p119-fogarty-1 - Predicting Human Interruptibility with...

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Predicting Human Interruptibility with Sensors JAMES FOGARTY, SCOTT E. HUDSON, CHRISTOPHER G. ATKESON, DANIEL AVRAHAMI, JODI FORLIZZI, SARA KIESLER, JOHNNY C. LEE, and JIE YANG Carnegie Mellon University 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 ofFce 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 ofFce workers. Our results also motivate the development of systems that use these models to negotiate interruptions at socially appropriate times. Categories and Subject Descriptors: H.5.2 [ Information Interfaces and Presentation ]: User Interfaces; H.5.3 [ Information Interfaces and Presentation ]: Group and Organization Inter- faces— Collaborative computing ; H.1.2 [ Models and Principles ]: User/Machine Systems; I.2.6 [ ArtiFcial Intelligence ]: Learning General Terms: Design, Measurement, Experimentation, Human ±actors Additional Key Words and Phrases: Situationally appropriate interaction, managing human atten- tion, context-aware computing, sensor-based interfaces, machine learning 1. INTRODUCTION People have developed a variety of conventions that deFne what behavior is socially appropriate in different situations [Barker 1968]. In ofFce working This work was funded in part by DARPA, by the National Science ±oundation under Grants IIS- 01215603, IIS-0205219, IIS-9980013, and by J. ±ogarty’s NS± Graduate Research ±ellowship. Author’s address: J. ±ogarty, School of Computer Science, Carnegie Mellon University, 5000 ±orbes Ave., Pittsburg, PA 15213-3891; email: jfogarty@cs.cmu.edu. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for proFt or direct commercial advantage and that copies show this notice on the Frst page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers,
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This note was uploaded on 02/24/2010 for the course COMM 4400 at Cornell University (Engineering School).

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p119-fogarty-1 - Predicting Human Interruptibility with...

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