Assessing Continuous Operator Workload with a Hybrid Scaffolded Neuroergonomic Modeling Approach

Authors

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

Document Type

Article

Publication Date

2-1-2017

Abstract

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.

Comments

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PMID: 28146679

DOI

10.1177/0018720816672308

Source Publication

Human Factors

Recommended Citation

Borghetti, B. J., Giametta, J. J., & Rusnock, C. F. (2017). Assessing Continuous Operator Workload With a Hybrid Scaffolded Neuroergonomic Modeling Approach. Human Factors, 59(1), 134–146. https://doi.org/10.1177/0018720816672308