Proceedings of 20th ACM Symposium on User Interface Software and Technology


 
Time and place:
Newport, RI, USA
October 7-10, 2007, 2007
Conf. description:
UIST is the premier forum for innovations in developing human-computer interfaces. The symposium brings together user-interface researchers and practitioners with an interest in techniques, tools, and technology for constructing high-quality, innovative user interfaces.
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Series:
This is a preferred venue for people like Scott E. Hudson, Ravin Balakrishnan, Brad A. Myers, Steven K. Feiner, and Takeo Igarashi. Part of the UIST - Symposium on User Interface Software and Technology conference series.
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Publisher:
ACM
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References from this conference (2007)

The following articles are from "Proceedings of 20th ACM Symposium on User Interface Software and Technology":

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Articles

Mutlu, Bilge, Krause, Andreas, Forlizzi, Jodi, Guestrin, Carlos and Hodgins, Jessica (2007): Robust, Lowcost, Non-intrusive Sensing and Recognition of Seated Postures. In: Proceedings of 20th ACM Symposium on User Interface Software and Technology October 7-10, 2007, 2007, Newport, RI, USA. . Available online

In this paper, we present a methodology for recognizing seated postures using data from pressure sensors installed on a chair. Information about seated postures could be used to help avoid adverse effects of sitting for long periods of time or to predict seated activities for a human-computer interface. Our system design displays accurate near-real-time classification performance on data from subjects on which the posture recognition system was trained by using a set of carefully designed, subject-invariant signal features. By using a near-optimal sensor placement strategy, we keep the number of required sensors low thereby reducing cost and computational complexity. We evaluated the performance of our technology using a series of empirical methods including (1) cross-validation (classification accuracy of 87% for ten postures using data from 31 sensors), and (2) a physical deployment of our system (78% classification accuracy using data from 19 sensors).

© All rights reserved Mutlu et al. and/or ACM




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