Continuous Monitoring of Stress Using Environmental Data

• US 20120289789A1
• 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, Gilman, Alexander, Reinhardt, Albert H. M.
• Granted Patent: US 8,617,067 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 an environmental sensor, and one or more of a mood sensor, 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 exposed to various environmental states, 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 an environmental sensor and one or more of a mood sensor, a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, or an accelerometer, wherein;

the data streams comprise environmental data from the environmental sensor and one or more of mood data of a person from the mood sensor, 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, and accelerometer data of the person from the accelerometer;

a first data set from the data streams was collected at a first time, the environmental data in the first data set indicating a first state at the first time; and

a second data set from the data streams was collected at a second time, the environmental data in the second data set indicating a second state at the second time;

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

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 mood data, heart-rate data, the blood-pressure data, the pulse-oximetry data, or accelerometer data of the person with the environmental data.

3. The method of claim 1, wherein the environmental sensor is selected from a 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.

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

5. The method of claim 4, wherein the data feed is selected from a group consisting of a stock-market ticker, a weather report, a news feed, a traffic-condition update, a public-health notice, an electronic calendar, and a social network news feed.

6. 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.

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

8. 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.

9. The method of claim 8, wherein the stress model of the person comprises baseline renal-Doppler data of the person, baseline environmental data, and one or more of baseline mood data of the person, 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.

10. The method of claim 8, wherein the stress model comprises an algorithm that comprises a plurality of variables based on the environmental data and one or more of the mood data of the person, 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.

11. The method of claim 8, 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.

12. The method of claim 1, wherein:

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

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

13. The method of claim 1, wherein:

at the first time the person was substantially stressed; and

at the second time the person was substantially unstressed.

14. 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.

15. The method of claim 1, wherein:

the plurality of sensors further comprise of 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 an electromyograph; and

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

19. 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.

20. 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.

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

the data streams comprise environmental data from the environmental sensor and one or more of mood data of a person from the mood sensor, 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, and accelerometer data of the person from the accelerometer;

a first data set from the data streams was collected at a first time, the environmental data in the first data set indicating a first state at the first time; and

a second data set from the data streams was collected at a second time, the environmental data in the second data set indicating a second state at the second time;

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

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.

22. The apparatus of claim 21, wherein to analyze the first data set and second data set with respect to each other comprises to contextualize one or more of the mood data, heart-rate data, the blood-pressure data, the pulse-oximetry data, or accelerometer data of the person with the environmental data.

23. The apparatus of claim 21, wherein the environmental sensor is selected from a 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.

24. The apparatus of claim 21, wherein the environmental sensor is a data feed.

25. The apparatus of claim 24, wherein the data feed is selected from a group consisting of a stock-market ticker, a weather report, a news feed, a traffic-condition update, a public-health notice, an electronic calendar, and a social network news feed.

26. The apparatus of claim 21, 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.

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

28. The apparatus of claim 21, 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.

29. The apparatus of claim 28, wherein the stress model of the person comprises baseline renal-Doppler data of the person, baseline environmental data, and one or more of baseline mood data of the person, 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.

30. The apparatus of claim 28, wherein the stress model comprises an algorithm that comprises a plurality of variables based on the environmental data and one or more of the mood data of the person, 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.

31. The apparatus of claim 28, 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.

32. The apparatus of claim 21, wherein:

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

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

33. The apparatus of claim 21, wherein:

at the first time the person was substantially stressed; and

at the second time the person was substantially unstressed.

34. The apparatus of claim 21, 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.

35. The apparatus of claim 21, wherein:

the plurality of sensors further comprise of a behavioral sensor; and

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

36. The apparatus of claim 21, wherein:

the plurality of sensors further comprise an electrocardiograph; and

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

37. The apparatus of claim 21, 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.

38. The apparatus of claim 21, wherein:

the plurality of sensors further comprise an electromyograph; and

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

39. The apparatus of claim 21, 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.

40. The apparatus of claim 21, 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.

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

the data streams comprise environmental data from the environmental sensor and one or more of mood data of a person from the mood sensor, 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, and accelerometer data of the person from the accelerometer;

a first data set from the data streams was collected at a first time, the environmental data in the first data set indicating a first state at the first time; and

a second data set from the data streams was collected at a second time, the environmental data in the second data set indicating a second state at the second time;

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

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.

