User’s Behavior Classification Model for Smart Houses Occupant Prediction
Rachid Kadouche, Hélène Pigot, Bessam Abdulrazak, Sylvain Giroux · Atlantis ambient and pervasive intelligence · 2011
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. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.