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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
computer science
Real-time adaptive automation system based on identification of operator functional state in simulated process control operations
IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, Volume 40, No. 2, Article 5345873, Year 2010
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Description
This paper proposes a new framework for the online monitoring and adaptive control of automation in complex and safety-critical humanmachine 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. © 2009 IEEE.
Authors & Co-Authors
Ting, Chinghua
Taiwan, Chiayi
National Chiayi University
Mahfouf, Mahdi
United Kingdom, Sheffield
The University of Sheffield
Nassef, Ahmed M.
Egypt, Tanta
Faculty of Engineering
Linkens, Derek Arthur
United Kingdom, Sheffield
The University of Sheffield
Panoutsos, G.
United Kingdom, Sheffield
The University of Sheffield
Nickel, Peter
Germany, Berlin
Deutsche Gesetzliche Unfallversicherung
Roberts, Adam Charles
United Kingdom, Oxford
University of Oxford
Hockey, Robert J.
United Kingdom, Sheffield
The University of Sheffield
Statistics
Citations: 95
Authors: 8
Affiliations: 5
Identifiers
Doi:
10.1109/TSMCA.2009.2035301
ISSN:
10834427
Research Areas
Environmental