Detecting and predicting of abnormal behavior using hierarchical Markov model in smart home network
WonJoon Kang, Dongkyoo Shin, Dongil Shin · 2010
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.