We investigated whether the 0.1-Hz component of heart rate variability (HRV) allows one to discriminate among levels of mental work stress induced by different types of tasks (diagnosticity) as well as among those induced by different levels of difficulty (sensitivity). Our 14 participants were presented 14 tasks of the Advisory Group for Aerospace Research and Development Standardized Tests for Research with Environmental Stressors battery in a repeated-measures design. Sufficient sensitivity was obtained for a discrimination between work and rest, but we found no support for a more fine-grained sensitivity. Concerning diagnosticity, only the grammatical reasoning task could be discriminated from all other tasks, indicating for this task a level of mental strain comparable to rest, which was in contrast with the results both for perceived difficulty and performance. We propose that HRV is an indicator for time pressure or emotional strain, not for mental workload, given that it seems to allow discrimination between tasks with and without pacing. Application of this research argues against using HRV as a measure of mental and especially cognitive workload, particularly where system safety or occupational risks may be at stake (e.g., when evaluating operator tasks or interface design in control room operations).s

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.