Device and system for detecting abnormality

• US 20030117279A1
• Filed: 12/23/2002
• Published: 06/26/2003
• Est. Priority Date: 12/25/2001
• Status: Active Grant

• Current Assignee: Matsushita Electric Industrial Company Limited (Panasonic Holdings Corporation)
• Original Assignee: Matsushita Electric Industrial Company Limited (Panasonic Holdings Corporation)
• Inventors: Hara, Kousuke, Kaneda, Noriko, Tanaka, Shinji, Inoue, Shigeyuki, Yamamoto, Hiroshi, Omori, Takashi, Ueno, Reiko
• Granted Patent: US 6,989,742 B2
• Time in Patent Office: Days
• US Class Current: 340/523
• CPC Class Codes: G06F 18/23 Clustering techniques
• CPC Class Codes: G06F 18/2433 Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
• CPC Class Codes: G06F 18/295 Markov models or related models, e.g. semi-Markov models; Markov random fields; Networks embedding Markov models
• CPC Class Codes: G06V 10/762 using clustering, e.g. of similar faces in social networks
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 10/85 Markov-related models; Markov random fields
• CPC Class Codes: G06V 20/52 Surveillance or monitoring of activities, e.g. for recognising suspicious objects recognising microscopic objects G06V20/69
• CPC Class Codes: G06V 40/20 Movements or behaviour, e.g. gesture recognition recognition of facial expressions G06V40/16
• CPC Class Codes: G08B 21/0423 detecting deviation from an expected pattern of behaviour or schedule
• CPC Class Codes: G08B 21/0469 Presence detectors to detect unsafe condition, e.g. infrared sensor, microphone G08B21/0476 takes precedence
• CPC Class Codes: G08B 31/00 Predictive alarm systems characterised by extrapolation or other computation using updated historic data

Abstract

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.

Claims

1. An abnormality detection device that detects occurrence of abnormality in an event under observation, comprising:

an input unit operable to acquire a sequence of an input pattern that is data which depends upon the event;

a transition analyzing unit operable to analyze a characteristic of a transition in the acquired sequence of the input pattern;

a comparing unit operable to compare the analyzed characteristic of the transition with a predetermined reference value, and judge that abnormality has occurred in the event when the characteristic and the reference value are not approximate to each other within a predetermined range; and

an output unit operable to output occurrence of abnormality when the comparing unit judges that abnormality has occurred.

2. The abnormality detection device according to claim 1,

wherein the transition analyzing unit calculates appearance frequency for each type of the transition in the sequence of the input pattern as the analysis, and

the comparing unit judges whether abnormality has occurred or not by comparing the input pattern acquired by the input unit with a reference input pattern as for the appearance frequency.

3. The abnormality detection device according to claim 2,

wherein the transition analyzing unit calculates probability distribution for each type of the transition as the appearance frequency, and

the comparing unit calculates a distance between the input pattern acquired by the input unit and a reference input pattern as for the probability distribution, and judges that abnormality has occurred when the distance exceeds a predetermined value.

4. The abnormality detection device according to claim 2,

wherein the transition analyzing unit calculates probability distribution for each type of the transition as the appearance frequency, and

the comparing unit calculates a likelihood of the input pattern acquired by the input unit to the reference input pattern as for the probability distribution, and judges that abnormality has occurred when the likelihood is a predetermined value or less.

5. The abnormality detection device according to claim 1,

wherein the transition analyzing unit calculates a duration time distribution that is a distribution of time required for the transition in the sequence of the input pattern as the analysis, and

the comparing unit judges whether abnormality has occurred or not by comparing the input pattern acquired by the input unit with a reference input pattern as for the duration time distribution.

6. The abnormality detection device according to claim 5,

wherein the comparing unit calculates a likelihood of the input pattern acquired by the input unit to the reference input pattern as for the duration time distribution, and judges that abnormality has occurred when the likelihood is a predetermined value or less.

7. The abnormality detection device according to claim 1 further comprising a clustering unit operable to transform the sequence of the input pattern acquired by the input unit into a cluster sequence that is a predetermined representative input pattern,

wherein the transition analyzing unit analyzes the characteristic in the cluster sequence which is transformed by the clustering unit.

8. The abnormality detection device according to claim 7,

wherein the clustering unit obtains the cluster sequence after averaging and normalizing the sequence of the input pattern acquired by the input unit.

9. The abnormality detection device according to claim 8,

wherein the clustering unit obtains the cluster sequence after specifying all the clusters by vector-quantizing the sequence of the input pattern obtained by the normalization.

10. The abnormality detection device according to claim 1,

wherein the reference value is a value, as for a reference event, obtained by acquiring the sequence of the input pattern in advance through the input unit and analyzing the characteristic through the transition analyzing unit.

11. The abnormality detection device according to claim 10,

wherein the reference value is a value determined based on data given by an operator which is learned with a teacher and the input pattern which is acquired repeatedly by the input unit.

12. The abnormality detection device according to claim 1,

wherein the reference value is a value specified by an operator.

