METHODS FOR DATA COLLECTION AND ANALYSIS FOR EVENT DETECTION

• US 20150302310A1
• Filed: 12/12/2014
• Published: 10/22/2015
• Est. Priority Date: 03/15/2013
• Status: Abandoned Application

• Current Assignee: Nordic Technology Group Incorporated
• Original Assignee: Nordic Technology Group Incorporated
• Inventors: Wernevi, Erik, Napoli, Joshua, Apsell, Sheldon
• Application Number:
  US14/569,063
• Publication Number:
  US 20150302310A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/045 Explanation of inference; Explainable artificial intelligence [XAI]; Interpretable artificial intelligence
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Behavior modeling includes how to detect and/or predict events based on observed changes in behavior. Detection of behavior that indicates possible adverse health events is performed by remote observation of a person'"'"'s behavior. Captured data is correlated with an appropriate person, without identifying the person. People are associated with objects/locations, in the environment based on how the people relate to those objects/locations. Thus, people are identified based on their body characteristics or movement. Person specific data captured is labeled with unique identifiers. The location of certain objects/locations is correlated with the behavior profile to capture and analyze a nested pattern within a larger behavior pattern. Next to certain objects, certain types of behaviors/movements are expected. However, if the movement at a determined point in time deviates significantly from “normal” behavior patterns, such deviation may be an indication that something is wrong.

Claims

1. A process for detecting and predicting events occurring to a person, comprising:

observing, using a sensor, a plurality of readings of a parameter of the person, wherein the parameter is one of;

horizontal location, vertical height, and time of observation;

storing the readings in a computer memory;

determining, by a processor, a pattern of behavior based on the readings;

storing a pattern of interest based on the readings;

identifying from the readings the pattern of interest;

distinguishing a person that exhibits the pattern of interest, from other people or animate objects;

labeling the person with a unique identifying label;

linking data captured about the person with the identifying label;

determining conditions under which a subset of the readings correspond to an occurrence of an event; and

detecting when the subset of readings corresponds to the occurrence of the event.

2. The process of claim 1, wherein observing the readings further comprises:

sensing the parameter with respect to a combination of two or more of the person'"'"'s body parts selected from the group consisting of a head, a torso, a limb, and combinations thereof.

3. The process of claim 1, wherein observing the readings further comprises:

sensing the parameter with respect to one body part selected from the group consisting of a head, a torso, and a limb.

4. The process of claim 1, wherein said detecting further comprises producing an electronic signal that controls another device that has an electronic control and storing readings corresponding to the event in memory for later retrieval and analysis.

5. The process of claim 1, wherein the pattern of interest is exhibited by person'"'"'s way of moving in general.

6. The process of claim 1, wherein pattern of interest is exhibited by person way of moving in a specific location.

7. The process of claim 1, wherein pattern of interest is exhibited by a person moving next to, or around, a specific object or a person using a specific object.

8. The process of claim 1, wherein pattern of interest is a result, or an intrinsic part, of a person body characteristic.

9. The process of claim 1, wherein said labeling further comprises that no personal identifying information for the person is either captured or stored.

10. The process of claim 1, wherein determining conditions under which the readings correspond to the occurrence of an event is done by comparison to a threshold, application of a conditional rule, or application of a statistical test.

11. The process of claim 1, wherein determining conditions under which the received reading correspond to the occurrence of an event is done by an agent external to the system.

12. The process of claim 1, wherein determining conditions under which the received reading correspond to the occurrence of an event is done by the system based on historic movement profile through identification of an event that has previously resulted in an adverse health incident or other incident of interest.

13. The process of claim 1, wherein said detecting further comprises detecting a change in behavior and identifying from the change in behavior a combination of one or more readings corresponding to an abnormal event.

14. The process of claim 1, wherein determining the pattern of interest is done from the historic movement profile of the person.

15. The process of claim 1, wherein determining the pattern of interest based on the readings is done through use of behavior templates for such behavior that are created by an agent external to the system or created by recording the behavior by the person, by a different set of users, or by one or more actors.

16. The process of claim 1, wherein observing, using a sensor, a reading of a parameter of the person, or a body part of the person, includes velocity.

17. The process of claim 1, wherein observing, using a sensor, a reading of a parameter of the person, or a body part of the person, includes orientation.

18. The process of claim 1, wherein observing, using a sensor, a reading of a parameter of the person, or a body part of the person, includes velocity and orientation.

19. The process of claim 1, wherein determining, by a processor, a pattern of behavior based on the readings further comprises that the processor in a training mode identifies and stores a pattern for normal behavior or a pattern of interest.

20. The process of claim 1, wherein the user, for which the process is detecting and predicting events, is an animate object.

21. A computing machine for detecting and predicting an event based on changes in behavior of a person comprising:

a computer memory;

a sensor; and

a computer processor in communication with the computer memory and the sensor, wherein

the computer processor executes a sequence of instructions stored in the computer memory, including instructions for;

observing, using a sensor, a plurality of readings of a parameter of the person, wherein the parameter is one of;

horizontal location, vertical height, and time of observation;

storing the readings in a computer memory;

determining, by a processor, a pattern of behavior based on the readings;

storing a pattern of interest based on the readings;

identifying from the readings the pattern of interest;

distinguishing a person that exhibits the pattern of interest, from other people or animate objects;

labeling a person that exhibits the pattern of interest with an unique identifying label;

linking data captured about the person with the identifying label;

determining conditions under which a subset of the readings correspond to an occurrence of an event; and

detecting when the subset of readings corresponds to the occurrence of the event.