OPERATOR FUNCTIONAL STATE CLASSIFICATION USING MULTIPLE PSYCHOPHYSIOLOGICAL FEATURES IN AIR TRAFFIC CONTROL TASK.

Author(s)
Wilson, G.F. & Russell, C.A.
Year
Abstract

This research studied 2 classifiers to determine their ability to discriminate among 4 levels of mental workload during a simulated air traffic control task using psychophysiological measures. Data from 7 air traffic controllers was used to train and test artificial neural network (ANN) and stepwise discriminant classifiers. When the 2 task difficulty manipulations were tested separately, the percentage correct classifications were between 84-88%. Feature reduction using saliency analysis for the ANNs resulted in a mean of 90% correct classification accuracy. Considering the data as a 2-class problem, acceptable load vs. overload, resulted in almost perfect classification accuracies, with mean percentage correct of 98%. In applied situations, the most important distinction among operator functional states would be to detect mental overload situations. These results suggest that psychophysiological data is capable of such discriminations with high levels of new and modified systems and adaptive aiding.

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Publication

Library number
TRIS 00967028
Source

Human Factors. 2003. Fall 45(3) Pp381-389 (5 Tab., Refs.)

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This publication is one of our other publications, and part of our extensive collection of road safety literature, that also includes the SWOV publications.