Title: Classifying visual fatigue severity based on neurophysiological signals and psychophysiological ratings

Authors: Hanniebey D. Wiyor; Celestine A. Ntuen; Joseph D.W. Stephens; Steven Jiang; Zongliang Jiang

Addresses: Department of Industrial and Systems Engineering, North Carolina A&T State University, 1601 East Market Street, 406 McNair Hall, Greensboro, NC 27411, USA ' Department of Industrial and Systems Engineering, North Carolina A&T State University, 1601 East Market Street, 406 McNair Hall, Greensboro, NC 27411, USA ' Department of Psychology, North Carolina A&T State University, 1601 East Market Street, 364 Science Building, Greensboro, NC 27411, USA ' Department of Industrial and Systems Engineering, North Carolina A&T State University, 1601 East Market Street, 406 McNair Hall, Greensboro, NC 27411, USA ' Department of Industrial and Systems Engineering, North Carolina A&T State University, 1601 East Market Street, 406 McNair Hall, Greensboro, NC 27411, USA

Abstract: This paper presents a neuroergonomics study of visual fatigue associated with visual display tasks. Symptoms of visual fatigue may include tiredness, headaches, eye soreness, eye aches, discomfort when the eyes are open, difficulties in focusing, and blurred vision. These symptoms can be caused by demands on the visual functions, such as focusing and converging of the eyes. A feed forward artificial neural network (FF-ANN) is used to classify visual fatigue based on the subjects' neurophysiological signals and psychophysiological ratings on a simulated sickness questionnaire (SSQ) scale. Inclusive of all data, a classification accuracy of 83.33% was obtained (with 70% for training, 15% for validating, and 15% for testing from nine subjects), 90.4% accuracy was obtained using eye response data, 86.78% accuracy with eye and EEG response data, and, 84.93% with eye and hemodynamics response data. The FF-ANN model classifies about 16.7% and 33.33% of severe and moderate ratings based on medium to difficult simulated air traffic control tasks on the SSQ scale.

Keywords: artificial neural networks; ANNs; visual display tasks; visual fatigue; neurophysiological signals; psychophysiological ratings; fatigue severity; neuroergonomics; ergonomics; eye response; EEG response; hemodynamics response; simulation; air traffic control tasks; electroencephalograms.

DOI: 10.1504/IJHFE.2013.055982

International Journal of Human Factors and Ergonomics, 2013 Vol.2 No.1, pp.11 - 32

Received: 09 Nov 2012
Accepted: 25 Apr 2013

Published online: 23 Aug 2013 *

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