DocumentCode :
595462
Title :
A new statistical model for activity discovery and recognition in pervasive environments
Author :
Chikhaoui, B. ; Shengrui Wang ; Pigot, H.
Author_Institution :
Prospectus Lab., Univ. of Sherbrooke, Sherbrooke, QC, Canada
fYear :
2012
fDate :
11-15 Nov. 2012
Firstpage :
3435
Lastpage :
3438
Abstract :
This paper presents a new unsupervised statistical model for human activity discovery and recognition in pervasive environments. The activities are encoded in sequences recorded by non-intrusive sensors disseminated in the environment. Our model studies the relationship between the activities and the sequential patterns from the sequence analysis perspective. Activity discovery is formulated as an optimization problem which is solved by maximization of the likelihood of data. We present experimental results on real datasets recorded in smart homes for persons performing their activities of daily living. The results obtained demonstrate the suitability of our model for activity discovery and recognition and how it outperforms most of the widely used approaches.
Keywords :
data handling; data mining; home automation; optimisation; sensors; statistical distributions; ubiquitous computing; daily living activities; data likelihood maximization; human activity discovery; human activity recognition; nonintrusive sensors; optimization problem; pervasive environments; sequence analysis; sequential patterns; smart homes; unsupervised statistical model; Accuracy; Equations; Hidden Markov models; Inference algorithms; Mathematical model; Optimization; Pattern recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (ICPR), 2012 21st International Conference on
Conference_Location :
Tsukuba
ISSN :
1051-4651
Print_ISBN :
978-1-4673-2216-4
Type :
conf
Filename :
6460903
Link To Document :
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