IoT Based Smart Classroom With Face Mask Detection for Post COVID-19 New-Normal Activity

The number of Covid-19 cases has decreased, with vaccines and the implementation of health protocols. Currently, people are starting to carry out face-to-face activities such as in schools, offices, shopping centers or other public places. However, entering the new normal activities, new variants of Covid19 may be formed and spread, therefore people still have to implement and maintain the health protocols, including the use of face mask in the offices or other public places. This study design the smart room system equipped with face mask detection to ensure the implementation of health protocols and air quality smart system to monitor the CO2 levels, temperature, and humidity, as well as disinfecting the room regularly with UV-C. Face Mask Detection system is developed in Raspberry, where the images from camera are processed with machine learning using MobileNetv2 for image classification method with Python libraries TensorFlow and Keras. The implementation results show that face mask detection able to process up to five people in one frame, with a maximum distance of 220cm from the camera. Air quality monitoring system also works well in detecting and classifying air parameters with error reading is under 5% and displaying them on the website application with average delay is about 4 seconds.