Creating a personalized stress profile using renal doppler sonography

• US 8,529,447 B2
• Filed: 05/13/2011
• Issued: 09/10/2013
• Est. Priority Date: 05/13/2011
• Status: Active Grant

• Current Assignee: Fujitsu Limited
• Original Assignee: Fujitsu Limited
• Inventors: Jain, Jawahar, Marvit, David Loren, Bahadur, Madan, Gandhi, Shreyans
• Primary Examiner(s): Thomson, Bill
• Assistant Examiner(s): Sands, Davin K
• Application Number:
  US13/107,615
• Publication Number:
  US 20120289792A1
• Time in Patent Office: 851 Days
• Field of Search: None
• US Class Current: 600/300
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/0024 for multiple sensor units attached to the patient, e.g. using a body or personal area network
• CPC Class Codes: A61B 5/165 Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/7232 involving compression of the physiological signal, e.g. to extend the signal recording period
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

In particular embodiments, a method includes accessing data streams from a renal Doppler sonograph and one or more of a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, or a mood sensor monitoring a person, and generating a stress model of the person based on the data streams.

Claims

1. A method comprising, by one or more processors associated with one or more computing devices:

accessing, by one or more of the processors, one or more data streams from a plurality of sensors, the sensors comprising a renal Doppler sonograph and one or more of a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, or a mood sensor, the data streams comprising renal-Doppler data of a person from the renal Doppler sonograph and one or more of heart-rate data of the person from the heart-rate monitor, blood-pressure data of the person from the blood-pressure monitor, pulse-oximetry data of the person from the pulse oximeter, or self-reported mood data of the person from the mood sensor; and

generating, by one or more of the processors, a stress model of the person based on the data streams, the stress model comprising baseline renal-Doppler data of the person, and one or more of baseline heart-rate data of the person, baseline blood-pressure data of the person, baseline pulse-oximetry data of the person, or baseline self-reported mood data of the person, wherein the baseline renal-Doppler data measures a stress response of the sympathetic nervous system of the person, and wherein the stress model correlates the baseline renal-Doppler data of the person with one or more of the baseline heart-rate data of the person, the baseline blood-pressure data of the person, the baseline pulse-oximetry data of the person, or the baseline self-reported mood data of the person.

2. The method of claim 1, wherein one or more of the sensors is affixed to the person'"'"'s body.

3. The method of claim 1, wherein the renal-Doppler data is renal-blood-velocity data.

4. The method of claim 1, wherein the renal-Doppler data is renal-blood-flow data.

5. The method of claim 1, wherein the stress model correlates a stress index of the person with the baseline renal-Doppler data of the person.

6. The method of claim 1, wherein the stress model comprises the baseline heart-rate data of the person, and wherein the stress model correlates a stress index of the person with the baseline heart-rate data of the person.

7. The method of claim 1, wherein the stress model comprises the baseline blood-pressure data of the person, and wherein the stress model correlates a stress index of the person with the baseline blood-pressure data of the person.

8. The method of claim 1, wherein the stress model comprises the baseline pulse-oximetry data of the person, and wherein the stress model correlates a stress index of the person with the baseline pulse-oximetry data of the person.

9. The method of claim 1, wherein the stress model comprises the baseline self-reported mood data of the person, and wherein the stress model correlates a stress index of the person with the baseline self-reported mood data of the person.

10. The method of claim 1, wherein the stress model comprises an algorithm that comprises a plurality of variables based on the baseline renal-Doppler data and one or more of the baseline heart-rate data of the person, the baseline blood-pressure data of the person, the baseline pulse-oximetry data of the person, or the baseline self-reported mood data of the person.

11. The method of claim 1, wherein the stress model comprises the baseline self-reported mood data of the person, and wherein the baseline self-reported mood data of the person is used to validate the stress model of the person.

12. The method of claim 1, wherein the stress model comprises the baseline self-reported mood data of the person, and wherein the baseline self-reported mood data of the person is used to identify correlations between a stress index of the person with one or more of the baseline renal-Doppler data, the baseline heart-rate data of the person, the baseline blood-pressure data of the person, or the baseline pulse oximetry data of the person.

13. The method of claim 1, wherein:

a first set of renal-Doppler data was collected from the person when the person is exposed to a particular stressor;

and a second set of renal-Doppler data was collected from the person when the person is not exposed to the particular stressor.

14. The method of claim 1, wherein:

a first set of renal-Doppler data was collected from the person when the person is substantially stressed; and

a second set of renal-Doppler data was collected from the person when the person is substantially unstressed.

15. The method of claim 1, wherein:

the plurality of sensors further comprise a behavioral sensor; and

the data streams further comprise behavioral data of the person from the behavioral sensor.

