IMU-Based Early Warning System for Driver Drowsiness Detection via Head Movement Analysis - Nurnaningsih, Desi; Kuswowo Adi; Bayu Surarso (Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah, 2025)
The high incidence of road accidents caused by human error—accounting for approximately 69.7% of all motor vehicle accidents in Indonesia—demonstrates the urgent need for an effective driver monitoring system. One critical factor contributing to human error is driver drowsiness, which can be observed through behavioral indicators such as abrupt changes in head position. This study aims to develop a real-time early warning system for detecting driver drowsiness based on head movement patterns using a wearable device equipped with the MPU-6050 GY-521 accelerometer sensor. The system monitors acceleration on the X, Y, and Z axes and identifies drowsiness when simultaneous changes exceed predefined thresholds. A drowsiness event is characterized by a rapid head displacement, occurring within approximately 18–20 milliseconds. The thresholds applied for detection are 1.0g for the X axis, 3.5g for the Y axis, and 0.5g for the Z axis. In ten test scenarios simulating drowsy head movements, the system successfully identified seven instances, resulting in a detection accuracy of 70%. The novelty of this approach lies in its lightweight, non-intrusive design and its ability to function independently of lighting conditions, making it a practical solution for real-time driver safety enhancement.