Using cardiovascular measures for adaptive automation

Adaptive automation is the term that describes a technologically equipped working environment that adapts to the user. In the field of adaptive automation technological systems are developed that are flexible and can adapt to specific needs and requirements of an individual. The idea is that the whole system (man and machine together) functions best when (human) workload is kept at an optimum level. It must be prevented that the whole task is automated, which has as effect that the human driver cannot quickly and appropriately intervene when something goes wrong with the automated system. However, it must also be prevented that workload is too high for long stretches of time and that fatigue and loss of concentration will not occur. To make it possible to keep workload at an adequate level it is necessary to be able to measure it. A suitable way of determining mental workload is with the aid of physiology, in particular with the aid of heart rate, blood pressure and respiration rate. In the literature, next to a lot of understanding, there is also some confusion about the relationship between mental effort and psychophysiological response. In this thesis, a number of ambiguities about this relationship are explained on the basis of a distinction made between state-related (or compensatory) effects and short-term effects that are probably more directly linked to changes in task demand. The state-related effects are attempts of the body to recover from prolonged effort and to return to an equilibrium situation. The short term effects arise because the body responds to instantaneous differences in workload when there is a need for energy supply in the body. A number of experiments in an ambulance control room simulation and driving simulator have been conducted. On the basis of these experiments a new method has been developed that provides more insight into current workload. This method also lends itself for use in adaptive automation. It can be said that the state effects overshadow the effects caused by current workload. This means that effects of workload (changes) are not visible. The solution described in this thesis is an analysis method based on short-term cardiovascular measurements. With this method differences in heart rate, heart rate variability, and other cardiovascular measures due to changes in task demand can still be seen in the short-term response patterns, despite the presence of the state-related effects. The short-term analysis is based on a time-frequency method in which the spectral power of the cardiovascular measurements is calculated in time segments of 30 seconds. The method has been tested in different experiments in the ambulance control room simulation and a driving simulator.The conclusion is that differences found in and between studies that were confusing at first can be explained by looking at the compensating effect of the blood pressure control system. Not properly identifying the effects of this control system makes the interpretation of direct workload-effects on cardiovascular measurements more difficult. This is particularly true for heart rate and heart rate variability. By distinguishing between effects that are directly related to the task demands and effects that are caused by the blood pressure control system, we are able to separate a number of these effects. This has led to the development of better mental workload measures that can be useful in adaptive automation, where adjustments of task demands can be based on these improved measures.

Dissertation: http://irs.ub.rug.nl/ppn/387641165