Providing Contextual Assistance in Response to Frustration in Visual Analytics Tasks

This paper proposes a method for helping users in visual analytic tasks by using machine learning to detect and respond to frustration and provide appropriate recommendations and guidance. We have collected an emotion dataset from 28 participants carrying out intentionally difficult visualization tasks and used it to build an interactive frustration state detection model which detects frustration using data streaming from a small wrist-worn skin conductance device and eye tracking. We present a work-in-progress design exploration for interventions appropriate to different intensities of frustrations detected by the model. The interaction method and the level of interruption and assistance can be adjusted in response to the intensity and longevity of detected user states.