Detecting Behavioral Deviations by Measuring Respiratory Patterns in Cohort Groups

• US 20090226043A1
• Filed: 03/05/2008
• Published: 09/10/2009
• Est. Priority Date: 03/05/2008
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Kraemer, James R., Angell, Robert Lee
• Granted Patent: US 8,218,871 B2
• Time in Patent Office: Days
• US Class Current: 382/115
• 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: G08B 21/0476 Cameras to detect unsafe condition, e.g. video cameras
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/80 for detecting, monitoring or modelling epidemics or pandemics, e.g. flu

Abstract

A computer implemented method, apparatus, and computer usable program code for detecting behavioral deviations in members of a cohort group. In one embodiment, a member of a cohort group is identified. Each member of the cohort group shares a common characteristic. Respiratory metadata associated with the member of the cohort group is received in real-time as the respiratory metadata is generated. The respiratory metadata describes respiration associated with the member of the cohort group. Patterns of respiratory changes are identified using the respiratory metadata. The respiratory metadata is analyzed to identify the patterns of respiratory changes. In response to the patterns of respiratory changes indicating behavioral deviations in the member of the cohort group, the member of the cohort group is identified as a person of interest.

Claims

1. A computer implemented method for detecting behavioral deviations in members of a cohort group, the computer implemented method comprising:

identifying a member of the cohort group, wherein each member of the cohort group shares a common characteristic;

retrieving respiratory metadata associated with the member of the cohort group in real-time as the respiratory metadata is generated, wherein the respiratory metadata describes respiration associated with the member of the cohort group;

identify patterns of respiratory changes using the respiratory metadata, wherein the respiratory metadata is analyzed to identify the patterns of respiratory changes; and

responsive to the patterns of respiratory changes indicating behavioral deviations in the member of the cohort group, identifying the member of the cohort group as a person of interest.

2. The computer implemented method of claim 1 further comprising:

receiving a continuous stream of video data associated with the member of the cohort group, wherein the continuous stream of video data is received from a set of cameras; and

analyzing the continuous stream of video data, by a video analysis system, to generate the respiratory metadata.

3. The computer implemented method of claim 1 further comprising:

receiving infrared data associated with the member of the cohort group, wherein the infrared data is received from a set of infrared sensors; and

analyzing the infrared data, by a video analysis system, to generate the respiratory metadata.

4. The computer implemented method of claim 3 wherein the respiratory metadata comprises metadata describing changes in infrared measurements of breath expired by the member of the cohort group.

5. The computer implemented method of claim 1 further comprising:

receiving audio data associated with the member of the cohort group, wherein the audio data is received from a set of audio sensors; and

analyzing the audio data, by video analysis system, to generate the respiratory metadata.

6. The computer implemented method of claim 5 wherein the respiratory metadata comprises metadata describing breath sounds and sounds associated with inspiration and expiration of air by a respiratory system of the member of the cohort group.

7. The computer implemented method of claim 1 further comprising:

analyzing event metadata associated with a plurality of individuals in real-time to identify individuals in the plurality of individuals that share the common characteristic, wherein the metadata describes events associated with each individual and patterns of behavior associated with the each individual; and

identifying the individuals in the plurality of individuals that share the common characteristic to form the cohort group.

8. The computer implemented method of claim 1 wherein the cohort group is a group of people in a transportation facility.

9. The computer implemented method of claim 7 wherein the transportation facility is an airport.

10. The computer implemented method of claim 1 further comprising:

analyzing the patterns of respiration to identify a rate of respiration associated with the member of the cohort group.

11. The computer implemented method of claim 1 wherein the respiratory metadata comprises metadata describing changes in movements of a rib cage and abdomen of the member of the cohort group and further comprising:

analyzing the respiratory metadata to determine characteristics of the respiration by the member of the cohort group, wherein the characteristics comprises at least one of deep breathing, shallow breathing, slow breathing, fast breathing, regular breathing, erratic breathing, abdominal breathing, or breathing from the chest.

12. The computer implemented method of claim 1 wherein the patterns of respiratory changes indicating behavioral deviations comprises at least one of patterns of respiratory changes indicating that the member of the cohort group is reacting in an atypical manner to external stimuli, the person is failing to display expected responses to external stimuli, the member of the cohort group is demonstrating an atypically high level of tension, the person is controlling their breathing, and the member of the cohort group is demonstrating a lack of emotion.

