Christina Rusnock

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OBJECTIVE We aimed to predict operator workload from neurological data using statistical learning methods to fit neurological-to-state-assessment models. BACKGROUND Adaptive systems require real-time mental workload assessment to perform dynamic task allocations or operator augmentation as workload issues arise. Neuroergonomic measures have great(More)
Models for evaluating changes in human workload as a function of task allocation between humans and automation are investigated. Specifically, SysML activity diagrams and IMPRINT workload models are developed for a tablet-based game with the ability to incorporate automation. Although a first order model could be created by removing workload associated with(More)
Ideally, when selecting a system design or redesign , the selected option should balance workload and performance. However, previous research suggests that the relationship between workload and performance is nonlinear, thus achieving one, does not imply achieving the other. To ensure that a system will meet performance and workload goals, system designers(More)
This study investigates the effects of reduced automation reliability rates on human-automation team performance. Specifically, System Modeling Language (SysML) activity diagrams and Improved Performance Research Integrated Tool (IMPRINT) models are developed for a tablet-based game which includes an automated teammate. The baseline model uses previously(More)