Summary
The primary objective of the research supported by this award is to inform real-time adaptive automation interventions in safety-critical system operations by leveraging cognitive workload predictions and integrating operator emotional state information. The research seeks to address two critical limitations in current cognitive workload modeling: (1) unreliable ground-truth labeling; and (2) classification model dependence on extensive offline training. By capturing multi-source physiological signals and context-dependent emotional state information, the project looks to formulate a new cogni
