Examining the robustness of sensor-based statistical models of human interruptibility

Current systems often create socially awkward interruptions or unduly demand attention because they have no way of knowing if a person is busy and should not be interrupted. Previous work has examined the feasibility of using sensors and statistical models to estimate human interruptibility in an office environment, but left open some questions about the robustness of such an approach. This paper examines several dimensions of robustness in sensor-based statistical models of human interruptibility. We show that real sensors can be constructed with sufficient accuracy to drive the predictive models. We also create statistical models for a much broader group of people than was studied in prior work. Finally, we examine the effects of training data quantity on the accuracy of these models and consider tradeoffs associated with different combinations of sensors. As a whole, our analyses demonstrate that sensor-based statistical models of human interruptibility can provide robust estimates...

• Type: Conference
• Year: 2004
• Where: CHI
• Authors: James Fogarty, Scott E. Hudson, Jennifer Lai
• Keywords: CHI 2004, Human Computer Interaction, Predictive Models, Sensor-based Interfaces, Sensor-based Statistical Models