Real-time stress determination of an individual

US11006874B2 · US · B2

Patent metadata
FieldValue
Publication numberUS-11006874-B2
Application numberUS-201313965523-A
CountryUS
Kind codeB2
Filing dateAug 13, 2013
Priority dateAug 13, 2012
Publication dateMay 18, 2021
Grant dateMay 18, 2021

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  1. Title

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  2. Abstract

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  3. Assignees and inventors

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  4. Key dates

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  5. First independent claim

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  6. CPC / IPC classifications

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  7. Citations and related patents

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Abstract

Official abstract text for this publication.

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.

First claim

Opening claim text (preview).

We claim: 1. A computer implemented method for real time determination of stress level of an individual, the method comprising: receiving, by an Electrocardiogram (ECG) sensing module, a first stream of physiological data from at least one primary sensor for a predefined duration and a second stream of physiological data after the first stream of physiological data from the at least one primary sensor for the predefined duration, wherein the at least one primary sensor is deployed in at least one of a workplace environment and a client device; preprocessing, by an analysis module, the first stream of physiological data and the second stream of physiological data to extract physiological parameters by performing a preliminary analysis on the first stream of physiological data and the second stream of physiological data; and determining, by the analysis module, a stress level of the individual by performing a statistical analysis on the physiological parameters and supplementary physiological data, wherein the supplementary physiological data is received from at least one secondary sensor at same time of receiving the first stream of physiological data and the second stream of physiological data, wherein the supplementary physiological data is captured from the working environment of the individual without active intervention of the individual and is considered for determining the stress level of the individual only when a duration of receiving the first stream of physiological data and the second stream of physiological data from the at least one primary sensor is less than the predefined duration, wherein the analysis module classifies the physiological parameters as a positive stress value or a negative stress value by a moving probe or window to compare successive readings to a baseline window of the predefined duration of the physiological data, and depending on a difference between the moving window and the baselines window, the analysis module classifies the readings as the positive stress value or the negative stress value, wherein a confidence level is considered for the statistical analysis and a transition in a state of the stress level of the individual is confirmed when a statistical value is less than a threshold, wherein the individual is assigned an identification number and linked to a profile in a database, and further the profile is updated with determined stress level of the individual. 2. The method as claimed in claim 1 , wherein the first stream of physiological data and the second stream of physiological data comprises an ECG signal. 3. The method as claimed in claim 1 , wherein the preliminary analysis comprises: filtering the first stream of physiological data and the second stream of physiological data to reduce noise; detecting R-peaks in the first stream of physiological data and the second stream of physiological data; calculating duration between subsequent R-peaks; and extracting the physiological parameters from the duration between the R-peaks. 4. The method as claimed in claim 3 , wherein the extracting further comprises performing one of a frequency domain analysis and a time domain analysis on the first stream of physiological data and the second stream of physiological data. 5. 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. 6. The method as claimed in claim 1 , wherein the predefined duration is one minute. 7. The method as claimed in claim 1 , wherein the supplementary physiological data includes data from accelerometer sensors, barometer sensors, and camera sensors of one or more client devices. 8. 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 the individual reference identification number. 9. 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 a recorded ECG signal. 10. 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 Electrocardiogram (ECG) sensing module configured to receive a first stream of physiological data from at least one primary sensor for a duration of one minute and a second stream of physiological data after the first stream of physiological data from the at least one primary sensor for one minute, wherein the at least one primary sensor is deployed in at least one of a workplace environment and a client device; an analysis module configured to: preprocess the first stream of physiological data and the second stream of physiological data to extract physiological parameters; and determine a stress level of the individual based on at least the physiological parameters and supplementary physiological data, wherein the supplementary physiological data is received from at least one secondary sensor at same time of receiving the first stream of physiological data and the second stream of physiological data, wherein the supplementary physiological data is captured from the working environment of the individual without active intervention of the individual and is considered for determining the stress level of the individual only when a duration of receiving the first stream of physiological data and the second stream of physiological data from the at least one primary sensor is less than one minute, wherein the analysis module classifies the physiological parameters as a positive stress value or a negative stress value by a moving probe or window to compare successive readings to a baseline window of one minute of the physiological data, and depending on a difference between the moving window and the baselines window, the analysis module classifies the readings as the positive stress value or the negative stress value, wherein a confidence level is considered for the statistical analysis and a transition in a state of the stress level of the individual is confirmed when a statistical value is less than a threshold, wherein the individual is assigned an identification number and linked to a profile in a database, and further the profile is updated with determined stress level of the individual. 11. The stress determination system as claimed in claim 10 , wherein the at least one primary sensor is one of a dry type sensor and a capacitive type sensor. 12. The stress determination system as claimed in claim 10 , 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. 13. The stress determination system as claimed in claim 10 , wherein the supplementary physiological data includes data from accelerometer sensors, barometer sensors, and camera sensors of one or more client devices. 14. The stress determination system as claimed in claim 10 , 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. 15. A non-transitory computer-readable medium having embodied thereon a computer program for executing a method comprising: receiving a first stream of physiological data pertaining to an individual from at least one primary sensor for a duration of one minute and a second stream of physiological data af

Assignees

Inventors

Classifications

  • by analysing the shape of a waveform, e.g. extracting parameters relating to peaks · CPC title

  • Biometric patterns based on physiological signals, e.g. heartbeat, blood flow · CPC title

  • Biofeedback (using electroencephalography [EEG] A61B5/375) · CPC title

  • Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition · CPC title

  • Determining posture transitions · CPC title

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What does patent US11006874B2 cover?
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 includ…
Who is the assignee on this patent?
Tata Consultancy Services Ltd
What technology area does this patent fall under?
Primary CPC classification A61B5/165. Mapped technology areas include Human Necessities.
When was this patent published?
Publication date Tue May 18 2021 00:00:00 GMT+0000 (Coordinated Universal Time) (B2). Legal status and post-grant events are not shown on this page.
What related patents are in patentsdb?
We list 8 related publications on this page (citations in our corpus or others sharing the same primary CPC).