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Detection of Irregular Behavior in Room Using Environmental Sensors and Power Consumption of Home Appliances Learning in HMMs
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英語タイトル
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Detection of Irregular Behavior in Room Using Environmental Sensors and Power Consumption of Home Appliances Learning in HMMs
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著者
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- Zhao ShiJie（趙 世杰）
- Toshihiko Sasama（笹間 俊彦）
- Takao Kawamura（川村 尚生）
- Kazunori Sugahara（菅原 一孔）
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論文誌
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International Journal of New Media Technology（IJNMT）
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巻
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5
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号
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2
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ページ
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pp. 94-98
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出版年
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2017年12月
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概要
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We propose a human behavior detect method based on our development system of multifunctional outlet. This is a low-power sensor network system that can recognize human behavior without any wearable devices. In order to detect human regular daily behaviors, we setup various sensors in rooms and use them to record daily lives. In this paper we present a monitoring method of unusual behaviors, and it also can be used for healthcare and so on. We use Hidden Markov Model(HMM), and set two series HMM input to recognize irregular movement from daily lives, One is time sequential sensor data blocks whose sensor values are binarized and splitted by its response. And the other is time sequential labels using Support Vector Machine (SVM). In experiments, our developed sensor network system logged 34days data. HMM learns data of the first 34days that include only usual daily behaviors as training data, and then evaluates the last 8 days that include unusual behaviors.
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ファイル
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BibTeX
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