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

TLDR

The framework builds on assessing operator functional state via psychophysiological measures. The paper proposes a framework for online monitoring and adaptive control of automation in safety‑critical human‑machine systems using psychophysiological markers of mental stress. The framework uses an adaptive fuzzy model that links heart‑rate variability and task load index to optimal performance, predicts operator stress indicators online, and a fuzzy decision maker adjusts automation level within a real‑time architecture. Offline experiments validated the model, and volunteer studies demonstrated promising performance gains.

Abstract

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.