Direct Interruptability Prediction and Scenario-based Evaluation of Wearable Devices: Towards Reliable Interruptability Predictions.
Abraham Bernstein, Peter Vorburger, Patrice Egger · 2005
In this paper we introduce the approach of direct interruptability inference from accelerometer and audio data and show that it provides highly accurate and robust predictions. Furthermore, we argue that scenarios are central for evaluating the performance of interruptability predicting devices 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 speedsup the training of such devices.