Calculating and monitoring a composite stress index

Patent metadata

Field	Value
Publication number	US-9189599-B2
Application number	US-201113107540-A
Country	US
Kind code	B2
Filing date	May 13, 2011
Priority date	May 13, 2011
Publication date	Nov 17, 2015
Grant date	Nov 17, 2015

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.

First claim

What is claimed is:

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, sel

Assignees

• Adler B Thomas
• Jain Jawahar
• Marvit David Loren
• Balakrishnan Rajalakshmi
• Gilman Alexander
• Stergiou Stergios
• Braun Albert C
• Reinhardt Albert H M
• Fujitsu Ltd

Inventors

• Adler B Thomas
• Jain Jawahar
• Marvit David Loren
• Balakrishnan Rajalakshmi
• Gilman Alexander
• Stergiou Stergios
• Braun Albert C
• Reinhardt Albert H M

Classifications

• G16H50/30 Primary

  for calculating health indices; for individual health risk assessment · CPC title

• G16H50/20

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title

• G06F19/345

  Physics · mapped topic

• G06F19/3431 Primary

  Physics · mapped topic