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Robust, Low-cost, Non-intrusive Sensing and Recognition of Seated Postures by Bilge Mutlu; Andreas Krause; Jodi Forlizzi; Carlos Guestrin; Jessica Hodgins is a scholarly article available to read on EtoBox.

What is Robust, Low-cost, Non-intrusive Sensing and Recognition of Seated Postures about?

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 not 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).

Author

Bilge Mutlu; Andreas Krause; Jodi Forlizzi; Carlos Guestrin; Jessica Hodgins

Publisher

ACM

Published

2007

Language

EN