The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jan. 16, 2024

Filed:

Dec. 29, 2020
Applicant:

Tata Consultancy Services Limited, Mumbai, IN;

Inventors:

Debatri Chatterjee, Kolkata, IN;

Dibyanshu Jaiswal, Kolkata, IN;

Arpan Pal, Kolkata, IN;

Ramesh Kumar Ramakrishnan, Bangalore, IN;

Ratna Ghosh, Kolkata, IN;

Madhurima Moulick, Kolkata, IN;

Rajesh Ranjan, Kolkata, IN;

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
A61B 5/16 (2006.01); G16H 10/60 (2018.01); G16H 50/70 (2018.01); G06N 20/00 (2019.01); A61B 5/0533 (2021.01); A61B 5/00 (2006.01); G16H 40/67 (2018.01);
U.S. Cl.
CPC ...
A61B 5/165 (2013.01); A61B 5/0533 (2013.01); A61B 5/7267 (2013.01); A61B 5/7278 (2013.01); G06N 20/00 (2019.01); G16H 10/60 (2018.01); G16H 40/67 (2018.01); G16H 50/70 (2018.01);
Abstract

Direct usage of endosomatic EDA has multiple challenges for practical cognitive load assessment. Embodiments of the method and system disclosed provide a solution to the technical challenges in the art by directly using the bio-potential signals to implement endosomatic approach for assessment of cognitive load. The method utilizes a multichannel wearable endosomatic device capable of acquiring and combining multiple bio-potentials, which are biomarkers of cognitive load experienced by a subject performing a cognitive task. Further, extracts information for classification of the cognitive load, from the acquired bio-signals using a set of statistical and a set of spectral features. Furthermore, utilizes a feature selection approach to identify a set of optimum features to train a Machine Learning (ML) based task classifier to classify the cognitive load experienced by a subject into high load task and low load task.


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