Driver fatigue and emotional distress are major contributors to traffic accidents, especially among long-haul and professional drivers, making their detection a critical area in road safety and public health. While existing research has explored various sensor-based and wearable solutions to monitor driver states, these approaches often suffer from high cost, intrusiveness, or limited scalability. Camera-based systems have recently gained attention for being less invasive and more practical for real-world deployment; however, many still depend on multiple sensors or cloud-based processing, which raise concerns around energy consumption, privacy, and integration complexity.
To address these gaps, we present a sustainable, camera-only Driver Health and Wellness Platform for real-time fatigue detection and emotional wellbeing monitoring. The system leverages a single in-cabin camera and applies lightweight, edge-optimized computer vision models to analyze facial features, eye dynamics, head orientation, and micro-expressions. This visual-only approach eliminates the need for additional hardware, reduces power consumption, and ensures privacy by processing all data locally within the vehicle.
Our solution was validated under both simulated and real driving scenarios. The results demonstrate high accuracy in detecting early signs of fatigue and emotional stress, with real-time responsiveness and minimal false positives. The platform’s sustainability, ease of deployment, and effectiveness make it well-suited for widespread adoption in commercial fleets and personal vehicles alike. This work contributes to safer, smarter, and more sustainable mobility systems by focusing on proactive driver care without compromising efficiency or privacy