Prediction of state of user's behavior using Hidden Markov Model in ubiquitous home network

In this paper, we used Hidden Markov prediction tools to predict the state of the behavior of users in a ubiquitous home network. The state of the user's behavior presents a change of interest in the action of the user. This paper proposes a weight (WEIGHT) for the level of interest in the beha...

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Bibliographic Details
Published in:	2010 IEEE International Conference on Industrial Engineering and Engineering Management pp. 1752 - 1756
Main Authors:	Wonjoon Kang, Dongkyoo Shine, Doingil Shin
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-12-2010
Subjects:	Accuracy Hidden Markov Model Hidden Markov models Home automation Home Network Sensors sequential data state prediction Training Training data Ubiquitous environment Viterbi algorithm
Online Access:	Get full text
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Summary:	In this paper, we used Hidden Markov prediction tools to predict the state of the behavior of users in a ubiquitous home network. The state of the user's behavior presents a change of interest in the action of the user. This paper proposes a weight (WEIGHT) for the level of interest in the behavior and the strength of the relation between the behavior and interest, which is the formulation of the user's interest in the human action. We investigate the feasibility of predicting the next state using the sequence of previously observed states and the action type, and analyze the efficiency of the Hidden Markov Model (HMM). The prediction accuracy of the method is determined. It is found that, on average, the choice of training data leads to a prediction accuracy of 84.61%, while in some cases the accuracy is as high as 91.23%.
ISBN:	9781424485017 1424485010
ISSN:	2157-3611 2157-362X
DOI:	10.1109/IEEM.2010.5674569