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|Title||A Scenario-Based Approach for Direct Interruptablity Prediction on Wearable Devices|
|Item Subtype||Original Work|
|Status||Published in final form|
|Journal Title||Journal of Pervasive Computing and Communications|
|Abstract Text||People are subjected to a multitude of interruptions. This situation is likely to get worse as technological devices are making us increasingly reachable. In order to manage the interruptions it is imperative to predict a person’s interruptability - his/her current readiness or inclination to be interrupted. In this paper we introduce the approach of direct interruptability inference from sensor streams (accelerometer and audio data) in a ubiquitous computing setup and show that it provides highly accurate and robust predictions. Furthermore, we argue that scenarios are central for evaluating the performance of ubiquitous computing devices (and interruptability predicting devices in particular) and prove it on our setup. We also demonstrate that scenarios provide the foundation for avoiding misleading results, assessing the results’ generalizability, and provide the basis for a stratified scenario-based learning model, which greatly speeds-up the training of such devices.|
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