Impact of Automation and Task Load on Unmanned System Operator’s Eye Movement Patterns

  • Cali M. Fidopiastis
  • Julie Drexler
  • Daniel Barber
  • Keryl Cosenzo
  • Michael Barnes
  • Jessie Y. C. Chen
  • Denise Nicholson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5638)

Abstract

Eye tracking under naturalistic viewing conditions may provide a means to assess operator workload in an unobtrusive manner. Specifically, we explore the use of a nearest neighbor index of workload calculated using eye fixation patterns obtained from operators navigating an unmanned ground vehicle under different task loads and levels of automation. Results showed that fixation patterns map to the operator’s experimental condition suggesting that systematic eye movements may characterize each task. Further, different methods of calculating the workload index are highly correlated, r(46) = .94, p = .01. While the eye movement workload index matches operator reports of workload based on the NASA TLX, the metric fails on some instances. Interestingly, these departure points may relate to the operator’s perceived attentional control score. We discuss these results in relation to automation triggers for unmanned systems.

Keywords

Adaptive Automation Unmanned Ground Systems Eye Tracking Workload 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Cali M. Fidopiastis
    • 1
  • Julie Drexler
    • 1
  • Daniel Barber
    • 1
  • Keryl Cosenzo
    • 2
  • Michael Barnes
    • 2
  • Jessie Y. C. Chen
    • 2
  • Denise Nicholson
    • 1
  1. 1.Applied Cogntion and Training in Immersive Virtual Enviroments (ACTIVE) Laboratory Institute of Simulation and Training (IST)University of Central FloridaUSA
  2. 2.U.S. Army Research Laboratory (ARL) - Human Research & Engineering DirectorateUSA

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