Abstract
Since the end of 2019, COVID-19 has been a challenge for the world, and it is expected that the world must take precautionary steps to tackle the virus spreading prior produces an efficient vaccine. Currently, most government efforts seek to avoid disseminating the coronavirus and forecast probable hot areas. The most susceptible to coronaviral infection are the healthcare staff due to their daily contact with potential patients. This article proposes a COVID-19 real-time system for tracking and identifying the suspected cases using an Internet of Things platform for capturing user symptoms and notify the authority. The proposed framework addressed four main components: (1) real-time symptom data collection via thermal scanning algorithm, (2) facial recognition algorithm, (3) a data analysis that uses artificial intelligence (AI) algorithm, and (4) a cloud infrastructure. A monitoring experiment was conducted to test three different ages, kid, middle, and older, considering the scanning distance influence compared with contact wearable sensors. The results show that 99.9% accuracy was achieved within a (500 ± 5) cm distance, and this accuracy tends to decrease as the distance the camera scanning and objects increased. The results also revealed that the scanning system's accuracy had been slightly changed as the environmental temperature dropped lower than 27 °C. Based on the high-temperature presence's simulated environment, the system demonstrated an effective and instant response via sending email and MQTT message to the person in charge of providing accurate identification of potential cases of COVID-19.