The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jan. 24, 2006

Filed:

Dec. 23, 2002
Applicants:

Reiko Ueno, Takarazuka, JP;

Noriko Kaneda, Kobe, JP;

Takashi Omori, Sapporo, JP;

Kousuke Hara, Hachioji, JP;

Hiroshi Yamamoto, Shijonawate, JP;

Shigeyuki Inoue, Kyotanabe, JP;

Shinji Tanaka, Ibaraki, JP;

Inventors:

Reiko Ueno, Takarazuka, JP;

Noriko Kaneda, Kobe, JP;

Takashi Omori, Sapporo, JP;

Kousuke Hara, Hachioji, JP;

Hiroshi Yamamoto, Shijonawate, JP;

Shigeyuki Inoue, Kyotanabe, JP;

Shinji Tanaka, Ibaraki, JP;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G08B 29/00 (2006.01);
U.S. Cl.
CPC ...
Abstract

An abnormality detection device includes small motion sensors that detect small motions of a person in a house; a data collecting unit that collects and stores sensor signals from the small motion sensors as sensor patterns, and a Markov chain operating unitthat 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 and so on using a Markov chain model. The abnormality detection device also includes a comparing unit that calculates a characteristic amount (Euclid distance and average log likelihood in an appearance frequency of a Markov chain and an 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.


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