| Sumario: | Accurate assessment of operator mental workload (MWL) is critical for ensuring safety in closed-cabin environments, yet traditional contact-based sensors are intrusive.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.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.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.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.
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