System Design and Development for Early Detection of HFMD Symptoms Using AI and Infrared Thermometer Sensor

Abstract

Hand, Foot, and Mouth Disease (HFMD) is a highly infectious disease that mainly occurs in children under six years of age and frequently breaks out in Malaysia, making it a major public health concern in those regions. For this project, an automated screening system is introduced using a combination of Artificial Intelligence and an infrared sensor to detect symptoms at an early stage. The project uses an MLX90614 infrared sensor to scan body temperatures without direct contact, and a YOLOv8 object detection algorithm to identify skin rash on a child's hand using a digital camera. Using IoT capabilities, the real-time scan results and notifications are stored in a cloud Firebase Realtime database and can also be viewed in a Streamlit application interface. The application of this system will serve as a proactive "first line of defence" in childcare centres or kindergartens to effectively control HFMD.