REAL-TIME STRESS DETERMINATION OF AN INDIVIDUAL

• US 20140046144A1
• Filed: 08/13/2013
• Published: 02/13/2014
• Est. Priority Date: 08/13/2012
• Status: Active Grant

• Current Assignee: TATA Consultancy Services Limited (Tata Sons Pvt Ltd.)
• Original Assignee: TATA Consultancy Services Limited (Tata Sons Pvt Ltd.)
• Inventors: Purushothaman, Balamuralidhar, Jayaraman, Srinivasan, Kumar, Kriti
• Granted Patent: US 11,006,874 B2
• Time in Patent Office: Days
• US Class Current: 600/301
• CPC Class Codes: A61B 2503/24 Computer workstation operators
• CPC Class Codes: A61B 2576/00 Medical imaging apparatus involving image processing or analysis A61B1/00009, A61B6/52 and A61B8/52 take precedence; image processing per se G06T; details of algorithms for analysing medical images G06T7/0012
• CPC Class Codes: A61B 5/0006 ECG or EEG signals
• CPC Class Codes: A61B 5/0077 Devices for viewing the surface of the body, e.g. camera, magnifying lens
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/0245 by using sensing means generating electric signals, i.e. ECG signals
• CPC Class Codes: A61B 5/1116 Determining posture transitions
• CPC Class Codes: A61B 5/165 Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/352 Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
• CPC Class Codes: A61B 5/486 Bio-feedback A61B5/375 takes precedence
• CPC Class Codes: A61B 5/7253 characterised by using transforms
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: G06F 2218/10 by analysing the shape of a waveform, e.g. extracting parameters relating to peaks
• CPC Class Codes: G06V 40/15 Biometric patterns based on physiological signals, e.g. heartbeat, blood flow
• CPC Class Codes: G16H 30/40 for processing medical images, e.g. editing
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

The present subject matter relates to a computer implemented method for real time determination of stress levels of an individual. The method includes receiving at least one stream of physiological data from at least one primary sensor for a predetermined duration, and preprocessing the at least one stream of physiological data to extract physiological parameters, where the preprocessing includes performing a preliminary analysis on the at least one stream of physiological data. The method further includes determining a stress level of the individual based on at least the physiological parameters, wherein the determining comprises performing a statistical analysis on the physiological parameters.

Claims

1. A computer implemented method for real time determination of stress levels of an individual, the method comprising:

receiving at least one stream of physiological data from at least one primary sensor for a predefined duration;

preprocessing the at least one stream of physiological data to extract physiological parameters, wherein the preprocessing comprises performing a preliminary analysis on the at least one stream of physiological data; and

determining a stress level of the individual based on at least the physiological parameters, wherein the determining comprises performing a statistical analysis on the physiological parameters.

2. The method as claimed in claim 1, further comprising determining the stress level based on the physiological parameters and supplementary physiological data, wherein the at least one stream of physiological data is received for less than a threshold time limit.

3. The method as claimed in claim 1, wherein the method further comprises deploying the at least one primary sensor in at least one of a workplace environment and a client device.

4. The method as claimed in claim 1, wherein the at least one stream of physiological data comprises an Electrocardiogram (ECG) signal.

5. The method as claimed in claim 1, wherein the preliminary analysis comprises:

filtering the at least one stream of physiological data to reduce noise;

detecting R-peaks in the at least one stream of physiological data;

calculating duration between subsequent R-peaks; and

extracting the physiological parameters from the duration between the R-peaks.

6. The method as claimed in claim 5, wherein the extracting further comprises performing one of a frequency domain analysis and a time domain analysis on the at least one stream of physiological data.

7. The method as claimed in claim 1, wherein the statistical analysis is based on at least one of a classifier technique, and a matching technique.

8. The method as claimed in claim 1, wherein the predefined duration is approximately one minute.

9. The method as claimed in claim 2, wherein the threshold time limit is one minute.

10. The method as claimed in claim 2, wherein the supplementary physiological data includes data from accelerometer sensors, barometer sensors, and camera sensors of one or more client devices.

11. The method as claimed in claim 1, wherein the determining further comprises comparing the analyzed physiological parameters with existing records in a database, based on an individual reference identification number.

12. The method as claimed in claim 1, wherein the determining the further comprises comparing the analyzed physiological parameters with existing records in a database, based on a recorded ECG signal.

13. A stress determination system for determining a stress level of an individual in real time, the stress determination system comprising:

a processor; and

a memory coupled to the processor, the memory comprising;

an ECG sensing module configured to receive at least one stream of physiological data from at least one primary sensor for a duration of approximately one minute;

an analysis module configured to;

preprocess the at least one stream of physiological data to extract physiological parameters; and

determine a stress level of the individual based on at least the physiological parameters.

14. The stress determination system as claimed in claim 13, wherein the at least one primary sensor is one of a dry type sensor and a capacitive type sensor.

15. The stress determination system as claimed in claim 13, wherein the analysis module is further configured to compare the analyzed physiological parameters with existing records in a database, based on a reference identification number, in order to assess a deviation of the analyzed physiological parameters with the existing records.

16. The stress determination system as claimed in claim 13, wherein the analysis module is configured to determine the stress level based on the physiological parameters and supplementary physiological data, when the at least one stream of physiological data is received for less than a threshold time limit.

17. The stress determination system as claimed in claim 13 wherein the supplementary physiological data includes data from accelerometer sensors, barometer sensors, and camera sensors of one or more secondary sensors.

18. The stress determination system as claimed in claim 13, wherein the threshold time limit is one minute.

19. The stress determination system as claimed in claim 13, wherein the analysis module is configured to trigger an application to relax the individual based on the determined stress level, and wherein the application comprises audio-visual media personalized by the individual.

20. A non-transitory computer-readable medium having embodied thereon a computer program for executing a method comprising:

receiving at least one stream of physiological data pertaining to an individual from at least one primary sensor for a duration of approximately one minute;

preprocessing the at least one stream of physiological data to extract physiological parameters, wherein the preprocessing comprises performing a preliminary analysis on the at least one stream of physiological data; and

determining a stress level of the individual based on at least the physiological parameters, wherein the determining comprises performing a statistical analysis on the physiological parameters.