Fuzzy Logic based Identification of Operator Functional States Using Multiple Physiological and Performance Measures

Author

Zhang, Jian-Hua ; Wang, Xing-Yu ; Mahfouf, M. ; Linkens, D.A.

Author_Institution

Dept. of Autom., East China Univ. of Sci. & Technol., Shanghai

Volume

1

fYear

2008

fDate

27-30 May 2008

Firstpage

570

Lastpage

574

Abstract

This paper assesses the operator functional state (OFS) based on a collection of psychophysiological and performance measures. Two types of adaptive fuzzy models, 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 adaptive fuzzy modelling procedures are described and then validated using real-life data measured from such a simulated human-machine process control system.

Keywords

biology computing; electrocardiography; electroencephalography; fuzzy logic; genetic algorithms; inference mechanisms; man-machine systems; medical control systems; process control; Mamdani fuzzy model; adaptive-network-based fuzzy inference system; automation-enhanced cabin air management system; fuzzy logic based identification; genetic algorithm; human-machine process control system; operator functional states; performance measure; psychophysiological measure; Adaptive control; Fuzzy control; Fuzzy logic; Fuzzy sets; Fuzzy systems; Genetic algorithms; Man machine systems; Process control; Programmable control; Psychology; ANFIS; Operator functional states; automation-enhanced cabin air management system (aCAMS); genetic algorithm; human-machine system;

fLanguage

English

Publisher

ieee

Conference_Titel

BioMedical Engineering and Informatics, 2008. BMEI 2008. International Conference on

Conference_Location

Sanya

Print_ISBN

978-0-7695-3118-2

Type

conf

DOI

10.1109/BMEI.2008.181

Filename

4548734