James Fogarty
Scott E. Hudson
Sara Kiesler
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.
- Barker, R. G. 1968. Ecological Psychology. Stanford University Press.Google Scholar
- Begole, J. B., Tang, J. C., and Hill, R. 2003. Rhythm modeling, visualizations, and applications. In Proceedings of the ACM Symposium on User Interface Software and Technology (UIST 2003). 11--20. Google Scholar
- Begole, J. B., Tang, J. C., Smith, R. B., and Yankelovich, N. 2002. Work rhythms: Analyzing visualizations of awareness histories of distributed groups. In Proceedings of the ACM Conference on Computer Supported Cooperative Work (CSCW 2002). 334--343. Google Scholar
- Bellotti, V. and Edwards, K. 2001. Intelligibility and accountability: Human considerations in context-aware systems. Hum.-Comput. Interact. 16, 2-4, 193--212. Google Scholar
- Burges, C. J. C. 1998. A tutorial on support vector machines for pattern recognition. Data Mining Knowl. Disc. 2, 2, 121--167. Google Scholar
- CMU Sphinx: Open Source Speech Recognition. http://www.speech.cs.cmu.edu/sphinx/.Google Scholar
- Cutrell, E., Czerwinski, M., and Horvitz, E. 2001. Notification, disruption, and memory: Effects of messaging interruptions on memory and performance. In Proceedings of the IFIP Conference on Human-Computer Interaction (INTERACT 2001). 263--269.Google Scholar
- Czerwinski, M., Cutrell, E., and Horvitz, E. 2000a. Instant messaging and interruptions: Influence of task type on performance. In Proceedings of the Australian Conference on Computer-Human Interaction (OZCHI 2000). 356--361.Google Scholar
- Czerwinski, M., Cutrell, E., and Horvitz, E. 2000b. Instant messaging: Effects of relevance and time. In Proceedings of the British HCI Group Annual Conference (HCI 2000). 71--76.Google Scholar
- Dahlböck, N., Jönsson, A., and Ahrenberg, L. 1993. Wizard of Oz studies---Why and how. In Proceedings of the International Conference on Intelligent User Interfaces (IUI 1993). 193--200. Google Scholar
- Duda, R. O. and Hart, P. E. 1973. Pattern Classification and Scene Analysis. John Wiley and Sons.Google Scholar
- Erickson, T. and Kellogg, W. A. 2000. Social translucence: An approach to designing systems that support social processes. ACM Trans. Comput.-Hum. Interact. (TOCHI) 7, 1, 59--83. Google Scholar
- Feldman-Barrett, L. and Barrett, D. J. 2001. Computerized experience-sampling: How technology facilitates the study of conscious experience. Soc. Sci. Comput. Rev. 19, 175--185. Google Scholar
- Fogarty, J., Forlizzi, J., and Hudson, S. E. 2001. Aesthetic information collages: Generating decorative displays that contain information. In Proceedings of the ACM Symposium on User Interface Software and Technology (UIST 2001). 141--150. Google Scholar
- Fogarty, J., Hudson, S., and Lai, J. 2004a. Examining the robustness of sensor-based statistical models of human interruptibility. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2004). 207--214. Google Scholar
- Fogarty, J., Lai, J., and Christensen, J. 2004b. Presence versus availability: The design and evaluation of a context-aware communication client. Int. J. Hum.-Comput. Stud. (IJHCS) 61, 3. Google Scholar
- Freund, Y. and Schapire, R. 1997. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 1, 119--139. Google Scholar
- Goffmann, E. 1982. On Facework. Interaction Ritual, E. Goffmann, Ed. Random House, New York, 5--45.Google Scholar
- Hall, M. A. 2000. Correlation-based feature selection for discrete and numeric class machine learning. In Proceedings of the International Conference on Machine Learning (ICML 2000). 359--366. Google Scholar
- Hatch, M. J. 1987. Physical barriers, task characteristics, and interaction activity in research and development firms. Admin. Sci. Quart. 32, 387--399.Google Scholar
- Heiner, J. M., Hudson, S. E., and Tanaka, K. 1999. The information percolator: Ambient information display in a decorative object. In Proceedings of the ACM Symposium on User Interface Software and Technology (UIST 1999). 141--148. Google Scholar
- Horvitz, E. 1999. Principles of mixed-initiative user interfaces. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 1999). Google Scholar
- Horvitz, E. and Apacible, J. 2003. Learning and reasoning about interruption. In Proceedings of the International Conference on Multimodal Interfaces (ICMI 2003). 20--27. Google Scholar
- Horvitz, E., Breese, J., Heckerman, D., Hovel, D., and Rommelse, K. 1998. The lumiere project: Bayesian user modeling for inferring the goals and needs of software users. In Proceedings of the Conference on Uncertainty and Artificial Intelligence (UAI 1998). 256--265. Google Scholar
- Horvitz, E., Jacobs, A., and Hovel, D. 1999. Attention-sensitive alerting. In Proceeding of the Conference on Uncertainty and Artificial Intelligence (UAI 1999). 305--313. Google Scholar
