Calculating and monitoring a composite stress index

• US 9,189,599 B2
• Filed: 05/13/2011
• Issued: 11/17/2015
• Est. Priority Date: 05/13/2011
• Status: Active Grant

• Current Assignee: Fujitsu Limited
• Original Assignee: Fujitsu Limited
• Inventors: Adler, B. Thomas, Jain, Jawahar, Marvit, David Loren, Balakrishnan, Rajalakshmi, Gilman, Alexander, Stergiou, Stergios, Braun, Albert C., Reinhardt, Albert H. M.
• Primary Examiner(s): Thomson, William
• Assistant Examiner(s): Downey, John R
• Application Number:
  US13/107,540
• Publication Number:
  US 20120290215A1
• Time in Patent Office: 1,649 Days
• Field of Search: 600/300
• US Class Current: 1/1
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

In particular embodiments, a method includes accessing data streams from a first group of physiological sensors monitoring a person, a second group of deconfounding sensors monitoring the person, and a third group of sensors monitoring a stressor, analyzing data sets collected from the person when the person is exposed and not exposed to the stressor, and determining a current stress factor for the stressor with respect to the person based on the analysis.

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, wherein;

the sensors comprise;

a glucocorticoid meter and one or more first sensors selected from a first group of sensor types consisting of a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, and an accelerometer;

one or two second sensors selected from a second group of sensor types consisting of a mood sensor, a behavioral sensor, and an environmental sensor;

one third sensor also selected from the second group of sensor types, the third sensor being different in sensor type from each of the two second sensors;

the data streams comprise glucocorticoid data of a person from the glucocorticoid meter 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, accelerometer data of the person from the accelerometer, self-reported mood data of the person from the mood sensor, behavioral data of the person from the behavioral sensor, or environmental data from the environmental sensor;

a first data set from the data streams was collected from the person at a first time, the person having been exposed to a stressor at the first time, as indicated by data in the first data set from the glucocorticoid meter and the third sensor; and

a second data set from the data streams was collected from the person at a second time, the person not having been exposed to the stressor at the second time, as indicated by data in the second data set from the glucocorticoid meter and the third sensor;

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

analyzing, by one or more of the processors, the first data set and second data set with respect to each other and with respect to the stress model;

determining, by one or more of the processors, a current stress factor for the stressor with respect to the person based on the analysis of the first data set and second data set with respect to each other and with respect to the stress model; and

transmitting, by one or more of the processors, a warning to a designated computing system referencing the current stress factor if the current stress factor deviates from a set of control parameters.

2. The method of claim 1, wherein the environmental sensor is selected from a fourth group consisting of a barometer, a weather sensor, a pollen counter, a location sensor, a seismometer, an altimeter, a hydrometer, a decibel meter, a light meter, a thermometer, a wind sensor, and a traffic sensor.

3. The method of claim 1, wherein the environmental sensor is a data feed.

4. The method of claim 1, determining the current stress factor for the stressor with respect to the person based on the analysis of the first data set and second data set with respect to each other comprises:

determining a first stress index of the person based on the first data set;

determining a second stress index of the person based on the second data set; and

determining a current stress factor for the stressor with respect to the person based on a comparison of the first stress index and second stress index of the person with respect to each other.

5. The method of claim 4, wherein the data from the second sensors is used to validate the stress index of the person.

6. The method of claim 1, wherein the data from the second sensors is used to validate the current stress factor of the person.

7. The method of claim 1, further comprising:

accessing a prior stress factor for the stressor of the person that precedes the current stress factor;

analyzing the current stress factor and prior stress factor for the stressor of the person with respect to each other; and

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

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

9. 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, the accelerometer data of the person, or the self-reported mood data of the person.

10. The method of claim 1, wherein:

at the first time the person was stressed; and

at the second time the person was unstressed.

11. The method of claim 1, wherein:

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

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

12. The method of claim 1, wherein:

the first group further consists of an electrocardiograph; and

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

13. The method of claim 1, wherein:

the first group further consists of an electromyograph; and

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

14. The method of claim 1, wherein:

the first group further consists of a respiration sensor; and

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

15. The method of claim 1, wherein:

the first group further consists of a galvanic-skin-response sensor; and

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

16. 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, wherein;

the sensors comprise;

a glucocorticoid meter and one or more first sensors selected from a first group of sensor types consisting of a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, and an accelerometer;

one or two second sensors selected from a second group of sensor types consisting of a mood sensor, a behavioral sensor, and an environmental sensor;

one third sensor also selected from the second group of sensor types, the third sensor being different in sensor type from each of the two second sensors;

the data streams comprise glucocorticoid data of a person from the glucocorticoid meter 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, accelerometer data of the person from the accelerometer, self-reported mood data of the person from the mood sensor, behavioral data of the person from the behavioral sensor, or environmental data from the environmental sensor;

a first data set from the data streams was collected from the person at a first time, the person having been exposed to a stressor at the first time, as indicated by data in the first data set from the glucocorticoid meter and the third sensor; and

a second data set from the data streams was collected from the person at a second time, the person not having been exposed to the stressor at the second time, as indicated by data in the second data set from the glucocorticoid meter and the third sensor;

access a stress model comprising baseline renal-Doppler data and baseline glucocorticoid data of the person, and two or more of baseline heart-rate data of the person, baseline blood-pressure data of the person, baseline pulse-oximetry data of the person, baseline accelerometer 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, and wherein the stress model correlates the baseline renal-Doppler data and the baseline glucocorticoid data of the person with the baseline heart-rate data of the person, the baseline blood-pressure data of the person, the baseline pulse-oximetry data of the person, the baseline accelerometer data of the person, or the baseline self-reported mood data of the person;

