TY - CHAP T1 - A Longitudinal Study of Pressure Sensing to Infer Real-World Water Usage Events in the Home T2 - Pervasive ComputingPervasive Computing Y1 - 2011 A1 - Jon Froehlich A1 - Larson,Eric A1 - Saba,Elliot A1 - Campbell,Tim A1 - Atlas,Les A1 - Fogarty,James A1 - Patel,Shwetak ED - Lyons,Kent ED - Hightower,Jeffrey ED - Huang,Elaine AB - 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. JA - Pervasive ComputingPervasive Computing T3 - Lecture Notes in Computer Science PB - Springer Berlin / Heidelberg VL - 6696 SN - 978-3-642-21725-8 UR - http://dx.doi.org/10.1007/978-3-642-21726-5_4 ER -