A hybrid machine learning and IoT system for driver fatigue monitoring in connected electric vehicles

Driver fatigue remains a critical factor in road accidents, particularly in long duration or cognitively demanding driving scenarios. This study presents a comprehensive, low cost, and real time system for monitoring driver health and electric vehicle status through physiological signal analysis. By integrating heart rate, eye movement, and breathing rate sensors, both simulated and real, this hybrid framework detects signs of fatigue using machine learning classifiers trained on publicly available datasets including OpenDriver, DriveDB, MAUS, YawDD, TinyML, and the Driver Respiration Dataset. The system architecture combines Arduino based hardware, cloud integration via Microsoft Azure, and advanced classification and anomaly detection algorithms such as Random Forest and Isolation Forest. Evaluation across diverse datasets revealed robust fatigue detection capabilities, with OpenDriver achieving 97.6% cross validation accuracy and 95.8% F1-score, while image and respiration-based models complemented the electrocardiogram-based analysis. These results demonstrate the feasibility of affordable, multimodal health monitoring in EVs, offering a scalable and deployable solution for enhancing road safety.