The N-Best algorithm: an efficient procedure for finding top N sentence hypotheses
HLT '89 Proceedings of the workshop on Speech and Natural Language
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
The design of eco-feedback technology
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The design and evaluation of prototype eco-feedback displays for fixture-level water usage data
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Sensor-based physical interactions as interventions for change in residential energy consumption
CHI '12 Extended Abstracts on Human Factors in Computing Systems
Recognizing water-based activities in the home through infrastructure-mediated sensing
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
A longitudinal study of vibration-based water flow sensing
ACM Transactions on Sensor Networks (TOSN)
Mapping hidden water pipelines using a mobile sensor droplet
ACM Transactions on Sensor Networks (TOSN)
WaterSense: water flow disaggregation using motion sensors
Proceedings of the Third ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Understanding visual attention of teams in dynamic medical settings through vital signs monitor use
Proceedings of the 2013 conference on Computer supported cooperative work
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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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 fixture-category 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.