13. The computer implemented method of claim 1 further comprising:

responsive to identifying the member of the cohort group as the person of interest, increasing a level of monitoring associated with the person of interest.

14. A computer program product for detecting behavioral deviations in members of a cohort group, the computer program product comprising:

a computer readable medium;

program code stored on the computer readable medium for identifying a member of the cohort group, wherein each member of the cohort group shares a common characteristic;

program code stored on the computer readable medium for retrieving respiratory metadata associated with the member of the cohort group in real-time as the respiratory metadata is generated, wherein the respiratory metadata describes respiration associated with the member of the cohort group;

program code stored on the computer readable medium for identifying patterns of respiratory changes using the respiratory metadata, wherein the respiratory metadata is analyzed to identify the patterns of respiratory changes;

program code stored on the computer readable medium for identifying the member of the cohort group as a person of interest in response to the patterns of respiratory changes indicating behavioral deviations in the member of the cohort group.

15. The computer program product of claim 14 further comprising:

receiving a continuous stream of video data associated with the member of the cohort group, wherein the continuous stream of video data is received from a set of cameras; and

analyzing the continuous stream of video data, by video analysis system, to generate the respiratory metadata.

16. The computer program product of claim 14 further comprising:

receiving infrared data associated with the member of the cohort group, wherein the infrared data is received from a set of infrared sensors; and

analyzing the infrared data, by a video analysis system, to generate the respiratory metadata.

17. The computer program product of claim 14 further comprising:

receiving audio data associated with the member of the cohort group, wherein the audio data is received from a set of audio sensors; and

analyzing the audio data, by video analysis system, to generate the respiratory metadata.

18. The computer program product of claim 14 wherein the respiratory metadata comprises metadata describing changes in movements of a rib cage and abdomen of the member of the cohort group and further comprising:

analyzing the respiratory metadata to determine characteristics of the respiration by the member of the cohort group, wherein the characteristics comprises at least one of deep breathing, shallow breathing, slow breathing, fast breathing, regular breathing, erratic breathing, abdominal breathing, or breathing from the chest.

19. An apparatus for detecting behavioral deviations in members of a cohort group, the computer implemented method comprising:

an analysis server, wherein the analysis server identifies a member of the cohort group based on data associated with the member of the cohort group gathered in real time, and wherein each member of the cohort group shares a common characteristic;

a video analysis system, wherein the video analysis system generates respiratory metadata associated with the member of the cohort group in real-time, wherein the respiratory metadata describes respiration by the member of the cohort group;

a respiratory pattern recognition engine, wherein the respiratory pattern recognition engine identifies patterns of respiratory changes associated with the respiration by the member of the cohort group using the respiratory metadata, wherein the respiratory metadata is analyzed to identify the patterns of respiratory changes; and

a person of interest identification engine, wherein the person of interest identification engine identifies the member of the cohort group as a person of interest in response to the patterns of respiratory changes indicating behavioral deviations by the member of the cohort group.

20. The apparatus of claim 19 further comprising:

a set of digital video cameras, wherein the set of digital video cameras capture a continuous stream of video data associated with the member of the cohort group, wherein the video analysis system analyzes the continuous video stream to generate the respiratory metadata.

21. The apparatus of claim 19 further comprising:

a set of infrared sensors, wherein the set of infrared sensors capture infrared data associated with the member of the cohort group, wherein the video analysis system analyzes the infrared data generate the respiratory metadata.

22. The apparatus of claim 19 further comprising:

a set of audio sensors, wherein the set of audio sensors capture audio data associated with the member of the cohort group, wherein the video analysis system analyzes the audio data to generate the respiratory metadata.

23. The apparatus of claim 19 further comprising:

a cohort analysis component, wherein the cohort analysis component analyzes event metadata associated with a plurality of individuals to identify individuals in the plurality of individuals that share a common characteristic, wherein the metadata describes events associated with each individual and patterns of behavior associated with the each individual; and

wherein the cohort analysis component identifies the individuals in the plurality of individuals that share the common characteristic to form the cohort group.

24. The apparatus of claim 19 further comprising:

a risk assessment engine, wherein the risk assessment engine identifies a level of risk associated with the person of interest; and

wherein the risk assessment engine increases a level of monitoring associated with the person of interest in response to the level of risk associated with the person of interest exceeding a threshold level of risk.

25. The apparatus of claim 19 wherein the respiratory pattern recognition engine analyzes the patterns of respiration to identify a rate of respiration associated with the member of the cohort group.