A new framework for real-time adaptive fuzzy monitoring and control for humans under psychophysiological stress

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.