Uav operators workload assessment by optical brain imaging technology (fnir)

Kurtulus Izzetoglu, Hasan Ayaz, James T. Hing, Patricia A. Shewokis, Scott C. Bunce, Paul Oh, Banu Onaral

Research output: Chapter in Book/Report/Conference proceedingChapter

12 Scopus citations

Abstract

The use of unmanned aerial vehicles (UAVs) is expected to increase exponentially over the next few years. UAV ground operators are required to acquire skills quickly and completely, with a level of expertise that builds the operator’s confidence in his/her ability to control the UAV under adverse conditions. As UAVs are held to increasingly higher standards of efficiency and safety, operators are routinely required to perform more informationally dense and cognitively demanding tasks, resulting in increased cognitive workloads during operation. Functional brain monitoring offers the potential to help UAV operators meet these challenges. Recent research has demonstrated the utility of near- infrared-based functional brain imaging systems (fNIRs) for the purpose of monitoring frontal cortical areas that support executive functions (attention, working memory, response monitoring). This technology provides portable, safe, affordable, noninvasive, and minimally intrusive monitoring systems with rapid application times for continuous measures of cortical activity. fNIR technology allows continuous monitoring of operators during training as they develop expertise, as well as the capacity to monitor their cognitive workload under operational conditions while controlling UAVs in critical missions. This chapter discusses the utilization of fNIR in the monitoring of a cognitive workload during UAV operation, and as an objective measure of expertise development, that is, the transition from novice to expert during operator training.

Original languageEnglish (US)
Title of host publicationHandbook of Unmanned Aerial Vehicles
PublisherSpringer Netherlands
Pages2475-2500
Number of pages26
ISBN (Electronic)9789048197071
ISBN (Print)2014944662, 9789048197064
DOIs
Publication statusPublished - Jan 1 2015

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All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science(all)
  • Mathematics(all)

Cite this

Izzetoglu, K., Ayaz, H., Hing, J. T., Shewokis, P. A., Bunce, S. C., Oh, P., & Onaral, B. (2015). Uav operators workload assessment by optical brain imaging technology (fnir). In Handbook of Unmanned Aerial Vehicles (pp. 2475-2500). Springer Netherlands. https://doi.org/10.1007/978-90-481-9707-1_22