操作员状态监控与下一代自适应自动化

Operator state monitoring (OSM) increasingly combines physiological, behavioral, performance, and contextual evidence to inform adaptive human-machine systems. Yet detecting a change in operator condition does not establish its psychological meaning, its prospective operational consequences, or whether automation should intervene. Drawing on adaptive aiding, physiological computing, and human–autonomy teaming, this Conceptual Analysis proposes a six-layer architecture comprising multimodal sensing, signal-quality management, individualized baseline and context modeling, cognitive-state inferen