Title:
Recognizing Water-Based Activities in the Home Through Infrastructure-Mediated Sensing

dc.contributor.author Thomaz, Edison
dc.contributor.author Bettadapura, Vinay
dc.contributor.author Reyes, Gabriel
dc.contributor.author Sandesh, Megha
dc.contributor.author Schindler, Grant
dc.contributor.author Plötz, Thomas
dc.contributor.author Abowd, Gregory D.
dc.contributor.author Essa, Irfan
dc.contributor.corporatename Georgia Institute of Technology. School of Interactive Computing en_US
dc.contributor.corporatename Georgia Institute of Technology. Institute for Robotics and Intelligent Machines en_US
dc.contributor.corporatename University of Newcastle upon Tyne. School of Computing Science en_US
dc.date.accessioned 2014-03-13T19:29:25Z
dc.date.available 2014-03-13T19:29:25Z
dc.date.issued 2012-09
dc.description Copyright ©2012 ACM en_US
dc.description Presented at the 14th International Conference on Ubiquitous Computing (Ubicomp 2012), September 5-8, 2012, Pittsburgh, PA.
dc.description DOI: 10.1145/2370216.2370230
dc.description.abstract Activity 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.embargo.terms null en_US
dc.identifier.citation E. 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.doi 10.1145/2370216.2370230
dc.identifier.isbn 978-1-4503-1224-0
dc.identifier.uri http://hdl.handle.net/1853/51327
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.publisher.original Association for Computing Machinery
dc.subject Activities of daily living en_US
dc.subject Activity recognition en_US
dc.subject Health en_US
dc.subject Infrastructure-mediated sensing en_US
dc.subject Machine learning en_US
dc.subject Vector space models en_US
dc.title Recognizing Water-Based Activities in the Home Through Infrastructure-Mediated Sensing en_US
dc.type Text
dc.type.genre Post-print
dc.type.genre Proceedings
dspace.entity.type Publication
local.contributor.author Essa, Irfan
local.contributor.author Abowd, Gregory D.
local.contributor.corporatename Institute for Robotics and Intelligent Machines (IRIM)
relation.isAuthorOfPublication 84ae0044-6f5b-4733-8388-4f6427a0f817
relation.isAuthorOfPublication a9e4f620-85d6-4fb9-8851-8b0c3a0e66b4
relation.isOrgUnitOfPublication 66259949-abfd-45c2-9dcc-5a6f2c013bcf
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