A model for human interruptability: experimental evaluation and automatic estimation from wearable sensors

Abstract

For the estimation of user interruptability in wearable and mobile settings, we propose in (N. Kern et al., 2003) to distinguish between the users' personal and social interruptability. In this paper, we verify this thesis with a user study on 24 subjects. Results show that there is a significant difference between social and personal interruptability. Further, we present a novel approach to estimate the social and personal interruptability of a user from wearable sensors. It is scalable for a large number of sensors, contexts, and situations and allows for online adaptation during run-time. We have developed a wearable platform, that allows to record and process the data from a microphone, 12 body-worn 3D acceleration sensors, and a location estimation. We have evaluated the approach on three different data sets, with a maximal length of two days.