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 in- terface. 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 us- ing 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 includ- ing (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).

@inproceedings{mutlu07robust,
	author = {Bilge Mutlu and Andreas Krause and Jodi Forlizzi and Carlos Guestrin and Jessica Hodgins},
	booktitle = {ACM Symposium on User Interface Software and Technology (UIST)},
	month = {October},
	title = {Robust, Low-cost, Non-intrusive Sensing and Recognition of Seated Postures},
	year = {2007}}