inhabitant prediction in smart houses
This paper addresses the inhabitant prediction issue in smart houses based on daily life activities. We use data provided by non intrusive sensors and devices to predict the house occupant. Support Vector Machines (SVM) classifier was applied to build a Behavior Classification Model (BCM) and learn the users' habits when they perform activities for predicting and identifying the house occupant. The model was tested using data coming from the Washington State University smart apartment tesbed and data from experiment held with six users at the DOMUS apartment. The BCM model results was also compared with a frequency based approach.
smart houses support vector machines inhabitant recognition