Continuous monitoring of stress using a stress profile created by renal doppler sonography

• US 8,540,629 B2
• Filed: 05/13/2011
• Issued: 09/24/2013
• Est. Priority Date: 05/13/2011
• Status: Active Grant

• Current Assignee: Fujitsu Limited
• Original Assignee: Fujitsu Limited
• Inventors: Jain, Jawahar, Marvit, David Loren, Gandhi, Shreyans, Bahadur, Madan
• Primary Examiner(s): Thomson, Bill
• Assistant Examiner(s): Sands, Davin K
• Application Number:
  US13/107,746
• Publication Number:
  US 20120289794A1
• Time in Patent Office: 865 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

Abstract

In particular embodiments, a method includes accessing data streams from one or more of a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, a mood sensor, or an accelerometer monitoring a person, accessing a stress model, analyzing the data streams with respect to the stress model, and determining based on the analysis a current stress index of the person.

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 two or more of a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, a mood sensor, or an accelerometer, the data streams comprising one or more of heart-rate data of a 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, self-reported mood data of the person from the mood sensor, or accelerometer data of the person from the accelerometer;

accessing, by one or more of the processors, a stress model, the stress model comprising baseline renal-Doppler data and two or more of baseline heart-rate data, baseline blood-pressure data, baseline pulse-oximetry data, or baseline self-reported mood data, wherein the baseline-renal-Doppler data measures a stress response of the sympathetic nervous system, and the stress model correlates the baseline renal-Doppler data with two or more of the baseline heart-rate data, the baseline blood-pressure data, the baseline pulse-oximetry data, or the baseline self-reported mood data;

analyzing, by one or more of the processors, the data streams with respect to the stress model; and

determining, by one or more of the processors, based on the analysis a current stress index 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 baseline renal-Doppler data is renal-blood-velocity data.

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

5. The method of claim 1, wherein the stress model is a stress model of the person and comprises baseline data of the person.

6. The method of claim 1, wherein the stress model is a stress model of a second person and comprises baseline data of the second person, wherein the person and the second person are in the same subset of patients.

7. The method of claim 1, wherein the stress model is a stress model of a subset of patients and comprises baseline data of the subset of patients, wherein the person and the subset of patients share particular distinguishing characteristics.

8. The method of claim 1, wherein the data streams comprise heart-rate data of the person and wherein the stress model correlates a stress index of the person with the heart-rate data of the person.

9. The method of claim 1, wherein the data streams comprise blood-pressure data of the person and wherein the stress model correlates a stress index of the person with the blood-pressure data of the person.

10. The method of claim 1, wherein the data streams comprise pulse-oximetry data of the person and wherein the stress model correlates a stress index of the person with the pulse-oximetry data of the person.

11. The method of claim 1, wherein the data streams comprise self-reported mood data of the person and wherein the stress model correlates a stress index of the person with the self-reported mood data of the person.

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

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

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

15. The method of claim 1, wherein:

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

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

16. The method of claim 1, wherein:

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

a second set of data from the data streams was collected from the person when the person is substantially unstressed.

17. The method of claim 1, wherein:

a first set of data from the data streams was collected from the person when the person is engaged in a first activity; and

a second set of data from the data streams was collected from the person when the person is engaged in a second activity.

18. 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.

19. 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.

20. 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.

21. The method of claim 1, wherein:

the plurality of sensors further comprise an electromyograph; and

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

22. 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.

23. 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.

24. The method of claim 1, wherein determining based on the analysis the stress index of the person comprises determining based on the analysis whether the data streams indicate a change in the person'"'"'s stress index compared to a prior stress index of the person that precedes the current stress index of the person.

25. The method of claim 1, further comprising:

accessing a prior stress index of the person that precedes the current stress index of the person;

analyzing the current stress index and prior stress index of the person with respect to each other; and

determining whether there is a change in the stress index of the person based on the analysis of the current stress index and the prior stress index with respect to each other.