42. The media of claim 41, wherein to analyze the first data set and second data set with respect to each other comprises to contextualize one or more of the mood data, heart-rate data, the blood-pressure data, the pulse-oximetry data, or accelerometer data of the person with the environmental data.

43. The media of claim 41, wherein the environmental sensor is selected from a 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.

44. The media of claim 41, wherein the environmental sensor is a data feed.

45. The media of claim 44, wherein the data feed is selected from a group consisting of a stock-market ticker, a weather report, a news feed, a traffic-condition update, a public-health notice, an electronic calendar, and a social network news feed.

46. The media of claim 41, 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.

47. The media of claim 41, wherein one or more of the sensors is affixed to the person'"'"'s body.

48. The media of claim 41, 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.

49. The media of claim 48, wherein the stress model of the person comprises baseline renal-Doppler data of the person, baseline environmental data, and one or more of baseline mood data of the person, 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.

50. The media of claim 48, wherein the stress model comprises an algorithm that comprises a plurality of variables based on the environmental data and one or more of the mood data of the person, 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.

51. The media of claim 48, 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.

52. The media of claim 41, wherein:

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

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

53. The media of claim 41, wherein:

at the first time the person was substantially stressed; and

at the second time the person was substantially unstressed.

54. The media of claim 41, 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.

55. The media of claim 41, wherein:

the plurality of sensors further comprise of a behavioral sensor; and

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

56. The media of claim 41, wherein:

the plurality of sensors further comprise an electrocardiograph; and

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

57. The media of claim 41, 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.

58. The media of claim 41, wherein:

the plurality of sensors further comprise an electromyograph; and

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

59. The media of claim 41, 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.

60. The media of claim 41, 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.

61. A system comprising:

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

the data streams comprise environmental data from the environmental sensor and one or more of mood data of a person from the mood sensor, 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, and accelerometer data of the person from the accelerometer;

a first data set from the data streams was collected at a first time, the environmental data in the first data set indicating a first state at the first time; and

a second data set from the data streams was collected at a second time, the environmental data in the second data set indicating a second state at the second time;

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

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.

62. The system of claim 61, 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 mood data, heart-rate data, the blood-pressure data, the pulse-oximetry data, or accelerometer data of the person with the environmental data.

63. The system of claim 61, wherein the environmental sensor is selected from a 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.

64. The system of claim 61, wherein the environmental sensor is a data feed.

65. The system of claim 64, wherein the data feed is selected from a group consisting of a stock-market ticker, a weather report, a news feed, a traffic-condition update, a public-health notice, an electronic calendar, and a social network news feed.

66. The system of claim 61, 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.

67. The system of claim 61, wherein one or more of the sensors is affixed to the person'"'"'s body.

68. The system of claim 61, 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.

69. The system of claim 68, wherein the stress model of the person comprises baseline renal-Doppler data of the person, baseline environmental data, and one or more of baseline mood data of the person, 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.

70. The system of claim 68, wherein the stress model comprises an algorithm that comprises a plurality of variables based on the environmental data and one or more of the mood data of the person, 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.

71. The system of claim 68, 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.

72. The system of claim 61, wherein:

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

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

73. The system of claim 61, wherein:

at the first time the person was substantially stressed; and

at the second time the person was substantially unstressed.

74. The system of claim 61, 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.

75. The system of claim 61, wherein:

the plurality of sensors further comprise of a behavioral sensor; and

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

76. The system of claim 61, wherein:

the plurality of sensors further comprise an electrocardiograph; and

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

77. The system of claim 61, 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.

78. The system of claim 61, wherein:

the plurality of sensors further comprise an electromyograph; and

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

79. The system of claim 61, 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.

80. The system of claim 61, 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.