2025/03/03 by Odikayor-Ogbomo, I. F., Nwaogwugwu, G. C., Ukueje, O. P. +2
#Android #Detection #Drowsiness #Eye blinks #Fatigue #Nodding #Yawning
paper · doi:10.60787/tnamp.v21.469
The occurrence of vehicle accidents due to driver drowsiness has become a major concern globally. This study presents the development of an Android-based drowsiness detection system utilizing smartphone front cameras combined with machine learning algorithms to track facial features such as eye closure, yawning, and head movement. The Google Firebase Machine Learning (ML) Kit was employed for real-time facial detection, and the Object-Oriented Analysis and Design Methodology (OOADM) was used for system development. The system alerts drivers via visual and audio signals when drowsiness is detected. Compared to traditional physiological-based methods, this approach is non-intrusive, cost-effective, and easily deployable. The study also evaluates system performance under varying lighting conditions and with drivers wearing glasses.