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Published in:
2013 | OriginalPaper | Chapter
Predicting of Abnormal Behavior Using Hierarchical Markov Model Based on User Profile in Ubiquitous Environment
Authors : Jaewan Shin, Dongkyoo Shin, DongIl Shin
Published in: Grid and Pervasive Computing
Publisher: Springer Berlin Heidelberg
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In this paper, we model the multilevel statistical structure as Hierarchical Hidden Markov Models (HHMM) for the problem of predicting the state of human behavior based on user profile in a ubiquitous home network. Algorithms to analyze the behavioral patterns of a user using the information provided by the user in a home network system. We propose the detecting of abnormal behavior algorithm, which builds profile based on the actions taken when the user enters a room. The main contributions of this paper lie in the application of the shared structure HHMM, the estimation of the state of a user’s behavior, and the detection of abnormal behavior. The user behavior data from an experiment show that directly modeling shared structures improves the recognition efficiency and prediction accuracy for the state of a human’s behavior when compared with a flat HMM.