A longitudinal study of pressure sensing to infer real-world water usage events in the home

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6696 LNCS, Issue , 2011, Pages 50-69

  Froehlich, Jon ^a   Larson, Eric ^a   Saba, Elliot ^a   Campbell, Tim ^a   Atlas, Les ^a   Fogarty, James ^a   Patel, Shwetak ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

Author keywords

activity inference; field deployments; sustainability; Water sensing

Indexed keywords

ACTIVITY INFERENCE; BAYESIAN APPROACHES; CLASSIFICATION APPROACH; FIELD DEPLOYMENT; GROUND TRUTH; LANGUAGE MODEL; LONGITUDINAL STUDY; PRESSURE SENSING; PRESSURE-BASED; PRIOR PROBABILITY; PROBABILISTIC ALGORITHM; WATER SENSING; WATER USAGE;

ALGORITHMS; BAYESIAN NETWORKS; COMPUTATIONAL LINGUISTICS; FIXTURES (TOOLING); INFERENCE ENGINES; PRESSURE SENSORS; SENSOR NETWORKS; TEMPLATE MATCHING; UBIQUITOUS COMPUTING; SPEECH RECOGNITION; SUSTAINABLE DEVELOPMENT;

SPEECH RECOGNITION; TEMPLATE MATCHING;

EID: 79958826713     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-21726-5_4     Document Type: Conference Paper

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