16. The method of claim 1, wherein:

the plurality of sensors further comprise an electrocardiograph; and

the data streams further comprise electrocardiograph data of the person from the electrocardiograph.

17. The method of claim 1, wherein:

the plurality of sensors further comprise a glucocorticoid meter; and

the data streams further comprise glucocorticoid data of the person from the glucocorticoid meter.

18. The method of claim 1, wherein:

the plurality of sensors further comprise a respiration sensor; and

the data streams further comprise respiration data of the person from the respiration sensor.

19. The method of claim 1, wherein:

the plurality of sensors further comprise a galvanic-skin-response sensor; and

the data streams further comprise galvanic-skin-response data of the person from the galvanic-skin-response sensor.

20. An apparatus comprising:

one or more processors; and

a memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to;

access one or more data streams from a plurality of sensors, the sensors comprising a renal Doppler sonograph and one or more of a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, or a mood sensor, the data streams comprising renal-Doppler data of a person from the renal Doppler sonograph and one or more of heart-rate data of the person from the heart-rate monitor, blood-pressure data of the person from the blood-pressure monitor, pulse-oximetry data of the person from the pulse oximeter, or self-reported mood data of the person from the mood sensor; and

generate a stress model of the person based on the data streams, the stress model comprising baseline renal-Doppler data of the person, and one or more of baseline heart-rate data of the person, baseline blood-pressure data of the person, baseline pulse-oximetry data of the person, or baseline self-reported mood data of the person, wherein the baseline renal-Doppler data measures a stress response of the sympathetic nervous system of the person, and wherein the stress model correlates the baseline renal-Doppler data of the person with one or more of the baseline heart-rate data of the person, the baseline blood-pressure data of the person, the baseline pulse-oximetry data of the person, or the baseline self-reported mood data of the person.

21. The apparatus of claim 20, wherein one or more of the sensors is affixed to the person'"'"'s body.

22. The apparatus of claim 20, wherein the renal-Doppler data is renal-blood-velocity data.

23. The apparatus of claim 20, wherein the renal-Doppler data is renal-blood-flow data.

24. The apparatus of claim 20, wherein the stress model correlates a stress index of the person with the baseline renal-Doppler data of the person.

25. The apparatus of claim 20, wherein the stress model comprises the baseline heart-rate data of the person, and wherein the stress model correlates a stress index of the person with the baseline heart-rate data of the person.

26. The apparatus of claim 20, wherein the stress model comprises the baseline blood-pressure data of the person, and wherein the stress model correlates a stress index of the person with the baseline blood-pressure data of the person.

27. The apparatus of claim 20, wherein the stress model comprises the baseline pulse-oximetry data of the person, and wherein the stress model correlates a stress index of the person with the baseline pulse-oximetry data of the person.

28. The apparatus of claim 20, wherein the stress model comprises the baseline self-reported mood data of the person, and wherein the stress model correlates a stress index of the person with the baseline self-reported mood data of the person.

29. The apparatus of claim 20, wherein the stress model comprises an algorithm that comprises a plurality of variables based on the baseline renal-Doppler data and one or more of the baseline heart-rate data of the person, the baseline blood-pressure data of the person, the baseline pulse-oximetry data of the person, or the baseline self-reported mood data of the person.

30. The apparatus of claim 20, wherein the stress model comprises the baseline self-reported mood data of the person, and wherein the baseline mood data of the person is used to validate the self-reported stress model of the person.

31. The apparatus of claim 20, wherein the stress model comprises the baseline self-reported mood data of the person, and wherein the baseline self-reported mood data of the person is used to identify correlations between a stress index of the person with one or more of the baseline renal-Doppler data, the baseline heart-rate data of the person, the baseline blood-pressure data of the person, or the baseline pulse oximetry data of the person.

32. The apparatus of claim 20, wherein:

a first set of renal-Doppler data was collected from the person when the person is exposed to a particular stressor; and

a second set of renal-Doppler data was collected from the person when the person is not exposed to the particular stressor.

33. The apparatus of claim 20, wherein:

a first set of renal-Doppler data was collected from the person when the person is substantially stressed; and

a second set of renal-Doppler data was collected from the person when the person is substantially unstressed.

34. The apparatus of claim 20, wherein:

the plurality of sensors further comprise a behavioral sensor; and

the data streams further comprise behavioral data of the person from the behavioral sensor.

35. The apparatus of claim 20, wherein:

the plurality of sensors further comprise an electrocardiograph; and

the data streams further comprise electrocardiograph data of the person from the electrocardiograph.

36. The apparatus of claim 20, wherein:

the plurality of sensors further comprise a glucocorticoid meter; and

the data streams further comprise glucocorticoid data of the person from the glucocorticoid meter.

