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)
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)
Fire station turn-out-time is vitally important to firefighters' ability to provide lifesaving services. Turn-out-time consists of two phases: first, dispatch by a controller in a 911 call center; second, turn-out, in which controllers notify the responders, and responders prepare for the emergency by donning their personal protective equipment and boarding(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)
It is important to recognize the human as an integral part of the system and its performance during systems development. However, systems engineers (SEs) currently fail to properly integrate the human into the system. This research aimed to improve system models by incorporating the human into modeling efforts. A set of Model-Based Systems Engineering(More)