Continuous monitoring of stress using accelerometer data

• US 8,622,901 B2
• Filed: 05/13/2011
• Issued: 01/07/2014
• 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, Balakrishnan, Rajalakshmi, Gilman, Alexander, Stergiou, Stergios
• Primary Examiner(s): Thomson, Bill
• Assistant Examiner(s): Sands, Davin K
• Application Number:
  US13/107,644
• Publication Number:
  US 20120289793A1
• Time in Patent Office: 970 Days
• Field of Search: None
• US Class Current: 600/300
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/0024 for multiple sensor units attached to the patient, e.g. using a body or personal area network
• CPC Class Codes: A61B 5/165 Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/7232 involving compression of the physiological signal, e.g. to extend the signal recording period
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

In particular embodiments, a method includes accessing data streams from an accelerometer and one or more of a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, or a mood sensor monitoring a person, analyzing data sets collected from the person when the person is engaged in various activities, and determining a current stress index of 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, the sensors comprising an accelerometer and one or more of a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, or a mood sensor, wherein;

the data streams comprise accelerometer data of a person from the accelerometer 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, and self-reported mood data of the person from the mood sensor;

a first data set from the data streams was collected from the person at a first time, the person being engaged in a first activity 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 engaged in a second activity at the second time;

accessing, by one or more of the processors, a stress model comprising baseline renal-Doppler data, baseline accelerometer data, and one 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 wherein the stress model correlates the baseline renal-Doppler data with the baseline accelerometer data and one 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 first data set and second data set with respect to each other and with respect to the stress model; and

determining, by one or more of the processors, 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 and with respect to the stress model.

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 self-reported mood data of the person with the accelerometer data of the person.

3. The method of claim 1, wherein the first activity is substantially physically resting.

4. The method of claim 1, wherein the second activity is a physical activity that is more than substantially physically resting.

5. 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.

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

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

8. The method of claim 1, wherein the stress model comprises an algorithm that comprises a plurality of variables based on the accelerometer 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 self-reported mood data of the person.

9. 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.

10. 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.

11. 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.

12. The method of claim 1, wherein:

the plurality of sensors further comprise a kinesthetic sensor; and

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

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

the data streams comprise accelerometer data of a person from the accelerometer 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, and self-reported mood data of the person from the mood sensor;

a first data set from the data streams was collected from the person at a first time, the person being engaged in a first activity 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 engaged in a second activity at the second time;

access a stress model comprising baseline renal-Doppler data, baseline accelerometer data, and one 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 wherein the stress model correlates the baseline renal-Doppler data with the baseline accelerometer data and one 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 first data set and second data set with respect to each other and with respect to the stress model; 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 and with respect to the stress model.

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 self-reported mood data of the person with the accelerometer data of the person.

21. The apparatus of claim 19, wherein the first activity is substantially physically resting.

22. The apparatus of claim 19, wherein the second activity is a physical activity that is more than substantially physically resting.

23. 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.

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

25. The apparatus of claim 19, wherein the stress model is a stress model of the person and comprises baseline data of the person.

26. The apparatus of claim 19, wherein the stress model comprises an algorithm that comprises a plurality of variables based on the accelerometer 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 self-reported mood data of the person.

27. The apparatus of claim 19, 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.

28. The apparatus of claim 19, 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.

29. The apparatus of claim 19, wherein:

at the first time the person was substantially stressed; and

at the second time the person was substantially unstressed.

30. The apparatus of claim 19, wherein:

the plurality of sensors further comprise a kinesthetic sensor; and

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

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

the data streams comprise accelerometer data of a person from the accelerometer 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, and self-reported mood data of the person from the mood sensor;

a first data set from the data streams was collected from the person at a first time, the person being engaged in a first activity 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 engaged in a second activity at the second time;

access a stress model comprising baseline renal-Doppler data, baseline accelerometer data, and one 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 wherein the stress model correlates the baseline renal-Doppler data with the baseline accelerometer data and one 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 first data set and second data set with respect to each other and with respect to the stress model; 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 and with respect to the stress model.

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 self-reported mood data of the person with the accelerometer data of the person.

39. The media of claim 37, wherein the first activity is substantially physically resting.

40. The media of claim 37, wherein the second activity is a physical activity that is more than substantially physically resting.

41. 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.

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

43. The media of claim 37, wherein the stress model is a stress model of the person and comprises baseline data of the person.

44. The media of claim 37, wherein the stress model comprises an algorithm that comprises a plurality of variables based on the accelerometer 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 self-reported mood data of the person.

45. The media of claim 37, 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.

46. The media of claim 37, 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.

47. The media of claim 37, wherein:

at the first time the person was substantially stressed; and

at the second time the person was substantially unstressed.

48. The media of claim 37, wherein:

the plurality of sensors further comprise a kinesthetic sensor; and

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

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.