Assessing Fitness to Drive: An IoT-Based System Integrating Driver Emotions and Visual Distraction Metrics

In this paper, we propose an integrated IoT-based framework for continuously assessing driver fitness by combining physiological, emotional, and visual distraction data. Our system utilizes wearable devices to capture heart rate data and in-vehicle cameras to monitor driver behavior. By combining these diverse data streams, we compute a Fitness to Drive (FtD) index that provides real-time feedback on driver performance. Experimental evaluations using a driving simulator demonstrate that the proposed system reliably detects critical levels of distraction and arousal, which are known to significantly impair driving performance. The integration of established techniques for emotion assessment and distraction detection further enhances the system's robustness. Overall, our results highlight the potential for advanced driver monitoring technologies to improve road safety and contribute to more sustainable mobility practices.