Using physiological sensors to detect levels of user frustration induced by system delays

Abstract

In mobile computing, varying access to resources makes it difficult for developers to ensure that satisfactory system response times will be maintained at all times. Wearable physiological sensors offer a way to dynamically detect user frustration in response to increased system delays. However, most prior efforts have focused on binary classifiers designed to detect the presence or absence of a task-specific stimulus. In this paper, we make two contributions. Our first contribution is in identifying the use of variable length system response delays, a universal and task-independent feature of computing, as a stimulus for driving different levels of frustration. By doing so, we are able to make our second and primary contribution, which is the development of models that predict multiple levels of user frustration from psycho-physiological responses caused by system response delays. We investigate how incorporating different sensor features, application settings, and timing constraints impact the performance of our models. We demonstrate that our models of physiological responses can be used to classify five levels of frustration in near real-time with over 80% accuracy, which is comparable to the accuracy of binary classifiers.