Assessing Continuous Operator Workload With a Hybrid Scaffolded Neuroergonomic Modeling Approach

Brett J. Borghetti, Air Force Institute of Technology
Joseph J. Giametta
Christina F. Rusnock, Air Force Institute of Technology

This article is published by Sage as U.S. federal government work in the public domain. Please attribute the article using the citation below, including DOI.


Excerpt: We aimed to predict operator workload from neurological data using statistical learning methods to fit neurological-to-state-assessment models. Adaptive systems require real-time mental workload assessment to perform dynamic task allocations or operator augmentation as workload issues arise. Neuroergonomic measures have great potential for informing adaptive systems, and we combine these measures with models of task demand as well as information about critical events and performance to clarify the inherent ambiguity of interpretation.