IoT and Machine Learning-Based Covid-19 Healthcare Monitoring System Using Face Recognition

After facing the horrible COVID-19 pandemic, steadily life is getting back to normal again. This pandemic came with opportunities as well, especially for researchers to come out with novel ideas and handle the situation. Many researchers have contributed with their dedicated research work with the help of recent technology to overcome similar circumstances. This paper presents a novel idea for proper monitoring and detecting normal/abnormal health using AI-based models. Proper monitoring and detection of symptoms are essential to ensure the health of members. This model is devised using several IoTs components and various ML (Machine Learning) techniques have been used to get comparative enhanced results. The hardware used Raspberry Pi 4 model B, which is the main hardware connected to several sensors like MLX906014 non-contact thermal sensor and MAX30100 pulse oximeter and heart rate sensor to measure body temperature without contact and to calculate the level of oxygen in the blood and measuring pulse rate respectively. Additionally, a Camera module for facilitating face recognition features for devices has been used. AnAlert will be sent to Admin if someone has an abnormal temperature and oxygen level. The Firebase database is used to store information and it gets updated in real-time. People's health history can be further analyzed through graphs for visualization and monitored by the administrator.