Sensing from the basement: a feasibility study of unobtrusive and low-cost home activity recognition
UIST '06 Proceedings of the 19th annual ACM symposium on User interface software and technology
NAWMS: nonintrusive autonomous water monitoring system
Proceedings of the 6th ACM conference on Embedded network sensor systems
HydroSense: infrastructure-mediated single-point sensing of whole-home water activity
Proceedings of the 11th international conference on Ubiquitous computing
A longitudinal study of pressure sensing to infer real-world water usage events in the home
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
Minimizing intrusiveness in home energy measurement
BuildSys '12 Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Hot water DJ: saving energy by pre-mixing hot water
BuildSys '12 Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
DoubleDip: leveraging thermoelectric harvesting for low power monitoring of sporadic water use
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
FixtureFinder: discovering the existence of electrical and water fixtures
Proceedings of the 12th international conference on Information processing in sensor networks
Circulo: Saving Energy with Just-In-Time Hot Water Recirculation
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
The Energy-Water Nexus in Campuses
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
Deep sparse coding based recursive disaggregation model for water conservation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Smart water meters will soon provide real-time access to instantaneous water usage in many homes, and disaggregation is the problem of deciding how much of that usage is due to individual fixtures in the home. Existing disaggregation techniques require additional water sensing infrastructure and/or a manual characterization of each water fixture, which can be expensive and time consuming. In this paper, we describe a novel technique called WaterSense to perform fixture-level disaggregation using only a handful of inexpensive motion sensors. WaterSense automatically infers how many fixtures are in each room, and how much water each fixture uses. We evaluate the system using a 7-day in-situ evaluation in 2 diverse multi-resident homes with a total of 10 different water fixtures and 467 fixture events and observe that our approach achieves 86% classification accuracy in identifying individual water fixture events and 80--90% accuracy in determining the water consumption of individual water fixtures.