13. The abnormality detection device according to claim 1,

wherein the input unit includes a plurality of small motion detection sensors placed in a plurality of places in a house, and acquires, as the input pattern, a sensor pattern that is a combination of data indicating whether there is a small motion or not outputted from a plurality of the small motion detection sensors.

14. The abnormality detection device according to claim 13,

wherein the input unit assumes that a new sensor pattern has occurred every time the sensor pattern changes and acquires a sequence of an input pattern corresponding to a sequence of the sensor pattern.

15. The abnormality detection device according to claim 14,

wherein the input unit acquires the input pattern by averaging and normalizing the sensor pattern in the time domain.

16. The abnormality detection device according to claim 1,

wherein the input unit acquires the sequence of the input pattern from an operator.

17. The abnormality detection device according to claim 1,

wherein the output unit reports to a predetermined destination via a transmission channel that abnormality has occurred.

18. The abnormality detection device according to claim 1,

wherein the transition analyzing unit calculates a Markov chain in the sequence of the input pattern and analyzes a characteristic of the calculated Markov chain as the analysis.

19. The abnormality detection device according to claim 18,

wherein the transition analyzing unit calculates an appearance frequency of the Markov chain as the analysis, and

the comparing unit judges whether abnormality has occurred or not by comparing the input pattern acquired by the input unit with a reference input pattern as for the appearance frequency.

20. The abnormality detection device according to claim 19,

wherein the comparing unit calculates an Euclid distance between the input pattern acquired by the input unit and the reference input pattern as for the appearance frequency of the Markov chain, and judges that abnormality has occurred when the Euclid distance exceeds a predetermined value.

21. The abnormality detection device according to claim 20,

wherein the comparing unit calculates a likelihood of the input pattern acquired by the input unit to the reference input pattern as for the appearance frequency of the Markov chain, and judges that abnormality has occurred when the likelihood is a predetermined value or less.

22. The abnormality detection device according to claim 18,

wherein the transition analyzing unit calculates a duration time distribution of the Markov chain as the analysis, and

the comparing unit judges whether abnormality has occurred or not by comparing the input pattern acquired by the input unit with the reference input pattern as for the duration time distribution.

23. The abnormality detection device according to claim 22,

wherein the comparing unit calculates a likelihood of the input pattern acquired by the input unit to the reference input pattern as for the duration time distribution of the Markov chain, and judges that abnormality has occurred when the likelihood is a predetermined value or less.

24. The abnormality detection device according to claim 1

wherein the reference value is a collection of local reference values in each time interval which satisfies a predetermined condition, and

the comparing unit judges whether abnormality has occurred or not by comparing the characteristic with the local reference value in said each time interval.

25. The abnormality detection device according to claim 24 further comprising:

a local reference value generating unit operable to generate the local reference value in said each time interval by acquiring a plurality of the sequences of the input patterns through the input unit, resolving the sequences in each time interval where the acquired input patterns are similar to each other within a predetermined range and collecting the resolved sequences in said each time interval; and

a local reference value selecting unit operable to predict and select an optimum reference value from among all the local reference values belonging to said each time interval based on the input patterns for abnormality detection,

wherein the comparing unit judges whether abnormality has occurred or not by comparing the characteristic with the local reference value selected by the local reference value selecting unit in said each time interval.

26. The abnormality detection device according to claim 25,

wherein the local reference value generating unit samples a time interval with a high correlation according to template matching using a time window and resolves the sequence in said each sampled time interval.

27. The abnormality detection device according to claim 25,

wherein the local reference value selecting unit calculates probability distributions for every transition type in the sequences of the input patterns for abnormality detection and the input patterns of the reference event respectively, and predicts and selects the optimum local reference value based on a distance between the probability distributions.

28. An abnormality detection system comprising:

an abnormality detection device that is placed in a place where an event for abnormality detection occurs;

a communication device that monitors occurrence of abnormality; and

a transmission channel that connects the abnormality detection device and the communication device,

wherein the abnormality detection device includes;

an input unit operable to acquire a sequence of an input pattern that is data which depends upon the event;

a transition analyzing unit operable to analyze a characteristic of a transition in the acquired sequence of the input pattern;

a comparing unit operable to compare the analyzed characteristic of the transition with a predetermined reference value, and judge that abnormality has occurred in the event when the characteristic and the reference value are not approximate to each other within a predetermined range; and

an output unit operable to output occurrence of abnormality when the comparing unit judges that abnormality has occurred, and

the communication device includes;

a receiving unit operable to receive a report from the abnormality detection device; and

a showing unit operable to show an operator the receipt of the report when the receiving unit receives the report.

30. The abnormality detection system according to claim 28 or claim 29,

wherein the input unit includes a plurality of small motion detection sensors placed in a plurality of places in a house, and acquires, as the input pattern, a sensor pattern that is a combination of data indicating whether there is a small motion or not outputted from a plurality of the small motion detection sensors.

31. The abnormality detection system according to claim 28 or claim 29,

wherein the input unit includes an operation state detection sensor that detects an operation state of equipment which is placed in the place, and acquires a sensor signal from the operation state detection sensor as the input pattern.

