Background Accurate assessment of operator mental workload (MWL) is critical for ensuring safety in closed-cabin environments, yet traditional contact-based sensors are intrusive. Objective This study aimed to develop and validate a fully non-contact, multimodal physiological monitoring framework for assessing levels of Mental Workload in closed-cabin environments. Methods This study employed a millimeter-wave radar and a camera to non-contactually acquire ECG, respiration, and eye movement signals from 30 participants performing a four-level monitoring task. Results Physiological features demonstrated a significant correlation with task difficulty. A Random Forest classifier built on these features achieved 83.33% accuracy in distinguishing the four MWL levels. Conclusions This study validates a fully non-contact, multimodal physiological monitoring framework, providing a practical paradigm for non-intrusive, continuous cognitive state assessment in safety-critical domains.
Wang et al. (Tue,) studied this question.