METHOD AND APPARATUS FOR DETECTION AND PREDICTION OF EVENTS BASED ON CHANGES IN BEHAVIOR

• US 20140279740A1
• Filed: 03/15/2013
• Published: 09/18/2014
• Est. Priority Date: 03/15/2013
• Status: Active Grant

• Current Assignee: Nordic Technology Group Incorporated
• Original Assignee: Nordic Technology Group Incorporated
• Inventors: Wernevi, Erik, Apsell, Sheldon
• Granted Patent: US 9,710,761 B2
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

A computer-implemented process for detecting and predicting events occurring to a person, includes: observing, using a sensor, a reading of a parameter of a body part of the person which is one of: horizontal location, vertical height, orientation, velocity, and time of observation, wherein the reading corresponds to less information than needed to define the person'"'"'s posture; receiving the reading into a computer memory; determining from the received reading a pattern of behavior; detecting a change in behavior; identifying from the change in behavior a combination of one or more readings corresponding to an abnormal event; and producing an alert signal when the combination of one or more readings corresponding to the abnormal event is identified. The process may be practiced using a computing machine including a computer memory; a sensor; and a computer processor.

Claims

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

observing, using a sensor, a reading of a parameter of a body part of the person which is one of;

horizontal location, vertical height, orientation, velocity, and time of observation, wherein the reading corresponds to less information than needed to define the person'"'"'s posture;

receiving the reading into a computer memory;

determining from the received reading a pattern of behavior;

detecting a change in behavior;

identifying from the change in behavior a combination of one or more readings corresponding to an abnormal event; and

producing an alert signal when the combination of one or more readings corresponding to the abnormal event is identified.

2. The computer-implemented process of claim 1, wherein identifying further comprises identifying a future abnormal event likely to occur.

3. The computer-implemented process of claim 1, wherein observing the reading of the parameter of the body part further comprises:

sensing the parameter with respect to the person'"'"'s head.

4. The computer-implemented process of claim 1, wherein observing the reading of the parameter of the body part further comprises:

sensing the parameter with respect to the person'"'"'s torso.

5. The computer-implemented process of claim 1, wherein observing the reading of the parameter of the body part further comprises:

sensing the parameter with respect to one of the person'"'"'s limbs.

6. The computer-implemented process of claim 1, wherein observing the reading of the parameter of the body part further comprises:

sensing the parameter with respect to a combination of two or more of the person'"'"'s head, the person'"'"'s torso, and one of the person'"'"'s limbs, wherein the combination is less than needed to define the person'"'"'s posture.

7. The computer-implemented process of claim 1, wherein identifying further comprises:

identifying the combination of one or more readings corresponding to the abnormal event when at least one other body part is obscured from the sensor.

8. The computer-implemented process of claim 3, wherein identifying further comprises:

identifying the combination of one or more readings corresponding to the abnormal event when at least one other body part is obscured from the sensor.

9. The computer-implemented process of claim 4, wherein identifying further comprises:

identifying the combination of one or more readings corresponding to the abnormal event when at least one other body part is obscured from the sensor.

10. The computer-implemented process of claim 5, wherein identifying further comprises:

identifying the combination of one or more readings corresponding to the abnormal event when at least one other body part is obscured from the sensor.

11. The computer-implemented process of claim 6, wherein identifying further comprises:

identifying the combination of one or more readings corresponding to the abnormal event when at least one other body part is obscured from the sensor.

12. The computer-implemented process of claim 1, further comprising:

computing velocity from a sequence of the readings.

13. The computer-implemented process of claim 1, further comprising:

identifying a combination of readings corresponding to a normal event.

14. The computer-implemented process of claim 13, wherein identifying a combination of readings corresponding to a normal event further comprises:

identifying a combination of readings representing an activity of daily living.

15. The computer-implemented process of claim 13, further comprising:

learning to differentiate between the normal event and the abnormal event by applying a statistical test to the sequence of readings.

16. The computer-implemented process of claim 15, wherein the statistical test is correlation.

17. The computer-implemented process of claim 1, wherein observation further comprises:

sensing an output of a wearable sensor.

18. The computer-implemented process of claim 1, wherein observation further comprises:

sensing an output of one of;

a visual camera and an infrared camera.

19. The computer-implemented process of claim 1, wherein observation further comprises:

sensing an output of a radio-wave measuring device.

