Assessment and Classification of Cognitive Decrements Associated with High Workload and Extended Work Periods in a UAV Setting

Schmidt, Regina; Wilson, Glenn F.; Funke, Margaret; Davis, Iris; Caldwell, John A.

doi:10.21236/ada515591

Cited by 5 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The multi-RPA operation task was a PC-based supervisory control simulation of a "suppression of enemy air defense" mission (Schmidt, Wilson, Funke, Davis, & Caldwell, 2010) using keyboard and mouse controls. Participants monitored the progress of eight or 16 generic RPAs on two abutted 51 cm (diagonal) computer screens as they flew a preplanned mission.…”

Section: Methodsmentioning

confidence: 99%

Co-Adaptive Aiding and Automation Enhance Operator Performance

Christensen¹,

Estepp²

2013

View full text Add to dashboard Cite

The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS.

show abstract

Section: Methodsmentioning

confidence: 99%

Co-Adaptive Aiding and Automation Enhance Operator Performance

Christensen¹,

Estepp²

2013

View full text Add to dashboard Cite

show abstract

“…The second dataset was collected from a "suppression of enemy air defense" simulation for controlling multiple RPAs [33] (Fig. 2).…”

Section: B Data Set Ii: Rpa (High-fidelity Task Environment)mentioning

confidence: 99%

Day-to-day variability in hybrid, passive brain-computer interfaces: Comparing two studies assessing cognitive workload

Klosterman

Estepp

Monnin

et al. 2016

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

View full text Add to dashboard Cite

As hybrid, passive brain-computer interface systems become more advanced, it is important to grow our understanding of how to produce generalizable pattern classifiers of physiological data. One of the most difficult problems in applying machine learning algorithms to these data types is nonstationarity, which can evolve over the course of hours and days, and is more susceptible to changes resulting from complex cognitive function in comparison to simple, stimulus-based processes. This nonstationarity, referenced as day-to-day variability, results in the inability of many learning algorithms to generalize to new data. In previous work, we have shown that increasing the number of unique testing sessions used to form a learning set can improve the accuracy of classifying mental workload in a binary state paradigm. While this result was very promising, we did not address whether the additional discriminability was the result of a larger learning set or the uniqueness contributed by the testing sessions being spread over multiple days. Further, the simulation task used in this prior analysis was low-fidelity with respect to the task it attempted to model; whether these methods extend to more realistic task simulation environments has not been comparatively investigated. In this work, we compare these previous results to a second study, with a similar multi-day paradigm, that required participants to perform a more realistic simulation task. Comparative analysis of these two studies reveals that the improved generalization of the multi-day learning set is attributable, in large part, to the uniqueness of the multi-day paradigm. Further, this multi-day effect was also observed in the higher fidelity simulation study. These results help to validate the use of the multi-day learning set approach for improving overall system classification accuracy. Future studies should consider the use of multi-day designs for improving generalizability over other interesting dimensions.

show abstract

“…The multi-RPA operation task was a PC-based supervisory control simulation of a mission involving "suppression of enemy air defense" (Schmidt, Wilson, Funke, Davis, & Caldwell, 2010) using keyboard and mouse controls. Participants monitored the progress of 8 or 16 generic RPAs on two abutted 51-cm (diagonal) computer screens as they flew a preplanned mission.…”

Section: Apparatus and Stimulimentioning

confidence: 99%