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

determine a current stress factor for the stressor with respect to the person based on the analysis of the first data set and second data set with respect to each other and with respect to the stress model; and

transmit a warning to a designated computing system of the person referencing the current stress factor if the current stress factor deviates from a set of control parameters.

17. The apparatus of claim 16, wherein the environmental sensor is selected from a fourth group consisting of a barometer, a weather sensor, a pollen counter, a location sensor, a seismometer, an altimeter, a hydrometer, a decibel meter, a light meter, a thermometer, a wind sensor, and a traffic sensor.

18. The apparatus of claim 16, wherein the environmental sensor is a data feed.

19. The apparatus of claim 16, wherein to determine the current stress factor for the stressor with respect to the person based on the analysis of the first data set and second data set with respect to each other comprises:

to determine a first stress index of the person based on the first data set;

to determine a second stress index of the person based on the second data set; and

to determine a current stress factor for the stressor with respect to the person based on a comparison of the first stress index and second stress index of the person with respect to each other.

20. The apparatus of claim 19, wherein the data from the second sensors is used to validate the stress index of the person.

21. The apparatus of claim 16, wherein the data from the second sensors is used to validate the current stress factor of the person.

22. The apparatus of claim 16, the apparatus further operable when executing instructions to:

access a prior stress factor for the stressor of the person that precedes the current stress factor;

analyze the current stress factor and prior stress factor for the stressor of the person with respect to each other; and

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

23. The apparatus of claim 16, wherein one or more of the sensors from the first group is affixed to the person'"'"'s body.

24. The apparatus of claim 16, 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, the accelerometer data of the person, or the self-reported mood data of the person.

25. The apparatus of claim 16, wherein:

at the first time the person was stressed; and

at the second time the person was unstressed.

26. The apparatus of claim 16, wherein:

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

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

27. The apparatus of claim 16, wherein:

the first group further consists of an electrocardiograph; and

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

28. The apparatus of claim 16, wherein:

the first group further consists of an electromyograph; and

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

29. The apparatus of claim 16, wherein:

the first group further consists of a respiration sensor; and

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

30. The apparatus of claim 16, wherein:

the first group further consists of a galvanic-skin-response sensor; and

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

31. 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, wherein;

the sensors comprise;

a glucocorticoid meter and one or more first sensors selected from a first group of sensor types consisting of a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, and an accelerometer;

one or two second sensors selected from a second group of sensor types consisting of a mood sensor, a behavioral sensor, and an environmental sensor;

one third sensor also selected from the second group of sensor types, the third sensor being different in sensor type from each of the two second sensors;

the data streams comprise glucocorticoid data of a person from the glucocorticoid meter 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, accelerometer data of the person from the accelerometer, self-reported mood data of the person from the mood sensor, behavioral data of the person from the behavioral sensor, or environmental data from the environmental sensor;

a first data set from the data streams was collected from the person at a first time, the person having been exposed to a stressor at the first time, as indicated by data in the first data set from the glucocorticoid meter and the third sensor; and

a second data set from the data streams was collected from the person at a second time, the person not having been exposed to the stressor at the second time, as indicated by data in the second data set from the glucocorticoid meter and the third sensor;

access a stress model comprising baseline renal-Doppler data and baseline glucocorticoid data of the person, and two or more of baseline heart-rate data of the person, baseline blood-pressure data of the person, baseline pulse-oximetry data of the person, baseline accelerometer 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, and wherein the stress model correlates the baseline renal-Doppler data and the baseline glucocorticoid data of the person with the baseline heart-rate data of the person, the baseline blood-pressure data of the person, the baseline pulse-oximetry data of the person, the baseline accelerometer data of the person, or the baseline self-reported mood data of the person;

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

determine a current stress factor for the stressor with respect to the person based on the analysis of the first data set and second data set with respect to each other and with respect to the stress model; and

transmit a warning to a designated computing system of the person referencing the current stress factor if the current stress factor deviates from a set of control parameters.

32. The media of claim 31, wherein the environmental sensor is selected from a fourth group consisting of a barometer, a weather sensor, a pollen counter, a location sensor, a seismometer, an altimeter, a hydrometer, a decibel meter, a light meter, a thermometer, a wind sensor, and a traffic sensor.

