Anomaly detection algorithm based on life pattern extraction from accumulated pyroelectric sensor data
Anomaly detection algorithm based on life pattern extraction from accumulated pyroelectric sensor data
复制标题
基于累积热释电传感器数据的生命模式提取的异常检测算法
DOI：
10.1109/iros.2008.4650864
复制
发表时间：
2008
期刊：
影响因子：
--
通讯作者：
Tomomasa Sato
中科院分区：
文献类型：
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作者：
Taketoshi Mori;R. Urushibata;M. Shimosaka;H. Noguchi;Tomomasa Sato
提出了一种针对独居老人的行为标注和异常检测算法。为了掌握人象的生活规律，我们在室内设置了热释电传感器，随时测量人象的运动数据。从这些序列数据中提取时间和持续时间两类信息，并计算它们的二维概率密度函数。使用该函数，我们尝试对行为标签进行分类并检测异常。在这里，我们假设两种异常，ldquothe罕见的行为rdquo和ldquothe生活模式的变化rdquo。通过对近400天的数据进行实验，验证了该算法在真实的行为数据上的有效性。
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.