancing; thermometer.