Abstract
Operator fatigue, stress, delayed response, and abnormal operation may compromise the safety of unmanned intelligent system operation in port-security scenarios. This study proposes a bio-inspired engineering architecture for physiological–behavioral fusion risk assessment of operators. The architecture is motivated by homeostatic regulation, coordinated physiological–behavioral responses, selective attention, and feedback concepts; it does not aim to reproduce a biological or neural mechanism. It combines operator-specific baseline calibration, physiological and behavioral feature representation, cross-modal coupling representation, modality-level adaptive weighting, and temporal risk-state classification to identify normal, fatigue, stress, and abnormal-operation states. Physiological information was acquired using a non-invasive smart-cushion sensing system and supplementary electrodermal-activity monitoring, while behavioral information was derived from operation-platform and task-event logs. The proposed method was evaluated under representative port-operation tasks using a participant-level data split and repeated computational training-and-evaluation runs. These repeated runs were used to characterize stochastic optimization stability and were not interpreted as independent participant experiments. The results indicate that the proposed method achieved improved overall classification performance, warning-related reliability, and repeated-run stability compared with single-modal, conventional machine-learning, direct-concatenation, and temporal-fusion baselines. The findings provide pilot evidence that physiological–behavioral fusion can support operational human-factor risk-state classification under the examined port-operation conditions. Larger cross-operator and cross-scenario studies are required to establish broader generalizability.