Internet of Things (IoT) based Drowsiness Detection and Intervention System

Abstract

This study aimed to develop a non-intrusive smart monitoring system that could identify and prevent drowsy driving, reducing the risk of accidents. The study developed a system that uses video processing to measure the Euclidean distance of the eye and an eye aspect ratio (EAR) in order to detect drowsiness. The system employed face recognition to accurately identify the driver's eye aspect ratio. An Internet of Things (IoT) module used for remote assessment of the driver's drowsiness response in real-time. If the driver is in a drowsy state, the system sends an alert/warning to the driver and relevant authorities. In addition, if a crash occurs, the system sends a warning message with the location of the collision. The system was tested on 20 participants, achieving an overall eye detection accuracy of 99.98% (with glasses), 99.89% (without glasses), and a drowsiness detection accuracy of 98.05% (with glasses) and 99.05% (without glasses). This system has the potential to be implemented in a variety of driving applications, where expensive technologies are often difficult to adopt.