Continuous Monitoring of Stress Using Self-Reported Psychological or Behavioral Data

• US 20120289790A1
• Filed: 05/13/2011
• Published: 11/15/2012
• Est. Priority Date: 05/13/2011
• Status: Active Grant

• Current Assignee: Fujitsu Limited
• Original Assignee: Fujitsu Limited
• Inventors: Jain, Jawahar, Marvit, David Loren, Adler, B. Thomas, Reinhardt, Albert H. M., Balakrishnan, Rajalakshmi
• Granted Patent: US 8,622,899 B2
• Time in Patent Office: Days
• US Class Current: 600/301
• CPC Class Codes: A61B 2560/0242 adapted to measure environmental factors, e.g. temperature, pollution
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/165 Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/4848 Monitoring or testing the effects of treatment, e.g. of medication
• CPC Class Codes: A61B 5/7232 involving compression of the physiological signal, e.g. to extend the signal recording period
• CPC Class Codes: G16H 20/70 relating to mental therapies, e.g. psychological therapy or autogenous training
• CPC Class Codes: G16H 40/67 for remote operation

Abstract

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

Claims

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

accessing one or more data streams from a plurality of sensors, the sensors comprising a mood sensor and one or more of a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, or an accelerometer, wherein;

the data streams comprise mood data of a person from the mood sensor 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 accelerometer data of the person from the accelerometer;

a first data set from the data streams was collected from the person at a first time, the person being substantially stressed at the first time; and

a second data set from the data streams was collected from the person at a second time, the person being substantially unstressed at the second time;

analyzing the first data set and second data set with respect to each other;

determining a current stress index of the person based on the analysis of the first data set and second data set with respect to each other.

2. The method of claim 1, wherein analyzing the first data set and second data set with respect to each other comprises contextualizing one or more of the heart-rate data, the blood-pressure data, the pulse-oximetry data, or accelerometer data of the person with the mood data of the person.

3. 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 prior stress index with respect to each other.

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

5. The method of claim 1, further comprising accessing a stress model of the person, wherein determining the current stress index of the person is further based on the analysis of the first data set and second data set with respect to the stress model.

6. The method of claim 5, wherein the stress model of the person comprises baseline renal-Doppler data of the person, baseline mood 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 accelerometer data of the person.

7. The method of claim 6, wherein the baseline mood data of the person is used to validate the stress model of the person.

8. The method of claim 6, wherein the baseline mood data of the person 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 of the person, the baseline blood-pressure data of the person, or the baseline pulse oximetry data of the person

9. The method of claim 5, wherein the stress model comprises an algorithm that comprises a plurality of variables based on the mood data of the person and one 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 accelerometer data of the person.

10. The method of claim 5, 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.

11. The method of claim 1, wherein:

at the first time the person is exposed to a particular stressor; and

at the second time the person is not exposed to the particular stressor.

12. The method of claim 1, wherein:

at the first time the person is engaged in a first activity; and

at the second time the person is engaged in a second activity.

13. 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.

14. 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.

15. 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.

16. 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.

17. 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.

18. 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.

19. 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 mood sensor and one or more of a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, or an accelerometer, wherein;

the data streams comprise mood data of a person from the mood sensor 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 accelerometer data of the person from the accelerometer;

a first data set from the data streams was collected from the person at a first time, the person being substantially stressed at the first time; and

a second data set from the data streams was collected from the person at a second time, the person being substantially unstressed at the second time;

analyze the first data set and second data set with respect to each other;

determine a current stress index of the person based on the analysis of the first data set and second data set with respect to each other.

20. The apparatus of claim 19, wherein to analyze the first data set and second data set with respect to each other comprises to contextualize one or more of the heart-rate data, the blood-pressure data, the pulse-oximetry data, or accelerometer data of the person with the mood data of the person.

21. The apparatus of claim 19, 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 prior stress index with respect to each other.

22. The apparatus of claim 19, wherein one or more of the sensors is affixed to the person'"'"'s body.

23. The apparatus of claim 19, the apparatus further operable when executing instructions to access a stress model of the person, wherein to determine the current stress index of the person is further based on the analysis of the first data set and second data set with respect to the stress model.

24. The apparatus of claim 23, wherein the stress model of the person comprises baseline renal-Doppler data of the person, baseline mood 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 accelerometer data of the person.

25. The apparatus of claim 24, wherein the baseline mood data of the person is used to validate the stress model of the person.

26. The apparatus of claim 24, wherein the baseline mood data of the person 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 of the person, the baseline blood-pressure data of the person, or the baseline pulse oximetry data of the person

27. The apparatus of claim 23, wherein the stress model comprises an algorithm that comprises a plurality of variables based on the mood data of the person and one 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 accelerometer data of the person.

28. The apparatus of claim 23, 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.

29. The apparatus of claim 19, wherein:

at the first time the person is exposed to a particular stressor; and

at the second time the person is not exposed to the particular stressor.

30. The apparatus of claim 19, wherein:

at the first time the person is engaged in a first activity; and

at the second time the person is engaged in a second activity.

