Comparison between artificial neural network and multilinear regression models in an evaluation of cognitive workload in a flight simulator

Manne Hannula; Kerttu Huttunen; Jukka Koskelo; Tomi Laitinen; Tuomo Leino

doi:10.1016/j.compbiomed.2008.09.007

Comparison between artificial neural network and multilinear regression models in an evaluation of cognitive workload in a flight simulator

Comput Biol Med. 2008 Nov-Dec;38(11-12):1163-70. doi: 10.1016/j.compbiomed.2008.09.007.

Authors

Manne Hannula¹, Kerttu Huttunen, Jukka Koskelo, Tomi Laitinen, Tuomo Leino

Affiliation

¹ Medical Engineering R & D Center, Oulu University of Applied Sciences, Finland. manne.hannula@oamk.fi

PMID: 19010465
DOI: 10.1016/j.compbiomed.2008.09.007

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.

Publication types

Comparative Study
Research Support, Non-U.S. Gov't

MeSH terms

Aviation*
Cognition*
Computer Simulation*
Electrocardiography
Humans
Neural Networks, Computer*
Psychophysics
Stress, Psychological
Workload*