Real-Time Adaptive Automation System Based on Identification of Operator Functional State in Simulated Process Control Operations

This paper proposes a new framework for the online monitoring and adaptive control of automation in complex and safety-critical human-machine systems using psychophysiological markers relating to humans under mental stress. The starting point of this framework relates to the assessment of the so-cal...

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Published inIEEE transactions on systems, man and cybernetics. Part A, Systems and humans Vol. 40; no. 2; pp. 251 - 262
Main Authors Ching-Hua Ting, Mahfouf, M., Nassef, A., Linkens, D.A., Panoutsos, G., Nickel, P., Roberts, A.C., Hockey, G.
Format Journal Article
LanguageEnglish
Published IEEE 01.03.2010
Subjects
Adaptive Automation (AA)
Adaptive control
Adaptive systems
Automation
Computerized monitoring
Fuzzy
Fuzzy set theory
Fuzzy systems
Human
Humans
Indicators
man-machine systems
Mathematical models
neural-fuzzy modeling and control
operator functional state (OFS)
Operators
Process control
Programmable control
Psychology
psychophysiology
Real time systems
signal processing
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ISSN1083-4427
1558-2426
DOI10.1109/TSMCA.2009.2035301

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Summary:This paper proposes a new framework for the online monitoring and adaptive control of automation in complex and safety-critical human-machine systems using psychophysiological markers relating to humans under mental stress. The starting point of this framework relates to the assessment of the so-called operator functional state using psychophysiological measures. An adaptive fuzzy model linking heart-rate variability and task load index with the subjects' optimal performance has been elicited and validated offline via a series of experiments involving process control tasks simulated on an automation-enhanced Cabin Air Management System. The elicited model has been used as the basis for an online control system via the predictions of the system performance indicators corresponding to the operator stressful state. These indicators have been used by a fuzzy decision maker to modify the level of automation under which the system may operate. A real-time architecture has been developed as a platform for this approach. It has been validated in a series of human volunteer studies with promising improvement in performance.
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ISSN:1083-4427
1558-2426
DOI:10.1109/TSMCA.2009.2035301