Automatic Gate for Body Temperature Check and Masks Wearing Compliance Using an Embedded System and Deep Learning

• Main Authors: Kesuma, Rahman Indra, Fernandes, Rivaldo, Manullang, Martin Clinton Tosima
• Format: UMS Journal (OJS)
• Language: eng
• Published: Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia 2022
• Subjects: COVID-19; Gerbang Otomatis; Pemeriksanaan Suhu; Kelasifikasi Penggunaan Masker; CNN; MobileNetV2; MLX90614;
• Online Access: https://journals.ums.ac.id/index.php/khif/article/view/15205
• Summary: A new coronavirus variant known as n-Cov has emerged with a fast transmission rate. The World Health Organization (WHO) has declared the related disease or COVID-19 as a global pandemic that requires special handling. Many parties have shown efforts to reduce virus transmission by implementing health protocols and adapting a new normal lifestyle. Implementation of the health protocol creates new problems, especially in the health check at the main entrance. The officers in charge of measuring body temperature are at risk of getting infected by COVID. Such a measurement is prone to errors. This study proposed a solution to build an automatic gate system that worked based on the new normal health protocol. The system utilizes the MLX90614 contactless temperature sensor to probe body temperature. It applies deep learning implementing the Convolutional Neural Network (CNN) algorithm with the MobileNetV2 architecture as a determinant of the conditions of wearing face masks. The system is equipped with an IoT-based remote controller to control the gate. Experimental results prove that the system works well. Temperature measurement takes a response time of 20 seconds for each user with 99% accuracy for the sensor and masks classification model.