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dc.contributor.authorThomaz, Edison
dc.contributor.authorBettadapura, Vinay
dc.contributor.authorReyes, Gabriel
dc.contributor.authorSandesh, Megha
dc.contributor.authorSchindler, Grant
dc.contributor.authorPlötz, Thomas
dc.contributor.authorAbowd, Gregory D.
dc.contributor.authorEssa, Irfan
dc.date.accessioned2014-03-13T19:29:25Z
dc.date.available2014-03-13T19:29:25Z
dc.date.issued2012-09
dc.identifier.citationE. Thomaz, V. Bettadapura, G. Reyes, M. Sandesh, G. Schindler, T. Ploetz, G. D. Abowd, and I. Essa (2012). “Recognizing Water-Based Activities in the Home Through Infrastructure-Mediated Sensing,” in Proceedings of ACM International Conference on Ubiquitous Computing (UBICOMP), 2012.en_US
dc.identifier.isbn978-1-4503-1224-0
dc.identifier.urihttp://hdl.handle.net/1853/51327
dc.descriptionCopyright ©2012 ACMen_US
dc.descriptionPresented at the 14th International Conference on Ubiquitous Computing (Ubicomp 2012), September 5-8, 2012, Pittsburgh, PA.
dc.descriptionDOI: 10.1145/2370216.2370230
dc.description.abstractActivity recognition in the home has been long recognized as the foundation for many desirable applications in fields such as home automation, sustainability, and healthcare. However, building a practical home activity monitoring system remains a challenge. Striking a balance between cost, privacy, ease of installation and scalability continues to be an elusive goal. In this paper, we explore infrastructure-mediated sensing combined with a vector space model learning approach as the basis of an activity recognition system for the home. We examine the performance of our single-sensor water-based system in recognizing eleven high-level activities in the kitchen and bathroom, such as cooking and shaving. Results from two studies show that our system can estimate activities with overall accuracy of 82.69% for one individual and 70.11% for a group of 23 participants. As far as we know, our work is the first to employ infrastructure-mediated sensing for inferring high-level human activities in a home setting.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectActivities of daily livingen_US
dc.subjectActivity recognitionen_US
dc.subjectHealthen_US
dc.subjectInfrastructure-mediated sensingen_US
dc.subjectMachine learningen_US
dc.subjectVector space modelsen_US
dc.titleRecognizing Water-Based Activities in the Home Through Infrastructure-Mediated Sensingen_US
dc.typePost-printen_US
dc.typeProceedings
dc.contributor.corporatenameGeorgia Institute of Technology. School of Interactive Computingen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Institute for Robotics and Intelligent Machinesen_US
dc.contributor.corporatenameUniversity of Newcastle upon Tyne. School of Computing Scienceen_US
dc.publisher.originalAssociation for Computing Machinery
dc.identifier.doi10.1145/2370216.2370230
dc.embargo.termsnullen_US


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