Authors:
A. Nassef
;
C. H. Ting
;
M. Mahfouf
;
D. A. Linkens
;
P. Nickel
;
G. R. J. Hockey
and
A. C. Roberts
Affiliation:
The University of Sheffield, United Kingdom
Keyword(s):
Adaptive Automation, Operator Functional State, Cardiovascular System, Electroencepharograph, Fuzzy Systems, Genetic Algorithms, Signal Processing.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer Vision, Visualization and Computer Graphics
;
Cybernetics and User Interface Technologies
;
Devices
;
Evolutionary Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Information and Systems Security
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics
;
Real-Time Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
This paper proposes a new framework for the on-line monitoring and adaptive control of psychophysiological markers relating to humans under stress. The starting point of this framework relates to the assessment of the so-called operator functional state (OFS) using physical as well as psychological measures. An adaptive neural-fuzzy model linking Heart-Rate Variability (HRV) and Task Load Index (TLI) with the subjects’ optimal performance has been elicited and validated via a series of real-life experiments involving process control tasks simulated on an Automation-Enhanced Cabin Air Management System (aCAMS). The elicited model has been used as the basis for an on-line control system, whereby the model predictions which indicate whether the actual system is in error or not, have been used to modify the level of automation which the system may operates under.