基于隐马尔可夫模型的异常行为检测行为建模方法

Haruka Ishii, Keisuke Kimino, M. Inoue, Masaki Arahira, Yayoi Suzuki
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引用次数: 7

摘要

痴呆症是由脑细胞死亡引起的记忆和判断障碍。大多数痴呆症状只有在室友注意到老年人的一些行为变化时才能被发现。因此,很难发现独居老人痴呆的早期症状。我们专注于流浪,这是痴呆症的典型症状。提出了一种基于传感器数据的隐马尔可夫模型(HMM)检测漫游症状的系统。我们安装了传感器来获取老年人的行为。然后,基于这些传感器数据,我们使用HMM建立了正常行为模型。然后,我们将此模式模型与检测到的行为模式进行比较。当检测到的行为模式与该模式模型不匹配时,系统将该行为分类为游荡症状。本文提出了一种建立正常行为模型的方法。我们使用两个月的传感器数据验证了我们提出的方法是否可以估计健康独居老年人的行为倾向。结果表明,该方法可以建立考虑受试者习惯的行为模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Method of behavior modeling for detection of anomaly behavior using hidden Markov model
Dementia is disorder of memory and judgment caused by dying brain cells. Most of dementia symptoms can be only detected by housemates when they notice some changes in behaviors of elderly people. Therefore, it is difficult to detect the early symptoms of dementia in elderly people living alone. We focused on wandering which is typical symptom of dementia. We proposed the system to detect wandering symptom based on sensors data using Hidden Markov Model (HMM). We installed sensors to acquire elderly people behavior. Then, we created normal behavior model based on these sensors data using HMM. After that, we compare between this pattern model and detected behavioral pattern. When the detected behavioral pattern did not much with this pattern model, the system will classify the behavior as a wandering symptom. In this paper, we proposed the method which is a creation of normal behavior model. We verified whether our proposal method can estimate behavioral tendency of healthy elderly subject's living alone using two months' sensor data. The result suggested that this method can create a behavior model with considering about subject's habits.
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