Claims:1. A computer-implemented method for training a model to estimate guess work using physiological sensing, the method comprising:
systematically presenting a plurality of stimuli to a candidate for performance of a sequence of tasks of varying difficulty level, the sequence of tasks comprising providing response to the plurality of stimuli, via one or more hardware processors, wherein the sequence of tasks comprises a rest time between two consecutive tasks of the sequence of tasks and wherein one of the two consecutive tasks is a relatively high load task and other task is a relatively low load task;
receiving the response to the plurality of stimuli from the candidate, the responses comprising at least one of guess responses and non-guess responses, via the one or more hardware processors;
determining a difference of the guess responses for the sequence of tasks of the varying difficulty level, via the one or more hardware processors;
receiving, via the one or more hardware processors, Photoplethysmogram (PPG) signal from the candidate performing the sequence of tasks, wherein the one or more hardware processors receives the PPG signal from an SPO2 device;
computing, via the one or more hardware processors, a difference in Heart Rate Variability (HRV) data of the candidate for the sequence of tasks of the varying difficulty level, the HRV features obtained from the PPG signal; and
associating the difference in the HRV data with the difference of the guess responses to obtain an indication of relative guess work associated with performance of the sequence of tasks of the varying difficulty level, as reflected in the HRV data.

2. The method as claimed in clam 1, wherein the varying difficulty level of the sequence of tasks is defined using analysis of variance (ANOVA) analysis of the sequence of tasks.

3. The method as claimed in claim 1, wherein systematically presenting the plurality of stimuli comprises presenting the sequence of tasks of the varying difficulty level performed after the rest time between the two consecutive tasks.

4. The method as claimed in claim 1, wherein the HRV features comprises Standard deviation of normal-to-normal interval (SDNN).

5. The method as claimed in claim 1, further comprising:
acquiring Galvanic Skin response (GSR) data from the candidate performing the sequence of tasks, wherein the one or more hardware processors receives the GSR data from one or more GSR sensors worn by the candidate;
extracting a plurality of GSR features for the candidate from the GSR data; and
determining an effort index (EI) based on the plurality of GSR features.

6. The method as claimed in claim 5, wherein the GSR features comprises tonic and phasic power indicative of specific physiological aspects of brain states, peak detection, and a fluctuation analysis of GSR data indicative of fluctuation of signal in GSR data.

7. The method as claimed in claim 6, further comprising pre-processing the acquired GSR data prior to extracting the plurality of GSR features to remove an artifact from the acquired GSR data.

8. The method as claimed in claim 7, further comprising validating the indication of the relative guess work associated with the sequence of tasks of the varying difficulty level based on the EI.

9. A system for training a model to estimate guess work using physiological sensing, the system comprising:
one or more memories; and
one or more hardware processors, the one or more memories coupled to the one or more hardware processors, wherein the one or more hardware processors are capable of executing programmed instructions stored in the one or more memories to:
systematically present a plurality of stimuli to a candidate for performance of a sequence of tasks of varying difficulty level, the sequence of tasks comprising providing response to the plurality of stimuli, wherein the sequence of tasks comprises a rest time between two consecutive tasks of the sequence of tasks and wherein one of the two consecutive tasks is a relatively high load task and other task is a relatively low load task;
receive the response to the plurality of stimuli from the candidate, the responses comprising at least one of guess responses and non-guess responses;
determine a difference of the guess responses for the sequence of tasks of the varying difficulty level;
receive Photoplethysmogram (PPG) signal from the candidate performing the sequence of tasks, wherein the one or more hardware processors receives the PPG signal from an SPO2 device;
compute a difference in Heart Rate Variability (HRV) data of the candidate for the sequence of tasks of the varying difficulty level, the HRV features obtained from the PPG signal; and
associate the difference in the HRV data with the difference of the guess responses to obtain an indication of relative guess work associated with performance of the sequence of tasks of the varying difficulty level, as reflected in the HRV data.

10. The system as claimed in clam 1, wherein the one or more hardware processors are capable of executing programmed instructions to define the varying difficulty level of the sequence of tasks using analysis of variance (ANOVA) analysis of the sequence of tasks.

11. The system as claimed in claim 1, wherein to systematically present the plurality of stimuli, the one or more hardware processors are capable of executing programmed instructions to present the sequence of tasks of the varying difficulty level performed after the rest time between the two consecutive tasks.

12. The system as claimed in claim 1, wherein the HRV features comprises Standard deviation of normal-to-normal interval (SDNN).

13. The system as claimed in claim 1, wherein the one or more hardware processors are capable of executing programmed instructions to:
acquire Galvanic Skin response (GSR) data from the candidate performing the sequence of tasks, wherein the one or more hardware processors receives the GSR data from one or more GSR sensors worn by the candidate;
extract a plurality of GSR features for the candidate from the GSR data; and
determine an effort index (EI) based on the plurality of GSR features.

14. The system as claimed in claim 5, wherein the GSR features comprises tonic and phasic power indicative of specific physiological aspects of brain states, peak detection, and a fluctuation analysis of GSR data indicative of fluctuation of signal in GSR data.

15. The system as claimed in claim 6, wherein the one or more hardware processors are capable of executing programmed instructions to pre-process the acquired GSR data prior to extracting the plurality of GSR features to remove an artifact from the acquired GSR data.

16. The system as claimed in claim 7, wherein the one or more hardware processors are capable of executing programmed instructions to validate the indication of the relative guess work associated with the sequence of tasks of the varying difficulty level based on the EI.
, Description:As Attached