2025/05/09 by Olivia Nocentini, Marta Lagomarsino, Nocentini, Olivia +11 · 1 citation
Computer Science · Psychology · #Artificial Intelligence (cs.AI) #Color perception and design #Computer Vision and Pattern Recognition (cs.CV) #Ergonomics and Musculoskeletal Disorders #FOS: Computer and information sciences #Gaze Tracking and Assistive Technology
paper · pdf · doi:10.48550/arxiv.2505.08800
openalex publication_date 2025/05/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Driver fatigue poses a significant challenge to railway safety, with traditional systems like the dead-man switch offering limited and basic alertness checks. This study presents an online behavior-based monitoring system utilizing a customised Directed-Graph Neural Network (DGNN) to classify train driver's states into three categories: alert, not alert, and pathological. To optimize input representations for the model, an ablation study was performed, comparing three feature configurations: skeletal-only, facial-only, and a combination of both. Experimental results show that combining facial and skeletal features yields the highest accuracy (80.88%) in the three-class model, outperforming models using only facial or skeletal features. Furthermore, this combination achieves over 99% accuracy in the binary alertness classification. Additionally, we introduced a novel dataset that, for the first time, incorporates simulated pathological conditions into train driver monitoring, broadening the scope for assessing risks related to fatigue and health. This work represents a step forward in enhancing railway safety through advanced online monitoring using vision-based technologies.