A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Predicting human interruptibility with sensors: a Wizard of Oz feasibility study
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The Aware Home: A Living Laboratory for Ubiquitous Computing Research
CoBuild '99 Proceedings of the Second International Workshop on Cooperative Buildings, Integrating Information, Organization, and Architecture
Multi-Camera Multi-Person Tracking for EasyLiving
VS '00 Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS'2000)
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TRIDENTCOM '05 Proceedings of the First International Conference on Testbeds and Research Infrastructures for the DEvelopment of NeTworks and COMmunities
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This paper presents a method for automatically estimating human interruptibility in home environments. To make online remote communication smoother, determining if it is appropriate to interrupt the remote partner is critical. As a first step in achieving this goal, several audio-visual features, extracted from data streams provided by a camera and a microphone, are correlated to human interruptibility. Based on these features, the level of interruptibility is estimated using the trained Support Vector Regression (SVR) technique. Finally, we discuss the potential of our method based on the results of several experiments.