26. 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 two or more of a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, a mood sensor, or an accelerometer, the data streams comprising one or more of heart-rate data of a 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, self-reported mood data of the person from the mood sensor, or accelerometer data of the person from the accelerometer;

access a stress model, the stress model comprising baseline renal-Doppler data and two or more of baseline heart-rate data, baseline blood-pressure data, baseline pulse-oximetry data, or baseline self-reported mood data, wherein the baseline-renal-Doppler data measures a stress response of the sympathetic nervous system, and the stress model correlates the baseline renal-Doppler data with two or more of the baseline heart-rate data, the baseline blood-pressure data, the baseline pulse-oximetry data, or the baseline self-reported mood data;

analyze the data streams with respect to the stress model; and

determine based on the analysis a current stress index of the person.

27. The apparatus of claim 26, wherein one or more of the sensors is affixed to the person'"'"'s body.

28. The apparatus of claim 26, wherein the baseline renal-Doppler data is renal-blood-velocity data.

29. The apparatus of claim 26, wherein the baseline renal-Doppler data is renal-blood-flow data.

30. The apparatus of claim 26, wherein the stress model is a stress model of the person and comprises baseline data of the person.

31. The apparatus of claim 26, wherein the stress model is a stress model of a second person and comprises baseline data of the second person, wherein the person and the second person are in the same subset of patients.

32. The apparatus of claim 26, wherein the stress model is a stress model of a subset of patients and comprises baseline data of the subset of patients, wherein the person and the subset of patients share particular distinguishing characteristics.

33. The apparatus of claim 26, wherein the data streams comprise heart-rate data of the person and wherein the stress model correlates a stress index of the person with the heart-rate data of the person.

34. The apparatus of claim 26, wherein the data streams comprise blood-pressure data of the person and wherein the stress model correlates a stress index of the person with the blood-pressure data of the person.

35. The apparatus of claim 26, wherein the data streams comprise pulse-oximetry data of the person and wherein the stress model correlates a stress index of the person with the pulse-oximetry data of the person.

36. The apparatus of claim 26, wherein the data streams comprise self-reported mood data of the person and wherein the stress model correlates a stress index of the person with the self-reported mood data of the person.

37. The apparatus of claim 26, wherein the stress model comprises an algorithm that comprises a plurality of variables based on two or more of the heart-rate data of the person, the blood-pressure data of the person, the pulse-oximetry data of the person, or the self-reported mood data of the person.

38. The apparatus of claim 26, wherein the stress model comprises the baseline self-reported mood data and wherein the baseline self-reported mood data is used to validate the stress model of the person.

39. The apparatus of claim 26, wherein the stress model comprises the baseline self-reported mood data and wherein the baseline self-reported mood data is used to identify correlations between a stress index of a person with one or more of the baseline renal-Doppler data, the baseline heart-rate data, the baseline blood-pressure data, or the baseline pulse-oximetry data.

40. The apparatus of claim 26, wherein:

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

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

41. The apparatus of claim 26, wherein:

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

a second set of data from the data streams was collected from the person when the person is substantially unstressed.

42. The apparatus of claim 26, wherein:

a first set of data from the data streams was collected from the person when the person is engaged in a first activity; and

a second set of data from the data streams was collected from the person when the person is engaged in a second activity.

43. The apparatus of claim 26, 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.

44. The apparatus of claim 26, wherein:

the plurality of sensors further comprise an electrocardiograph; and

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

45. The apparatus of claim 26, 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.

46. The apparatus of claim 26, wherein:

the plurality of sensors further comprise an electromyograph; and

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

47. The apparatus of claim 26, 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.

48. The apparatus of claim 26, 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.

49. The apparatus of claim 26, wherein to determine based on the analysis the stress index of the person comprises to determine based on the analysis whether the data streams indicate a change in the person'"'"'s stress index compared to a prior stress index of the person that precedes the current stress index of the person.

50. The apparatus of claim 26, the apparatus further operable when executing instructions to:

access a prior stress index of the person that precedes the current stress index of the person;

analyze the current stress index and prior stress index of the person with respect to each other; and

determine whether there is a change in the stress index of the person based on the analysis of the current stress index and the prior stress index with respect to each other.

