@inbook {14324,
	title = {A Longitudinal Study of Pressure Sensing to Infer Real-World Water Usage Events in the Home},
	booktitle = {Pervasive ComputingPervasive Computing},
	series = {Lecture Notes in Computer Science},
	volume = {6696},
	year = {2011},
	month = {2011///},
	pages = {50 - 69},
	publisher = {Springer Berlin / Heidelberg},
	organization = {Springer Berlin / Heidelberg},
	abstract = {We present the first longitudinal study of pressure sensing to infer real-world water usage events in the home (e.g., dishwasher, upstairs bathroom sink, downstairs toilet). In order to study the pressure-based approach out in the wild , we deployed a ground truth sensor network for five weeks in three homes and two apartments that directly monitored valve-level water usage by fixtures and appliances . We use this data to, first, demonstrate the practical challenges in constructing water usage activity inference algorithms and, second, to inform the design of a new probabilistic-based classification approach. Inspired by algorithms in speech recognition, our novel Bayesian approach incorporates template matching, a language model, grammar, and prior probabilities. We show that with a single pressure sensor, our probabilistic algorithm can classify real-world water usage at the fixture level with 90\% accuracy and at the fixturecategory level with 96\% accuracy. With two pressure sensors, these accuracies increase to 94\% and 98\%. Finally, we show how our new approach can be trained with fewer examples than a strict template-matching approach alone.},
	isbn = {978-3-642-21725-8},
	url = {http://dx.doi.org/10.1007/978-3-642-21726-5_4},
	author = {Jon Froehlich and Larson,Eric and Saba,Elliot and Campbell,Tim and Atlas,Les and Fogarty,James and Patel,Shwetak},
	editor = {Lyons,Kent and Hightower,Jeffrey and Huang,Elaine}
}
