Real-Time Driver Monitoring Using Multi-Modal Emotion Recognition Using Machine Learning

2 Dec
Posted on Author

Authors: Vinod V. Kulkarni, Sujyothsna B, Vaishali BS, Jnanesh Kumar BN, Pruthvi Reddy k

Abstract: Road traffic safety requires continuous assessment of driver state, including fatigue and emotional distraction. This paper presents a robust real-time Driver Monitoring & Alert System, integrating multi-modal machine learning for drowsiness and emotion analytics. The system logs behavioral metrics—facial landmarks, speech volume, and micro-expressions—using edge computation and stores entries in a scalable database. A modern analytics dashboard visualizes trends, enables time-based filtering, and computes key summaries (e.g., average drowsiness, emotion distributions). Empirical results show high recognition accuracy, rapid dashboard responsiveness, and practical potential for real-world automotive deployment. The project demonstrates advances in real-time driver monitoring, combining intuitive visualization with rigorous machine learning methods.

DOI: http://doi.org/10.5281/zenodo.17797363