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A Composite Cognitive Workload Assessment System in Pilots Under Various Task Demands Using Ensemble Learning

  • Hyuk OhEmail author
  • Bradley D. Hatfield
  • Kyle J. Jaquess
  • Li-Chuan Lo
  • Ying Ying Tan
  • Michael C. Prevost
  • Jessica M. Mohler
  • Hartley Postlethwaite
  • Jeremy C. Rietschel
  • Matthew W. Miller
  • Justin A. Blanco
  • Shuo Chen
  • Rodolphe J. Gentili
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9183)

Abstract

The preservation of attentional resources under mental stress holds particular importance for the execution of effective performance. Specifically, the failure to conserve attentional resources could result in an overload of attentional capacity, the failure to execute critical brain processes, and suboptimal decision-making for effective motor performance. Therefore, assessment of attentional resources is particularly important for individuals such as pilots who must retain adequate attentional reserve to respond to unexpected events when executing their primary task. This study aims to devise an expert model to assess an operator’s dynamic cognitive workload in a flight simulator under various levels of challenge. The results indicate that the operator’s cognitive workload can be effectively predicted with combined classifiers of neurophysiological biomarkers, subjective assessments of perceived cognitive workload, and task performance. This work provides conceptual feasibility to develop a real-time cognitive state monitoring tool that facilitates adaptive human-computer interaction in operational environments.

Keywords

Attentional reserve Mental workload Simulated visuomotor task Ensemble of classifiers 

Notes

Acknowledgement

The authors would like to express their appreciation for the guidance and supports provided by Kenneth T. Ham (Captain, USN) who was a naval astronaut and the chair of the Aerospace Engineering Department at the USNA, and all student aviators who were midshipmen at the USNA and volunteered to participate in the study. In addition, this research was supported by the Lockheed Martin Corporation (Bethesda, MD, USA) under grant 4-321830.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Hyuk Oh
    • 1
    Email author
  • Bradley D. Hatfield
    • 1
    • 2
  • Kyle J. Jaquess
    • 2
  • Li-Chuan Lo
    • 2
  • Ying Ying Tan
    • 1
  • Michael C. Prevost
    • 5
  • Jessica M. Mohler
    • 6
  • Hartley Postlethwaite
    • 7
  • Jeremy C. Rietschel
    • 8
  • Matthew W. Miller
    • 9
  • Justin A. Blanco
    • 7
  • Shuo Chen
    • 4
  • Rodolphe J. Gentili
    • 1
    • 2
    • 3
  1. 1.Neuroscience and Cognitive Science ProgramUniversity of MarylandCollege ParkUSA
  2. 2.Department of KinesiologyUniversity of MarylandCollege ParkUSA
  3. 3.Maryland Robotics CenterUniversity of MarylandCollege ParkUSA
  4. 4.Department of Epidemiology and BiostatisticsUniversity of MarylandCollege ParkUSA
  5. 5.Naval Survival Training InstitutePensacolaUSA
  6. 6.Midshipmen Development CenterUnited States Naval AcademyAnnapolisUSA
  7. 7.Electrical and Computer Engineering DepartmentUnited States Naval AcademyAnnapolisUSA
  8. 8.Maryland Exercise and Robotics Center of ExcellenceVA Maryland Health Care SystemBaltimoreUSA
  9. 9.School of KinesiologyAuburn UniversityAuburnUSA

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