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

  • Authors:

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

  • Affiliations:

  • Medical Engineering R & D Center, Oulu University of Applied Sciences, Finland and Department of Otorhinolaryngology, Institute of Clinical Medicine, University of Oulu, Finland;Department of Otorhinolaryngology, Institute of Clinical Medicine, University of Oulu, Finland;Department of Clinical Physiology and Nuclear Medicine, University of Kuopio and Kuopio University Hospital, Finland;Department of Clinical Physiology and Nuclear Medicine, University of Kuopio and Kuopio University Hospital, Finland;Department of Otorhinolaryngology, Institute of Clinical Medicine, University of Oulu, Finland and Air Force Academy, The Finnish Air Force, Finland

  • Venue:
  • Computers in Biology and Medicine
  • Year:
  • 2008

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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.