Adaptive aiding with an individualized workload model based on psychophysiological measures

Abstract

Potential benefits of technology such as automation are oftentimes negated by improper use and application. Adaptive systems provide a means to calibrate the use of technological aids to the operator’s state, such as workload state, which can change throughout the course of a task. Such systems require a workload model which detects workload and specifies the level at which aid should be rendered. Workload models that use psychophysiological measures have the advantage of detecting workload continuously and relatively unobtrusively, although the inter-individual variability in psychophysiological responses to workload is a major challenge for many models. This study describes an approach to workload modeling with multiple psychophysiological measures that was generalizable across individuals, and yet accommodated inter-individual variability. Under this approach, several novel algorithms were formulated. Each of these underwent a process of evaluation which included comparisons of the algorithm’s performance to an at-chance level, and assessment of algorithm robustness. Further evaluations involved the sensitivity of the shortlisted algorithms at various threshold values for triggering an adaptive aid.