Performance Enhancement with Real-Time Physiologically Controlled Adaptive Aiding

Abstract

The realization of optimal system performance is the goal of both system designers and users. One critical component in attaining this goal is proper operator functioning. In contemporary systems the functional state of the operator is not considered during system operation. Degraded states of operator functioning can result from the demands of controlling complex systems, the work environment and internal operator variables. This, in turn, can lead to errors and overall suboptimal system performance. In the case of mental workload, system performance could be improved by reducing task demands during periods of operator overload. Accurate estimation of the operator's functional state is crucial to successful implementation of an adaptive aiding system. One method of determining operator functional state is by monitoring the operator's physiology. In the present study, physiological signals were used to continuously monitor subject's functional state and to adapt the task by reducing the number of subtasks when high levels of mental workload were detected. The goal was to demonstrate performance improvement with adaptive aiding. Because adaptive aiding during high mental workload has not been previously implemented its benefit has not be demonstrated. Application of adaptive aiding techniques reduced tracking task error by 44% and resource monitoring error by 33%. These results demonstrate the utility of adaptive aiding using physiological measures with artificial neural networks to determine the appropriate time to introduce the aiding.