20. The computer-implemented process of claim 1, wherein observation further comprises:

sensing an output of a light-wave measuring device.

41. The computer-implemented process of claim 1, further comprising:

computing horizontal location from a sequence of the readings.

42. The computer-implemented process of claim 1, further comprising:

computing vertical height from a sequence of the readings.

43. The computer-implemented process of claim 1, further comprising:

computing orientation from a sequence of the readings.

47. The computer-implemented process of claim 2, wherein identifying further comprises:

identifying the combination of one or more readings corresponding to the abnormal event when at least one other body part is obscured from the sensor.

49. The computer-implemented process of claim 1, wherein observation further comprises:

sensing an output of an acoustical detector.

21. A computing machine for detecting and predicting an event based on changes in behavior of a person in an absence of information regarding the person'"'"'s posture comprising:

a computer memory;

a sensor; and

a computer processor;

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

observing, using a sensor, a reading of a parameter of a body part of the person which is one of;

horizontal location, vertical height, orientation, velocity, and time of observation, wherein the reading corresponds to less information than needed to define the person'"'"'s posture;

receiving the reading into a computer memory;

determining from the received reading a pattern of behavior;

detecting a change in behavior;

identifying a combination of one or more readings corresponding to an abnormal event; and

producing an alert signal when the combination of one or more readings corresponding to the abnormal event is identified.

22. The computing machine of claim 21, wherein identifying further comprises identifying a future abnormal event likely to occur.

23. The computing machine of claim 21, wherein observing the reading of the parameter of the body part further comprises:

sensing the parameter with respect to the person'"'"'s head.

24. The computing machine of claim 21, wherein observing the reading of the parameter of the body part further comprises:

sensing the parameter with respect to the person'"'"'s torso.

25. The computing machine of claim 21, wherein observing the reading of the parameter of the body part further comprises:

sensing the parameter with respect to one of the person'"'"'s limbs.

26. The computing machine of claim 21, wherein observing the reading of the parameter of the body part further comprises:

sensing the parameter with respect to a combination of two or more of the person'"'"'s head, the person'"'"'s torso, and one of the person'"'"'s limbs, wherein the combination is less than needed to define the person'"'"'s posture.

27. The computing machine of claim 21, wherein identifying further comprises:

identifying the combination of one or more readings corresponding to the abnormal event when at least one other body part is obscured from the sensor.

28. The computing machine of claim 23, wherein identifying further comprises:

identifying the combination of one or more readings corresponding to the abnormal event when at least one other body part is obscured from the sensor.

29. The computing machine of claim 24, wherein identifying further comprises:

identifying the combination of one or more readings corresponding to the abnormal event when at least one other body part is obscured from the sensor.

30. The computing machine of claim 25, wherein identifying further comprises:

identifying the combination of one or more readings corresponding to the abnormal event when at least one other body part is obscured from the sensor.

31. The computing machine of claim 26, wherein identifying further comprises:

identifying the combination of one or more readings corresponding to the abnormal event when at least one other body part is obscured from the sensor.

32. The computing machine of claim 21, wherein the sensor comprises:

a velocity sensor.

33. The computing machine of claim 21, the instructions further comprising:

identifying a combination of readings corresponding to a normal event.

34. The computing machine of claim 33, wherein identifying a combination of readings corresponding to a normal event further comprises:

identifying a combination of readings representing an activity of daily living.

35. The computing machine of claim 33, the instructions further comprising:

learning to differentiate between the normal event and the abnormal event by applying a statistical test to the sequence of readings.

36. The computing machine of claim 35, wherein the statistical test is correlation.

37. The computing machine of claim 21, the sensor comprising:

a wearable sensor.

38. The computing machine of claim 21, the sensor comprising:

one of;

a visual camera and an infrared camera.

39. The computing machine of claim 21, the sensor comprising:

a radio-wave measuring device.

40. The computing machine of claim 21, the sensor comprising:

a light-wave measuring device.

44. The computing machine of claim 21, wherein the sensor comprises:

a vertical height sensor.

45. The computing machine of claim 21, wherein the sensor comprises:

a horizontal location sensor.

46. The computing machine of claim 21, wherein the sensor comprises:

an orientation sensor.

48. The computing machine of claim 22, wherein identifying further comprises:

identifying the combination of one or more readings corresponding to the abnormal event when at least one other body part is obscured from the sensor.

50. The computing machine of claim 21, the sensor comprising:

an acoustical detector.