51. 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 two or more of a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, a mood sensor, or an accelerometer, the data streams comprising one or more of heart-rate data of a 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, self-reported mood data of the person from the mood sensor, or accelerometer data of the person from the accelerometer;

access a stress model, the stress model comprising baseline renal-Doppler data and two or more of baseline heart-rate data, baseline blood-pressure data, baseline pulse-oximetry data, or baseline self-reported mood data, wherein the baseline-renal-Doppler data measures a stress response of the sympathetic nervous system, and the stress model correlates the baseline renal-Doppler data with two or more of the baseline heart-rate data, the baseline blood-pressure data, the baseline pulse-oximetry data, or the baseline self-reported mood data;

analyze the data streams with respect to the stress model; and

determine based on the analysis a current stress index of the person.

52. The media of claim 51, wherein one or more of the sensors is affixed to the person'"'"'s body.

53. The media of claim 51, wherein the baseline renal-Doppler data is renal-blood-velocity data.

54. The media of claim 51, wherein the baseline renal-Doppler data is renal-blood-flow data.

55. The media of claim 51, wherein the stress model is a stress model of the person and comprises baseline data of the person.

56. The media of claim 51, wherein the stress model is a stress model of a second person and comprises baseline data of the second person, wherein the person and the second person are in the same subset of patients.

57. The media of claim 51, wherein the stress model is a stress model of a subset of patients and comprises baseline data of the subset of patients, wherein the person and the subset of patients share particular distinguishing characteristics.

58. The media of claim 51, wherein the data streams comprise heart-rate data of the person and wherein the stress model correlates a stress index of the person with the heart-rate data of the person.

59. The media of claim 51, wherein the data streams comprise blood-pressure data of the person and wherein the stress model correlates a stress index of the person with the blood-pressure data of the person.

60. The media of claim 51, wherein the data streams comprise pulse-oximetry data of the person and wherein the stress model correlates a stress index of the person with the pulse-oximetry data of the person.

61. The media of claim 51, wherein the data streams comprise self-reported mood data of the person and wherein the stress model correlates a stress index of the person with the self-reported mood data of the person.

62. The media of claim 51, wherein the stress model comprises an algorithm that comprises a plurality of variables based on two or more of the heart-rate data of the person, the blood-pressure data of the person, the pulse-oximetry data of the person, or the self-reported mood data of the person.

63. The media of claim 51, wherein the stress model comprises the baseline self-reported mood data and wherein the baseline self-reported mood data is used to validate the stress model of the person.

64. The media of claim 51, wherein the stress model comprises the baseline self-reported mood data and wherein the baseline self-reported mood data is used to identify correlations between a stress index of a person with one or more of the baseline renal-Doppler data, the baseline heart-rate data, the baseline blood-pressure data, or the baseline pulse-oximetry data.

65. The media of claim 51, wherein:

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

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

66. The media of claim 51, wherein:

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

a second set of data from the data streams was collected from the person when the person is substantially unstressed.

67. The media of claim 51, wherein:

a first set of data from the data streams was collected from the person when the person is engaged in a first activity; and

a second set of data from the data streams was collected from the person when the person is engaged in a second activity.

68. The media of claim 51, 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.

69. The media of claim 51, wherein:

the plurality of sensors further comprise an electrocardiograph; and

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

70. The media of claim 51, 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.

71. The media of claim 51, wherein:

the plurality of sensors further comprise an electromyograph; and

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

72. The media of claim 51, 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.

73. The media of claim 51, 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.

74. The media of claim 51, wherein to determine based on the analysis the stress index of the person comprises to determine based on the analysis whether the data streams indicate a change in the person'"'"'s stress index compared to a prior stress index of the person that precedes the current stress index of the person.

75. The media of claim 51, the media embodying instructions that are further operable when executed to:

access a prior stress index of the person that precedes the current stress index of the person;

analyze the current stress index and prior stress index of the person with respect to each other; and

determine whether there is a change in the stress index of the person based on the analysis of the current stress index and the prior stress index with respect to each other.