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公开(公告)号：EP1324290A2
公开(公告)日：2003-07-02
申请号：EP02028793.4
申请日：2002-12-27
发明人： Ueno, Reiko , Kaneda, Noriko , Omori, Takashi , Hara, Kousuke , Yamamoto, Hiroshi , Inoue, Shigeyuki , Tanaka, Shinji
IPC分类号： G08B21/04
CPC分类号： G06K9/00771 , G06F19/00 , G06K9/00335 , G06K9/6218 , G06K9/6284 , G06K9/6297 , G08B21/0423 , G08B21/0469 , G08B31/00
摘要： An abnormality detection device 30 includes small motion sensors 25a∼25c that detect small motions of a person in a house, a data collecting unit 32 that collects and stores sensor signals from the small motion sensors 25a∼25c as sensor patterns, a Markov chain operating unit 33 that transforms the sensor patterns into a cluster sequence by vector-quantizing input patterns which are obtained by averaging and normalizing the sensor patterns and calculates a transition number matrix and a duration time distribution of a Markov chain and so on using a Markov chain model, a comparing unit 34 that calculates characteristic amount (Euclid distance and average log likelihood in appearance frequency of a Markov chain and average log likelihood to the duration time distribution of a Markov chain) of a sample activity as against a daily activity based on the obtained transition number matrix and the duration time distribution and so on, and others.
摘要翻译： 异常检测装置30包括检测房屋内的人的小动作的小动作传感器25a〜25c，收集并存储来自小动作传感器25a〜25c的传感器信号作为传感器模式的数据收集部32，马尔可夫链动作 单元33，其通过对传感器图案进行平均和归一化所获得的输入图案进行矢量量化，并将传感器图案变换成聚类序列，并且使用马尔科夫链模型计算马尔可夫链等的转移数矩阵和持续时间分布等 ，比较单元34，其基于所获得的样本活动相对于日常活动来计算样本活动的特征量（马尔可夫链的出现频率上的欧几里得距离和平均对数似然性以及马尔可夫链的持续时间分布的平均对数似然性） 过渡数矩阵和持续时间分布等等。
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公开(公告)号：EP1324290B1
公开(公告)日：2005-08-17
申请号：EP02028793.4
申请日：2002-12-27
发明人： Ueno, Reiko , Kaneda, Noriko , Omori, Takashi , Hara, Kousuke , Yamamoto, Hiroshi , Inoue, Shigeyuki , Tanaka, Shinji
IPC分类号： G08B21/04
CPC分类号： G06K9/00771 , G06F19/00 , G06K9/00335 , G06K9/6218 , G06K9/6284 , G06K9/6297 , G08B21/0423 , G08B21/0469 , G08B31/00
摘要： An abnormality detection device 30 includes small motion sensors 25a SIMILAR 25c that detect small motions of a person in a house, a data collecting unit 32 that collects and stores sensor signals from the small motion sensors 25a SIMILAR 25c as sensor patterns, a Markov chain operating unit 33 that transforms the sensor patterns into a cluster sequence by vector-quantizing input patterns which are obtained by averaging and normalizing the sensor patterns and calculates a transition number matrix and a duration time distribution of a Markov chain and so on using a Markov chain model, a comparing unit 34 that calculates characteristic amount (Euclid distance and average log likelihood in appearance frequency of a Markov chain and average log likelihood to the duration time distribution of a Markov chain) of a sample activity as against a daily activity based on the obtained transition number matrix and the duration time distribution and so on, and others.
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公开(公告)号：EP1324290A3
公开(公告)日：2003-11-26
申请号：EP02028793.4
申请日：2002-12-27
发明人： Ueno, Reiko , Kaneda, Noriko , Omori, Takashi , Hara, Kousuke , Yamamoto, Hiroshi , Inoue, Shigeyuki , Tanaka, Shinji
IPC分类号： G08B21/04
CPC分类号： G06K9/00771 , G06F19/00 , G06K9/00335 , G06K9/6218 , G06K9/6284 , G06K9/6297 , G08B21/0423 , G08B21/0469 , G08B31/00
摘要： An abnormality detection device 30 includes small motion sensors 25a∼25c that detect small motions of a person in a house, a data collecting unit 32 that collects and stores sensor signals from the small motion sensors 25a∼25c as sensor patterns, a Markov chain operating unit 33 that transforms the sensor patterns into a cluster sequence by vector-quantizing input patterns which are obtained by averaging and normalizing the sensor patterns and calculates a transition number matrix and a duration time distribution of a Markov chain and so on using a Markov chain model, a comparing unit 34 that calculates characteristic amount (Euclid distance and average log likelihood in appearance frequency of a Markov chain and average log likelihood to the duration time distribution of a Markov chain) of a sample activity as against a daily activity based on the obtained transition number matrix and the duration time distribution and so on, and others.
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