Multiple Edge Computing Devices with Computer Vision for Social Distancing

Coronavirus disease, widely known as COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. Once infected, a person can spread the virus through their nose or mouth in small particles when they cough, sneeze, speak, or breathe. According to the World Health Organization (WHO), one way to be protected from the risk of virus infection is to stay at least 1 meter apart from others while wearing a properly filtered mask. The study aims to design and develop a multiple edge computing system with computer vision capabilities to monitor the adherence of social distancing in multiple locations and in real time. An edge computing device uses a camera to process a stream of images. Graphical Processing Unit (GPU) was utilized for faster inference processing to detect people. The person's location will undergo transformation to get a 2D perspective. Then, a distance calculation algorithm will be imposed to each pair of persons detected to detect breach of social distancing protocol. For every breach detected, location coordinates will be sent to the host database for visualization and monitoring. The use of multiple edge computing devices for computer vision application was compared to the IP camera system in monitoring multiple locations. It is found that utilization of multiple edge computing devices has significant advantages in terms of power consumption, data acquisition, image processing and inference, and setup cost. © 2022 IEEE.

Design of an Automatic Temperature Screening System for Elevated Skin Temperature with Information Logging Capability

With the outbreak of the highly-contagious SARS-CoV-2 virus and its accompanying coronavirus disease 2019 (COVID-19), many government agencies adopted contact tracing to measure and mitigate the spread of the virus. Contact tracing aims to keep track of the individual's movements and activities and identify all those who they come in contact with. This study is focused on designing a cost-effective, efficient, and accurate system for information logging and temperature screening with a complementary contact tracing feature. The system provides an automated, safe, and physical-distance-aware alternative to manual temperature measurement and data logging practiced by most commercial establishments. The system uses an Arduino and a Raspberry Pi, along with infrared temperature sensors utilizing proper calibration methods to yield temperature reading difference of 0.1-0.3 degree-Celsius taken at 10 cm distance. User identification is done by reading either specifically-registered RFID tags or system-generated identity-QR code. Temperature is subsequently read, date and time stamped, and logged into the system. This allows for automated and exact association of the user logged information with their corresponding temperature. © 2021 IEEE.

Design of a Breach Detection System for Social Distancing

The pandemic caused by the 2019 novel coronavirus introduced essential health protocols for everyone's safety. One of which is maintaining a social distance of at least 1 meter as per the guideline set by World Health Organization (WHO). Currently, most spaces were designed prior to the implementation of the social/physical distancing protocol. This project aims to design and develop a detection system utilizing closed-circuit television cameras, to identify spaces where there is a possible breach in the social distancing protocol. The system will generate discrete data to be queried for tabulation, and analysis. The system will also generate a breach map, which indicates the area in the CCTV footage where increasing breaches occur and are marked in increasing color intensity. The system utilized the YOLO V3 object detection algorithm in identifying an object to be human. The system utilized perspective transformation and Euclidean distance estimation in approximating distance for the social distancing protocol. In summary, the human detection accuracy of the system is ≃ 91%, processing at a rate of 30 frames per second in real-time. © 2021 IEEE.

Agent-based Modeling of COVID-19 Infection Rate vis-à-vis the Philippine Government Community Quarantine and Face Covering Measures

The COVID-19 pandemic has uprooted the normal lives and routines of Filipinos. Dubbed as the 'new normal,' the wearing of face masks and face shields and adherence to social distancing and the community quarantines have been enforced by the Philippine government to all its citizens. The primary concern is to slow down the spread of SARS-CoV-2 virus absent of widespread and available pharmaceutical interventions such as vaccines and therapies, in the hopes of keeping the overall healthcare system functional. In this study, the researchers use NetLogo, an agent-based modeling software to emulate such government measures on a closed population of agents and observe the effects on the spread of COVID-19. Running different simulation scenarios, the researchers find that the NetLogo model is able to predict long- term trends imposed by these government restrictions. The results show that through increasing the number of people using face coverings and restricting interactions through enforcement of community quarantines, the rise of infections can be decreased substantially. The results of our model are consistent with the Philippine government's collated pandemic data on the effect of the enforcement of restrictions to the ongoing outbreak during certain time periods in the year 2020. © 2021 IEEE.