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