User’s Behavior Classification Model for Smart Houses Occupant Prediction View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2011-05-05

AUTHORS

Rachid Kadouche , Hélène Pigot , Bessam Abdulrazak , Sylvain Giroux

ABSTRACT

This paper deals with the smart house occupant prediction issue based on daily life activities. Based on data provided by non intrusive sensors and devices, our approach uses supervised learning technics to predict the house occupant. We applied Support Vector Machines (SVM) classifier to build a Behavior Classification Model (BCM) and learn the users’ habits when they perform activities for predicting and identifying the house occupant. To test the model, we have analyzed the early morning routine activity of six users at the DOMUS apartment and two users of the publicly available dataset of the Washington State University smart apartment tesbed. The results showed a high prediction precision and demonstrate that each user has his own manner to perform his morning activity, and can be easily identified by just learning his habits. More... »

PAGES

149-164

Book

TITLE

Activity Recognition in Pervasive Intelligent Environments

ISBN

978-90-78677-42-0
978-94-91216-05-3

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.2991/978-94-91216-05-3_7

DOI

http://dx.doi.org/10.2991/978-94-91216-05-3_7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1088313046


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