31. The apparatus of claim 19, 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.

32. The apparatus of claim 19, wherein:

the plurality of sensors further comprise an electrocardiograph; and

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

33. The apparatus of claim 19, 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.

34. The apparatus of claim 19, wherein:

the plurality of sensors further comprise an electromyograph; and

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

35. The apparatus of claim 19, 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.

36. The apparatus of claim 19, 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.

37. 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 mood sensor and one or more of a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, or an accelerometer, wherein;

the data streams comprise mood data of a person from the mood sensor 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 accelerometer data of the person from the accelerometer;

a first data set from the data streams was collected from the person at a first time, the person being substantially stressed at the first time; and

a second data set from the data streams was collected from the person at a second time, the person being substantially unstressed at the second time;

analyze the first data set and second data set with respect to each other;

determine a current stress index of the person based on the analysis of the first data set and second data set with respect to each other.

38. The media of claim 37, wherein to analyze the first data set and second data set with respect to each other comprises to contextualize one or more of the heart-rate data, the blood-pressure data, the pulse-oximetry data, or accelerometer data of the person with the mood data of the person.

39. The media of claim 37, 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 prior stress index with respect to each other.

40. The media of claim 37, wherein one or more of the sensors is affixed to the person'"'"'s body.

41. The media of claim 37, the media embodying instructions that are further operable when executed to access a stress model of the person, wherein to determine the current stress index of the person is further based on the analysis of the first data set and second data set with respect to the stress model.

42. The media of claim 41, wherein the stress model of the person comprises baseline renal-Doppler data of the person, baseline mood 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 accelerometer data of the person.

43. The media of claim 42, wherein the baseline mood data of the person is used to validate the stress model of the person.

44. The media of claim 42, wherein the baseline mood data of the person 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 of the person, the baseline blood-pressure data of the person, or the baseline pulse oximetry data of the person

45. The media of claim 41, wherein the stress model comprises an algorithm that comprises a plurality of variables based on the mood data of the person and one 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 accelerometer data of the person.

46. The media of claim 41, 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.

47. The media of claim 37, wherein:

at the first time the person is exposed to a particular stressor; and

at the second time the person is not exposed to the particular stressor.

48. The media of claim 37, wherein:

at the first time the person is engaged in a first activity; and

at the second time the person is engaged in a second activity.

49. The media of claim 37, 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.

50. The media of claim 37, wherein:

the plurality of sensors further comprise an electrocardiograph; and

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

51. The media of claim 37, 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.

52. The media of claim 37, wherein:

the plurality of sensors further comprise an electromyograph; and

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

53. The media of claim 37, 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.

54. The media of claim 37, 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.

55. A system comprising:

means for accessing one or more data streams from a plurality of sensors, the sensors comprising a mood sensor and one or more of a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, or an accelerometer, wherein;

the data streams comprise mood data of a person from the mood sensor 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 accelerometer data of the person from the accelerometer;

a first data set from the data streams was collected from the person at a first time, the person being substantially stressed at the first time; and

a second data set from the data streams was collected from the person at a second time, the person being substantially unstressed at the second time;

means for analyzing the first data set and second data set with respect to each other;

means for determining a current stress index of the person based on the analysis of the first data set and second data set with respect to each other.

56. The system of claim 55, wherein the means for analyzing the first data set and second data set with respect to each other comprises means for contextualizing one or more of the heart-rate data, the blood-pressure data, the pulse-oximetry data, or accelerometer data of the person with the mood data of the person.

57. The system of claim 55, further comprising:

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

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

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

58. The system of claim 55, wherein one or more of the sensors is affixed to the person'"'"'s body.

59. The system of claim 55, further comprising means for accessing a stress model of the person, wherein the means for determining the current stress index of the person is further based on the analysis of the first data set and second data set with respect to the stress model.

60. The system of claim 59, wherein the stress model of the person comprises baseline renal-Doppler data of the person, baseline mood 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 accelerometer data of the person.

61. The system of claim 60, wherein the baseline mood data of the person is used to validate the stress model of the person.

62. The system of claim 60, wherein the baseline mood data of the person 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 of the person, the baseline blood-pressure data of the person, or the baseline pulse oximetry data of the person

63. The system of claim 59, wherein the stress model comprises an algorithm that comprises a plurality of variables based on the mood data of the person and one 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 accelerometer data of the person.

64. The system of claim 59, 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.

65. The system of claim 55, wherein:

at the first time the person is exposed to a particular stressor; and

at the second time the person is not exposed to the particular stressor.

66. The system of claim 55, wherein:

at the first time the person is engaged in a first activity; and

at the second time the person is engaged in a second activity.

67. The system of claim 55, 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.

68. The system of claim 55, wherein:

the plurality of sensors further comprise an electrocardiograph; and

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

69. The system of claim 55, 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.

70. The system of claim 55, wherein:

the plurality of sensors further comprise an electromyograph; and

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

71. The system of claim 55, 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.

72. The system of claim 55, 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.