Predicting human interruptibility with sensors

  • Authors:

  • James Fogarty;Scott E. Hudson;Christopher G. Atkeson;Daniel Avrahami;Jodi Forlizzi;Sara Kiesler;Johnny C. Lee;Jie Yang

  • Affiliations:

  • Carnegie Mellon University, Pittsburg, PA;Carnegie Mellon University, Pittsburg, PA;Carnegie Mellon University, Pittsburg, PA;Carnegie Mellon University, Pittsburg, PA;Carnegie Mellon University, Pittsburg, PA;Carnegie Mellon University, Pittsburg, PA;Carnegie Mellon University, Pittsburg, PA;Carnegie Mellon University, Pittsburg, PA

  • Venue:
  • ACM Transactions on Computer-Human Interaction (TOCHI)
  • Year:
  • 2005

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Abstract

A person seeking another person's attention is normally able to quickly assess how interruptible the other person currently is. Such assessments allow behavior that we consider natural, socially appropriate, or simply polite. This is in sharp contrast to current computer and communication systems, which are largely unaware of the social situations surrounding their usage and the impact that their actions have on these situations. If systems could model human interruptibility, they could use this information to negotiate interruptions at appropriate times, thus improving human computer interaction.This article presents a series of studies that quantitatively demonstrate that simple sensors can support the construction of models that estimate human interruptibility as well as people do. These models can be constructed without using complex sensors, such as vision-based techniques, and therefore their use in everyday office environments is both practical and affordable. Although currently based on a demographically limited sample, our results indicate a substantial opportunity for future research to validate these results over larger groups of office workers. Our results also motivate the development of systems that use these models to negotiate interruptions at socially appropriate times.