32. The abnormality detection system according to claim 28 or claim 29,

wherein the place is a house,

the abnormality detection system further comprises:

a home network that connects a plurality of electrical household appliances which are placed in the house; and

a controller that controls a plurality of the electrical household appliances via the home network, and

the input unit detects operation states of a plurality of the electrical household appliances via the home network and acquires the detected operation states as the input patterns.

33. The abnormality detection system according to claim 28 or claim 29,

wherein the place is a house,

the abnormality detection system further comprises a home network that connects a plurality of electrical household appliances which are placed in the house,

the abnormality detection device further includes a controller that controls a plurality of the electrical household appliances via the home network, and

the input unit detects operation states of a plurality of the electrical household appliances via the home network and acquires the detected operation states as the input patterns.

29. An abnormality detection system comprising:

an abnormality detection device that is placed in a place where an event for abnormality detection occurs;

a communication device that monitors occurrence of abnormality; and

a transmission channel that connects the abnormality detection, device and the communication device,

wherein the abnormality detection device includes;

an input unit operable to acquire a sequence of an input pattern that is data which depends upon the event; and

a sending unit operable to send the acquired input pattern to the communication device via the transmission channel, and

the communication device includes;

a receiving unit operable to receive the input pattern sent from the abnormality detection device;

a transition analyzing unit operable to analyze a characteristic of a transition in the acquired sequence of the input pattern;

a comparing unit operable to compare the analyzed characteristic of the transition with a predetermined reference value, and judge that abnormality has occurred in the event when the characteristic and the reference value are not approximate to each other within a predetermined range; and

a showing unit operable to show an operator the occurrence of abnormality when the comparing unit judges that abnormality has occurred.

34. An abnormality detection method for detecting occurrence of abnormality in an event under observation, comprising:

an inputting step for acquiring a sequence of an input pattern that is data which depends upon the event;

a transition analyzing step for analyzing a characteristic of a transition in the acquired sequence of the input pattern;

a comparing step for comparing the analyzed characteristic of the transition with a predetermined reference value, and judging that abnormality has occurred in the event when the characteristic and the reference value are not approximate to each other within a predetermined range; and

an outputting step for outputting occurrence of abnormality when the comparing unit judges that abnormality has occurred.

35. The abnormality detection method according to claim 34,

wherein in the transition analyzing step, appearance frequency for each type of the transition in the sequence of the input pattern is calculated as the analysis, and

in the comparing step, whether abnormality has occurred or not is judged by comparing the input pattern acquired in the inputting step with a reference input pattern as for the appearance frequency.

36. The abnormality detection method according to claim 34,

wherein in the transition analyzing step, a duration time distribution that is a distribution of time required for the transition in the sequence of the input pattern is calculated as the analysis, and

in the comparing step, whether abnormality has occurred or not is judged by comparing the input pattern acquired in the inputting step with a reference input pattern as for the duration time distribution.

37. The abnormality detection method according to claim 34 further comprising a clustering step for transforming the sequence of the input pattern acquired in the inputting step into a cluster sequence that is a predetermined representative input pattern,

wherein in the transition analyzing step, the characteristic in the cluster sequence which is transformed in the clustering step is analyzed.

38. The abnormality detection method according to claim 34,

wherein the reference value is a value, for a reference event, obtained by acquiring the sequence of the input pattern in advance in the inputting step and analyzing the characteristic in the transition analyzing step.

39. The abnormality detection method according to claim 34,

wherein the reference value is a value specified by an operator.

40. The abnormality detection method according to claim 34,

wherein in the inputting step, a plurality of small motion detection sensors placed in a plurality of places in a house are included, and a sensor pattern that is a combination of data indicating whether there is a small motion or not outputted from a plurality of the small motion detection sensors is acquired as the input pattern.

41. The abnormality detection method according to claim 34,

wherein in the inputting step, the sequence of the input pattern is acquired from an operator.

42. The abnormality detection method according to claim 34,

wherein in the outputting step, it is reported to a predetermined destination via a transmission channel that abnormality has occurred.

43. The abnormality detection method according to claim 34,

wherein in the transition analyzing step, a Markov chain in the sequence of the input pattern is calculated and a characteristic of the calculated Markov chain is analyzed as the analysis.

44. The abnormality detection method according to claim 34,

wherein the reference value is a collection of local reference values in each time interval which satisfies a predetermined condition, and

in the comparing step, whether abnormality has occurred or not is judged by comparing the characteristic with the local reference value in said each time interval.

45. A program for detecting that abnormality has occurred in an event under observation, the program causing a computer to execute:

an inputting step for acquiring a sequence of an input pattern that is data which depends upon the event;

a transition analyzing step for analyzing a characteristic of a transition in the acquired sequence of the input pattern;

a comparing step for comparing the analyzed characteristic of the transition with a predetermined reference value, and judging that abnormality has occurred in the event when the characteristic and the reference value are not approximate to each other within a predetermined range; and

an outputting step for outputting occurrence of abnormality when the comparing unit judges that abnormality has occurred.