NDLI: Discovery of topological relations for spatial Activity Recognition

Content Provider	IEEE Xplore Digital Library
Author	Bouchard, K. Bouzouane, A. Bouchard, B.
Copyright Year	2013
Description	Author affiliation: LIARA Lab., UQAC, Chicoutimi, QC, Canada (Bouchard, K.; Bouzouane, A.; Bouchard, B.)
Abstract	Human Activity Recognition (HAR) is a challenging problem that could enable an outstanding number of applications in pervasive computing. Many approaches have been developed to overcome this issue, but they all suffer from major drawbacks. While some use invasive sensors such as video-cameras and wearable technology, other exploit complex models to only recognize coarse-grained activities. In this paper, we propose to exploit the largely neglected spatial aspects in the smart home to recognize the activity of daily living (ADLs) of a resident in a noninvasive fashion. To do so, we designed an extension to well-known data mining algorithms that we exploit to automatically learn the models of the resident ADLs. The models are built from the retrieval of spatial patterns corresponding to the topological relationships of the smart home entities. We demonstrate the advantages of our new semi-supervised system through comprehensive experiments inside a smart home and compare the results with expert defined models of activity.
Starting Page	73
Ending Page	80
File Size	1363482
Page Count	8
File Format	PDF
ISBN	9781467358958
DOI	10.1109/CIDM.2013.6597220
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-04-16
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Knowledge based systems Hidden Markov models topology Smart homes Probabilistic logic smart home Spatial databases Sensors Data mining spatial data mining activity recognition
Content Type	Text
Resource Type	Article

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