Abstract
This paper describes an algorithm of behavior labeling and anomaly detection for elderly people living alone. In order to grasp the person's life pattern, we equip some pyroelectric sensors into the house and measure the person's movement data all the time. From those sequential data, we extract two kinds of information, time and duration of each behavior, and make the system calculate two-dimensional probabilistic density function of them. By using this function, the system classifies behavior labels and detects anomaly. In addition to these two kinds of information, we consider another kind of informaion, behavior transition patterns, at anomaly detection. Here, we assume local anomaly and global anomaly. The former means the rare behaviors and the latter means the changes of life pattern. The algorithm is confirmed through the experiment on about 400 days real behavior data.
