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

  • Authors:

  • James Fogarty;Scott E. Hudson;Jennifer Lai

  • Affiliations:

  • Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;IBM T.J. Watson Research Center, Hawthorne, NY

  • Venue:
  • Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
  • Year:
  • 2004

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Abstract

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 for a variety of office workers in a range of circumstances, and can do so with accuracy as good as or better than people. Integrating these models into systems could support a variety of advances in human computer interaction and computer-mediated communication.