- Hudson, J. M., Christensen, J., Kellogg, W. A., and Erickson, T. 2002. “I'd be overwhelmed, but it's just one more thing to do”: Availability and interruption in research management. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2002). 97--104. Google Scholar
- Hudson, S., Fogarty, J., Atkeson, C., Avrahami, D., Forlizzi, J., Kiesler, S., Lee, J., and Yang, J. 2003. Predicting human interruptibility with sensors: A wizard of Oz feasibility study. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2003). 257--264. Google Scholar
- Kohavi, R. and John, G. H. 1997. Wrappers for feature subset selection. Artif. Intel. 97, 1--2, 273--324. Google Scholar
- Langley, P. and Sage, S. 1994. Induction of selected Bayesian classifiers. In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI 1994). 399--406.Google Scholar
- Lu, L., Zhang, H., and Jiang, H. 2002. Content analysis for audio classification and segmentation. IEEE Trans. Speech Audio Process. 10, 7, 504--516.Google Scholar
- Maulsby, D., Greenberg, S., and Mander, R. 1993. Prototyping an intelligent agent through wizard of Oz. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 1993). Google Scholar
- McFarlane, D. C. 2002. Comparison of four primary methods for coordinating the interruption of people in human-computer interaction. Hum.-Comput. Interact. 17, 1, 63--139. Google Scholar
- McFarlane, D. C. 1999. Coordinating the interruption of people in human-computer interaction. In Proceedings of the IFIP Conference on Human-Computer Interaction (INTERACT 1999).Google Scholar
- Milewski, A. E. and Smith, T. M. 2000. Providing presence cues to telephone users. In Proceedings of the ACM Conference on Computer Supported Cooperative Work (CSCW 2000). 89--96. Google Scholar
- Mitchell, T. M. 1997. Machine Learning. McGraw-Hill. Google Scholar
- Oliver, N., Horvitz, E., and Garg, A. 2002. Layered representations for recognizing office activity. In Proceedings of the International Conference on Multimodal Interaction (ICMI 2002). 3--8.Google Scholar
- Perlow, L. A. 1999. The time famine: Toward a sociology of work time. In Admin. Sci. Quart. 44, 1, 57--81.Google Scholar
- Quinlan, J. R. 1993. C4.5: Programs for Machine Learning. Morgan Kaufmann. Google Scholar
- Redström, J., Skog, T., and Hallnäs, L. 2000. Informative art: Using amplified artworks as information displays. In Proceedings of Designing Augmented Reality Environments. Google Scholar
- Schmidt, A., Takaluoma, A., and Mäntyjärvi, J. 2000. Context-aware telephony over WAP. Pers. Ubiquit. Comput. 4, 4, 225--229. Google Scholar
- Seshadri, S. and Shapira, Z. 2001. Managerial allocation of time and effort: The effects of interruptions. Manage. Sci. 47, 5, 647--662. Google Scholar
- Voida, A., Newstetter, W. C., and Mynatt, E. D. 2002. When conventions collide: The tensions of instant messaging attributed. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2002). 187--194. Google Scholar
- Witten, I. H. and Frank, E. 1999. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann. Google Scholar
Index Terms
- Predicting human interruptibility with sensors
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Reviews
Claudia Roda
How can a system decide whether it is appropriate to interrupt a person's activity to report, for example, the availability of new information, or the arrival of an email or phone call__?__ The authors of this paper argue "that simple sensors can support the construction of models that estimate human interruptibility as well as people do." They support their claim with a very detailed description of a set of quantitative studies, analyzing audio and video recordings of people in their working environments, along with their self-reported "rate of current interruptibility." The results obtained from a set of manually simulated sensors detecting various activities in the environment are used to feed models (based on standard machine learning techniques) capable of providing estimates of interruptibility. The authors demonstrate that these estimates "are as good as or better than the estimates provided by people watching [the] audio and video recordings." While the authors are mainly interested in demonstrating that interruptibility can be estimated using easy-to-build sensors, the reader will have to turn to other sources for a more holistic view of human interruptibility. Such a holistic view would address, for example, the issues of a person's goals with respect to the interruption [1]; the situation in which the interruption takes place, and individual preferences with respect to interruptions [2]; and the fine timing of interruptions during task performance. The results and methodologies described in this paper will be very relevant for those studying sensor-based detection of human cognitive states. Online Computing Reviews Service
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