A context-aware do-not-disturb service for mobile devices
Yujue Qin, Tanusri Bhattacharya, Lars Kulik, James A Bailey · 2014
Modern sensor-equipped smartphones have attracted significant research interest in the pervasive community for recognizing and creating context-aware applications at a personal or community scale level. In this paper, we propose a proof of concept Do-Not-Disturb (DND) service that can a) determine a user's context relevant for DND service from the built-in smartphone sensors and b) correctly predict the DND status based on the given context such as being in a meeting, sleeping, or working at the office. In this preliminary study, we investigate whether sensor data can be clustered to represent user contexts. We use standard machine learning techniques to learn the relationship between a user's context and the corresponding DND status (available or unavailable). Given a user's current context, the DND service predicts a DND status and configures the mobile device accordingly. Our preliminary experiment demonstrates that the proposed system can achieve a prediction accuracy of up to 90% when trained with sufficient data.