Ryo Urushibata

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This paper presents three behavior labeling algorithms based on supervised learning using accumulated pyroelectric sensor data in the living space. We summarize features of each algorithm to use them in combination matched to usage of the livelihood support application. They are (a)labeling algorithms based on switching model around a behavioral(More)
This paper describes an algorithm of behavior labeling and anomaly detection for elder people living alone. In order to grasp the personpsilas life pattern, we set some pyroelectric sensors in the house and measure the personpsilas movement data all the time. From those sequential data, we extract two kinds of information, time and duration, and calculate(More)
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