Structural Learning of Activities from Sparse Datasets

Abstract. A major challenge in pervasive computing is to learn activity patterns, such as bathing and cleaning from sensor data. Typical sensor deployments generate sparse datasets with thousands of sensor readings and a few instances of activities. The imbalance between the number of features (i.e. sensors) and the classification targets (i.e. activities) complicates the learning process. In this paper, we propose a novel framework for discovering relationships between sensor signals and observed human activities from sparse datasets. The framework builds on the use of Bayesian networks for modeling activities by representing statistical dependencies between sensors. We optimize learning Bayesian networks of activities in 3 ways. Firstly, we perform multicollinearity analysis to focus on orthogonal sensor data with minimal redundancy. Secondly, we propose Efron's bootstrapping to generate large training sets that capture important features of an activity. Finally, we find the bes...

• Type: Conference
• Year: 2007
• Where: PERCOM
• Authors: Fahd Albinali, Nigel Davies, Adrian Friday
• Keywords: Orthogonal Sensor Data, PERCOM 2007, Sensor Signals, Typical Sensor Deployments, Wireless Networks