Confidence-based state estimation: A novel tool for test and evaluation of human-systems

Abstract

Test and evaluation (T&E) of complex human-in-the loop systems has been a challenge for system developers. Traditional methods for T&E rely on questionnaires given periodically in combination with task performance measures to quantify the effectiveness of a given system. This approach is inherently obtrusive and interferes with natural system interaction. Here, we propose a method to leverage unobtrusive wearable technology to create a system for continuously assessing human state. Previous efforts at this type of assessment have often failed to generalize beyond controlled laboratory environments due to increased variability in signal quality from both the wearable sensors and in human behavior. We propose a method to account for this variability using measures of confidence to create robust estimates of state capable of dynamically adapting to changes in behavior over time. We postulate that the confidence-based approach can provide high-resolution estimates of state that will augment T&E of complex systems.