Detecting and predicting of abnormal behavior using hierarchical Markov model in smart home network

• Published in: 2010 IEEE 17Th International Conference on Industrial Engineering and Engineering Management pp. 410 - 414
• Main Authors: Wonjoon Kang, Dongkyoo Shin, Dongil Shin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2010
• Subjects: Accuracy; detecting abnormal behavior; Hidden Markov Model; Hidden Markov models; Hierarchy Hidden Markov Model; smart home network; ubiquitous environment; Viterbi algorithm
• ISBN: 1424464838; 9781424464838
• DOI: 10.1109/ICIEEM.2010.5646583

In this paper, we present an application of the hierarchical hidden Markov model (HHMM) for the problem of predicting the state of human behavior in a smart home network. We argue that to robustly model and recognize sequential human activities, it is crucial to exploit both the natural hierarchical decomposition and shared semantics embedded in a ubiquitous environment. To this end, we propose the use of the HHMM, a rich stochastic model that has recently been extended to handle shared structures, for representing and recognizing a set of complex indoor activities. 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.