Adaptive fuzzy model of operator functional state in human-machine system: a preliminary study

  • Authors:

  • J. Zhang;M. Mahfouf;D. A. Linkens;P. Nickel;G. R. J. Hockey;A. C. Roberts

  • Affiliations:

  • Department of Automatic Control and Systems Engineering, The University of Sheffield, UK;Department of Automatic Control and Systems Engineering, The University of Sheffield, UK;Department of Automatic Control and Systems Engineering, The University of Sheffield, UK;Department of Psychology, The University of Sheffield, UK;Department of Psychology, The University of Sheffield, UK;Department of Psychology, The University of Sheffield, UK

  • Venue:
  • BIEN '07 Proceedings of the fifth IASTED International Conference: biomedical engineering
  • Year:
  • 2007

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Abstract

This paper assesses the operator functional state (OFS) based on a collection of psychophysiological (i.e., cardiovascular and EEG) and performance measures. Two types of adaptive fuzzy model, namely ANFIS (adaptive-network-based fuzzy inference system) and GA (genetic algorithm) based Mamdani fuzzy model, are employed to estimate the OFSs under a set of simulated process control tasks involved in an automation-enhanced cabin air management system (aCAMS). The fuzzy modelling procedures are described in detail. The adaptive fuzzy models are validated using real-life data measured from two well-trained collegiate participants. The preliminary simulation results implied that the overall performance of human-machine system may be improved by identifying and predicting OFSs via the proposed fuzzy models.