A MICROCONTROLLER-DRIVEN ENTRANCE GATE TO COMBAT RESPIRATORY VIRUS SPREAD

Abstract

Respiratory illnesses, including COVID-19, continue to be a significant public health concern worldwide. In this regard, public health organizations provide preventative measures, such as wearing masks, and practicing good hand hygiene, to help control the spread of respiratory illnesses. However, existing preventive measures may not be fully effective in ensuring compliance, especially in dispersed economic conditions, leading to continued risks of respiratory virus spread in public spaces. To address this challenge, this study proposes a microcontroller-driven system designed to monitor and regulate entry into public spaces, aiming to reduce the transmission of respiratory illnesses. The system employs a camera, a temperature sensor and an ultrasonic sensor to detect face mask usage, measure body temperature, and track the distance of hands from the sensor for automatic handwashing. Using deep learning method to measure accuracy rates of 0.90, 0.89, 0.89, and 0.89 for detecting face masks, precision, recall, and F1 score, respectively, and an accuracy of 99.18% in measuring body temperature. The system has the potential to enhance public safety significantly. The automatic door opening feature, triggered only when a person is wearing a mask, has an average body temperature, and has washed their hands automatically, adds to the system's efficacy. The system's ability to detect and respond to non-compliance with safety measures can help promote adherence to public health guidelines and reduce the risk of infection. This study's findings demonstrate the developed system's high potential to contribute to public safety in the era of respiratory viruses.