Learning movement patterns of the occupant in smart home environments: an unsupervised learning approach

Abstract

In recent years, the concept of smart homes and smart buildings have gradually emerged as mainstream with the development of sensor technology and the introduction of several commercial solutions. As one of the most important aspect of this technical-driven smart home system, learning and recognizing the occupant’s routine activities and movement patterns is the foundation of further human behavior understanding and prediction process. In this paper, we propose a decomposition based unsupervised learning approach, which is able to learn the occupant’s moving patterns directly from the sensor data without event annotations. It overcomes the limitation suffered by a lot of other supervised learning approaches that sensor data sets with event annotations are not difficult to collect since manually data labeling is not scalable and extremely time consuming. By applying the proposed method on Aruba motion sensor data set, we show that the decomposition based pattern learning method leverages the well-developed Non-negative Matrix Factorization algorithm that it can learn the movement pattern candidates of the occupant very fast, with outputs that can be easily interpreted.