Anomaly detection algorithm based on life pattern extraction from accumulated pyroelectric sensor data

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 two-dimensional probabilistic density function of them. Using this function, we try to classify behavior labels and detect anomaly. Here, we assume two kinds of anomaly, ldquothe rare behaviorsrdquo and ldquothe changes of life patternrdquo. The algorithm is confirmed to work on real behavior data through the experiment on about 400 days data.