ABSTRACT
COVID-19 Pandemic affects daily life and the global economy. The COVID-19 virus can be spread by small liquid particles, which can be filtered using a face mask. Wearing masks in public areas is an excellent approach to preventing illness. As a result, mask detection is necessary to stop the spread of the disease before a person enters the facility. Regarding Single Shot Multibox Detector-MobileNetV2 (SSD-MobileNetV2) was used in this research to build tools to detect and monitor unmasked people in the facility or working rooms that consist of many people. In this paper, we showed the experimental performance of SSDMobileNetv2 based on an application that runs on an edge device to detect unmasked people in the room and compromise with very high accuracy of 97% in rooms smaller than 16 square meters, which is sufficient to detect the wearing of masks in public places or various locations. © 2022 IEEE.
ABSTRACT
This article provides an overview of the design and methodology of the "Corona lockdown: how fit are you?" (CLOFIT) study, including the questionnaires and scales that were included in the online survey. The aim of the CLOFIT study was to investigate the psychosocial and health consequences of the coronavirus disease 2019 (COVID-19) pandemic in the Netherlands. The survey was conducted among the Dutch population to collect data on immune fitness and the psychological and health consequences of the 2019 coronavirus disease (COVID-19) pandemic lockdown in the Netherlands. The CLOFIT dataset contains measures from N = 1910 participants and is broadly representative of the Dutch general population. The dataset represents both sexes, a range of ages including the elderly, different education levels, and ethnic backgrounds. The cohort also includes people with a diverse health status and range of medication use.