33. The media of claim 31, wherein the environmental sensor is a data feed.

34. The media of claim 31, wherein to determine the current stress factor for the stressor with respect to the person based on the analysis of the first data set and second data set with respect to each other comprises:

to determine a first stress index of the person based on the first data set;

to determine a second stress index of the person based on the second data set; and

to determine a current stress factor for the stressor with respect to the person based on a comparison of the first stress index and second stress index of the person with respect to each other.

35. The media of claim 34, wherein the data from the second sensors is used to validate the stress index of the person.

36. The media of claim 31, wherein the data from the second sensors is used to validate the current stress factor of the person.

37. The media of claim 31, the media embodying instructions further operable when executed to:

access a prior stress factor for the stressor of the person that precedes the current stress factor;

analyze the current stress factor and prior stress factor for the stressor of the person with respect to each other; and

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

38. The media of claim 31, wherein one or more of the sensors from the first group is affixed to the person'"'"'s body.

39. The media of claim 31, 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, the accelerometer data of the person, or the self-reported mood data of the person.

40. The media of claim 31, wherein:

at the first time the person was stressed; and

at the second time the person was unstressed.

41. The media of claim 31, wherein:

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

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

42. The media of claim 31, wherein:

the first group further consists of an electrocardiograph; and

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

43. The media of claim 31, wherein:

the first group further consists of an electromyograph; and

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

44. The media of claim 31, wherein:

the first group further consists of a respiration sensor; and

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

45. The media of claim 31, wherein:

the first group further consists of a galvanic-skin-response sensor; and

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

46. A system comprising:

means for accessing one or more data streams from a plurality of sensors, wherein;

the sensors comprise;

a glucocorticoid meter and one or more first sensors selected from a first group of sensor types consisting of a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, and an accelerometer;

one or two second sensors selected from a second group of sensor types consisting of a mood sensor, a behavioral sensor, and an environmental sensor;

one third sensor also selected from the second group of sensor types, the third sensor being different in sensor type from each of the two second sensors;

the data streams comprise glucocorticoid data of a person from the glucocorticoid meter 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, accelerometer data of the person from the accelerometer, self-reported mood data of the person from the mood sensor, behavioral data of the person from the behavioral sensor, or environmental data from the environmental sensor;

a first data set from the data streams was collected from the person at a first time, the person having been exposed to a stressor at the first time, as indicated by data in the first data set from the glucocorticoid meter and the third sensor; and

a second data set from the data streams was collected from the person at a second time, the person not having been exposed to the stressor at the second time, as indicated by data in the second data set from the glucocorticoid meter and the third sensor;

means for accessing a stress model comprising baseline renal-Doppler data and baseline glucocorticoid data of the person, and two or more of baseline heart-rate data of the person, baseline blood-pressure data of the person, baseline pulse-oximetry data of the person, baseline accelerometer 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, and wherein the stress model correlates the baseline renal-Doppler data and the baseline glucocorticoid data of the person with the baseline heart-rate data of the person, the baseline blood-pressure data of the person, the baseline pulse-oximetry data of the person, the baseline accelerometer data of the person, or the baseline self-reported mood data of the person;

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

means for determining a current stress factor for the stressor with respect to the person based on the analysis of the first data set and second data set with respect to each other and with respect to the stress model; and

means for transmitting a warning to a designated computing system of the person referencing the current stress factor if the current stress factor deviates from a set of control parameter.

47. The system of claim 46, wherein the environmental sensor is selected from a fourth group consisting of a barometer, a weather sensor, a pollen counter, a location sensor, a seismometer, an altimeter, a hydrometer, a decibel meter, a light meter, a thermometer, a wind sensor, and a traffic sensor.

48. The system of claim 46, wherein the environmental sensor is a data feed.

49. The system of claim 46, the means for determining the current stress factor for the stressor with respect to the person based on the analysis of the first data set and second data set with respect to each other comprises:

means for determining a first stress index of the person based on the first data set;

means for determining a second stress index of the person based on the second data set; and

means for determining a current stress factor for the stressor with respect to the person based on a comparison of the first stress index and second stress index of the person with respect to each other.

50. The system of claim 49, wherein the data from the second sensors is used to validate the stress index of the person.

51. The system of claim 46, wherein the data from the second sensors is used to validate the current stress factor of the person.

52. The system of claim 46, further comprising:

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

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

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

53. The system of claim 46, wherein one or more of the sensors from the first group is affixed to the person'"'"'s body.

54. The system of claim 46, 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, the accelerometer data of the person, or the self-reported mood data of the person.

55. The system of claim 46, wherein:

at the first time the person was stressed; and

at the second time the person was unstressed.

56. The system of claim 46, wherein:

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

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

57. The system of claim 46, wherein:

the first group further consists of an electrocardiograph; and

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

58. The system of claim 46, wherein:

the first group further consists of an electromyograph; and

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

59. The system of claim 46, wherein:

the first group further consists of a respiration sensor; and

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

60. The system of claim 46, wherein:

the first group further consists of a galvanic-skin-response sensor; and

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