title: Comparison between artificial neural network and multilinear regression models in an evaluation of cognitive workload in a flight simulator.
abstract: In this study, the performances of artificial neural network (ANN) analysis and multilinear regression (MLR) model-based estimation of heart rate were compared in an evaluation of individual cognitive workload. The data comprised electrocardiography (ECG) measurements and an evaluation of cognitive load that induces psychophysiological stress (PPS), collected from 14 interceptor fighter pilots during complex simulated F/A-18 Hornet air battles. In our data, the mean absolute error of the ANN estimate was 11.4 as a visual analog scale score, being 13-23% better than the mean absolute error of the MLR model in the estimation of cognitive workload.
authors.affiliation: Medical Engineering R & D Center, Oulu University of Applied Sciences, Finland. manne.hannula@oamk.fi
authors.forename: Manne
authors.initials: M
authors.lastname: Hannula
authors.forename: Kerttu
authors.initials: K
authors.lastname: Huttunen
authors.forename: Jukka
authors.initials: J
authors.lastname: Koskelo
authors.forename: Tomi
authors.initials: T
authors.lastname: Laitinen
authors.forename: Tuomo
authors.initials: T
authors.lastname: Leino
mesh_terms: D001359:Aviation; D003071:Cognition; D003198:Computer Simulation; D004562:Electrocardiography; D006801:Humans; D016571:Neural Networks, Computer; D011601:Psychophysics; D013315:Stress, Psychological; D016526:Workload