%0 Book Section %B Pervasive Computing %D 2011 %T A Longitudinal Study of Pressure Sensing to Infer Real-World Water Usage Events in the Home %A Jon Froehlich %A Larson,Eric %A Saba,Elliot %A Campbell,Tim %A Atlas,Les %A Fogarty,James %A Patel,Shwetak %E Lyons,Kent %E Hightower,Jeffrey %E Huang,Elaine %X 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. %B Pervasive Computing %S Lecture Notes in Computer Science %I Springer Berlin / Heidelberg %V 6696 %P 50 - 69 %8 2011 %@ 978-3-642-21725-8 %G eng %U http://dx.doi.org/10.1007/978-3-642-21726-5_4