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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 (1) labeling algorithms based on time attribution of ldquoon-offrdquo data, (2) one based on Hidden Markov Models, and (3)...
This paper presents a behavior description algorithm from time-series data on daily life activities extracted using home sensors. In a previous work, we proposed a method of time-series data clustering based on hidden Markov models (HMMs). This method separates the time-series data in segments of equally short length, and applies a behavior label for each segment. However, the change points of behaviors...
In this paper, we propose a method consists of two components, behavior patterns extraction and anomaly detection algorithm in daily life. To begin with, sensor data are accumulated in a room environment and behavior description labels are assigned for each data segment using HMM(hidden Markov model) and k-means method. An HMM is composed every day based on sensor data segments of the day. The behavior...
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