37. The apparatus of claim 20, wherein:

the plurality of sensors further comprise a respiration sensor; and

the data streams further comprise respiration data of the person from the respiration sensor.

38. The apparatus of claim 20, wherein:

the plurality of sensors further comprise a galvanic-skin-response sensor; and

the data streams further comprise galvanic-skin-response data of the person from the galvanic-skin-response sensor.

39. One or more computer-readable non-transitory storage media embodying software that is operable when executed to:

access one or more data streams from a plurality of sensors, the sensors comprising a renal Doppler sonograph and one or more of a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, or a mood sensor, the data streams comprising renal-Doppler data of a person from the renal Doppler sonograph and one or more of heart-rate data of the person from the heart-rate monitor, blood-pressure data of the person from the blood-pressure monitor, pulseoximetry data of the person from the pulse oximeter, or self-reported mood data of the person from the mood sensor; and

generate a stress model of the person based on the data streams, the stress model comprising baseline renal-Doppler data of the person, and one or more of baseline heart-rate data of the person, baseline blood-pressure data of the person, baseline pulse-oximetry data of the person, or baseline self-reported mood data of the person, wherein the baseline renal-Doppler data measures a stress response of the sympathetic nervous system of the person, and wherein the stress model correlates the baseline renal-Doppler data of the person with one or more of the baseline heart-rate data of the person, the baseline blood-pressure data of the person, the baseline pulse-oximetry data of the person, or the baseline self-reported mood data of the person.

40. The media of claim 39, wherein one or more of the sensors is affixed to the person'"'"'s body.

41. The media of claim 39, wherein the renal-Doppler data is renal-blood-velocity data.

42. The media of claim 39, wherein the renal-Doppler data is renal-blood-flow data.

43. The media of claim 39, wherein the stress model correlates a stress index of the person with the baseline renal-Doppler data of the person.

44. The media of claim 39, wherein the stress model comprises the baseline heart-rate data of the person, and wherein the stress model correlates a stress index of the person with the baseline heart-rate data of the person.

45. The media of claim 39, wherein the stress model comprises the baseline blood-pressure data of the person, and wherein the stress model correlates a stress index of the person with the baseline blood-pressure data of the person.

46. The media of claim 39, wherein the stress model comprises the baseline pulse-oximetry data of the person, and wherein the stress model correlates a stress index of the person with the baseline pulse-oximetry data of the person.

47. The media of claim 39, wherein the stress model comprises the baseline self-reported mood data of the person, and wherein the stress model correlates a stress index of the person with the baseline self-reported mood data of the person.

48. The media of claim 39, wherein the stress model comprises an algorithm that comprises a plurality of variables based on the baseline renal-Doppler data and one or more of the baseline heart-rate data of the person, the baseline blood-pressure data of the person, the baseline pulse-oximetry data of the person, or the baseline self-reported mood data of the person.

49. The media of claim 39, wherein the stress model comprises the baseline self-reported mood data of the person, and wherein the baseline self-reported mood data of the person is used to validate the stress model of the person.

50. The media of claim 39, wherein the stress model comprises the baseline self-reported mood data of the person, and wherein the baseline self-reported mood data of the person is used to identify correlations between a stress index of the person with one or more of the baseline renal-Doppler data, the baseline heart-rate data of the person, the baseline blood-pressure data of the person, or the baseline pulse oximetry data of the person.

51. The media of claim 39, wherein:

a first set of renal-Doppler data was collected from the person when the person is exposed to a particular stressor; and

a second set of renal-Doppler data was collected from the person when the person is not exposed to the particular stressor.

52. The media of claim 39, wherein:

a first set of renal-Doppler data was collected from the person when the person is substantially stressed; and

a second set of renal-Doppler data was collected from the person when the person is substantially unstressed.

53. The media of claim 39, wherein:

the plurality of sensors further comprise a behavioral sensor; and

the data streams further comprise behavioral data of the person from the behavioral sensor.

54. The media of claim 39, wherein:

the plurality of sensors further comprise an electrocardiograph; and

the data streams further comprise electrocardiograph data of the person from the electrocardiograph.

55. The media of claim 39, wherein:

the plurality of sensors further comprise a glucocorticoid meter; and

the data streams further comprise glucocorticoid data of the person from the glucocorticoid meter.

56. The media of claim 39, wherein:

the plurality of sensors further comprise a respiration sensor; and

the data streams further comprise respiration data of the person from the respiration sensor.

57. The media of claim 39, wherein:

the plurality of sensors further comprise a galvanic-skin-response sensor; and

the data streams further comprise galvanic-skin-response data of the person from the galvanic-skin-response sensor.