Calculating and monitoring the efficacy of stress-related therapies

• US 8,622,900 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, Braun, Albert C., Bahadur, Madan
• Primary Examiner(s): Thomson, Bill
• Assistant Examiner(s): Sands, Davin K
• Application Number:
  US13/107,605
• Publication Number:
  US 20120289791A1
• Time in Patent Office: 970 Days
• Field of Search: None
• US Class Current: 600/300
• 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 at least two of an accelerometer, 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 engage and not engaged in a therapy, and determining a current stress factor for the therapy on 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 two or more of an accelerometer, a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, or a mood sensor, wherein;

the data streams comprise two or more of accelerometer data of a person from the accelerometer, 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 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 having engaged in a therapy at the first time; and

a second data set from the data streams was collected from the person at a second time, the person not having engaged in the therapy at the second time;

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

determining, by one or more of the processors, a current stress factor for the therapy on 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, the current stress factor measuring a change in the person'"'"'s stress index associated with the therapy.

2. The method of claim 1, wherein the therapy is selected from a group consisting of an intervention, biofeedback, a breathing exercise, a progressive muscle relaxation exercise, a presentation of personal media, an exit strategy, a psychotherapeutic technique, and cognitive reframing therapy.

3. The method of claim 1, further comprising:

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

analyzing the current stress factor and prior stress factor of the therapy with respect to each other; and

determining whether there is a change in the stress factor for the therapy based on the analysis of the current stress factor and prior stress factor 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, wherein the stress model is a stress model of the person and comprises baseline data of the person.

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

7. 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.

8. 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.

9. 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.

10. 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.

11. 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.

12. 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.

13. 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.

14. 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.

15. 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.

16. 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.

17. An apparatus comprising:

one or more processors; and

a memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to;

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

the data streams comprise two or more of accelerometer data of a person from the accelerometer, 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 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 having engaged in a therapy at the first time; and

a second data set from the data streams was collected from the person at a second time, the person not having engaged in the therapy at the second time;

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

determine a current stress factor for the therapy on 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, the current stress factor measuring a change in the person'"'"'s stress index associated with the therapy.

18. The apparatus of claim 17, wherein the therapy is selected from a group consisting of an intervention, biofeedback, a breathing exercise, a progressive muscle relaxation exercise, a presentation of personal media, an exit strategy, a psychotherapeutic technique, and cognitive reframing therapy.

19. The apparatus of claim 17, the apparatus further operable when executing instructions to:

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

analyze the current stress factor and prior stress factor of the therapy with respect to each other; and

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

20. The apparatus of claim 17, wherein one or more of the sensors is affixed to the person'"'"'s body.

21. The apparatus of claim 17, wherein the stress model is a stress model of the person and comprises baseline data of the person.

22. The apparatus of claim 17, wherein the stress model comprises an algorithm that comprises a plurality of variables based on two or more of the accelerometer 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 self-reported mood data of the person.

23. The apparatus of claim 17, 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.

24. The apparatus of claim 17, wherein:

at the first time the person was substantially stressed; and

at the second time the person was substantially unstressed.

25. The apparatus of claim 17, 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.

26. The apparatus of claim 17, 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.

27. The apparatus of claim 17, 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.

28. The apparatus of claim 17, wherein:

the plurality of sensors further comprise an electrocardiograph; and

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

29. The apparatus of claim 17, 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.

30. The apparatus of claim 17, wherein:

the plurality of sensors further comprise an electromyograph; and

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

31. The apparatus of claim 17, 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.

32. The apparatus of claim 17, 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.

33. One or more computer-readable non-transitory storage media embodying software that is operable when executed to:

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

the data streams comprise two or more of accelerometer data of a person from the accelerometer, 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 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 having engaged in a therapy at the first time; and

a second data set from the data streams was collected from the person at a second time, the person not having engaged in the therapy at the second time;

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

determine a current stress factor for the therapy on 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, the current stress factor measuring a change in the person'"'"'s stress index associated with the therapy.

34. The media of claim 33, wherein the therapy is selected from a group consisting of an intervention, biofeedback, a breathing exercise, a progressive muscle relaxation exercise, a presentation of personal media, an exit strategy, a psychotherapeutic technique, and cognitive reframing therapy.

35. The media of claim 33, the media embodying instructions further operable when executed to:

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

analyze the current stress factor and prior stress factor of the therapy with respect to each other; and

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

36. The media of claim 33, wherein one or more of the sensors is affixed to the person'"'"'s body.

37. The media of claim 33, wherein the stress model is a stress model of the person and comprises baseline data of the person.

38. The media of claim 33, wherein the stress model comprises an algorithm that comprises a plurality of variables based on two or more of the accelerometer 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 self-reported mood data of the person.

39. The media of claim 33, 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.

40. The media of claim 33, wherein:

at the first time the person was substantially stressed; and

at the second time the person was substantially unstressed.

41. The media of claim 33, 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 33, 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.

43. The media of claim 33, wherein:

the plurality of sensors further comprise a behavioral sensor; and

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

44. The media of claim 33, wherein:

the plurality of sensors further comprise an electrocardiograph; and

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

45. The media of claim 33, wherein:

the plurality of sensors further comprise a glucocorticoid meter; and

the data streams further comprise glucocorticoid data of the person from the glucocorticoid meter.

46. The media of claim 33, wherein:

the plurality of sensors further comprise an electromyograph; and

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

47. The media of claim 33, wherein:

the plurality of sensors further comprise a respiration sensor; and

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

48. The media of claim 33, 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.