Claims:1. A computer implemented method for optimized task allocation, the method comprising:
receiving a first data pertaining to current activity of one or more workers;
receiving a second data pertaining to at least one task to be allocated to the one or more workers;
determining activity-task pairs based on an activity feature vector corresponding to at least one human body part used during the current activity by the one or more workers and a task feature vector corresponding to at least one human body part required for the at least one task to be performed by the one or more workers;
estimating cognitive load on the one or more workers for the determined activity-task pairs;
determining an optimum activity-task pair based on the estimated cognitive load;
and
allocating the at least one task to the one or more workers based on the determined optimum activity-task pair.

2. The computer implemented method of claim 1, wherein estimating cognitive load comprises:
obtaining a GOMS (a set of Goals, a set of Operators, a set of Methods for achieving the goals, and a set of Selections rules for choosing among competing methods for goals)-style goal sheet for each activity and task in the determined activity-task pairs;
simulating the current activity and the allocated at least one task using a QN-MHP (Queuing Network Model Human Processor) model for the one or more workers based on the corresponding obtained GOMS-style goal sheet; and
estimating an initial probability of completion of the at least one task allocated to the one or more workers based on a simulated time taken by the one or more workers.

3. The computer implemented method of claim 2, wherein obtaining a GOMS-style goal sheet comprises one or more of (i) manually generating the GOMS-style goal sheet; (ii) using fNIRS (Functional near-infrared spectroscopy) scanners to detect brain activity; and (iii) using crowdsourcing inputs.

4. The computer implemented method of claim 3, wherein using fNIRS scanners comprises:
generating an ordered list of nodes of QN-MHP model for each activity and task in the determined activity-task pairs based on sequence of execution through the network thereof, the ordered list of nodes being based on the mapping of each of the nodes of the QN-MHP model with operators implemented therein and corresponding areas of human brain based on the detected brain activity by the fNIRS scanners associated with the one or more workers; and
determining operators associated with each of the nodes.

5. The computer implemented method of claim 4, wherein determining operators is based on crowdsourcing inputs.

6. The computer implemented method of claim 4 further comprising determining loops present in the sequence of execution and optimizing the obtained GOMS-style goal sheet.

7. The computer implemented method of claim 3, wherein using crowdsourcing inputs comprises:
allocating the at least one task and activity in the determined activity-task pairs to the one or more workers;
mapping operators associated with each node of the QN-MHP model for each of the at least one task and activity based on votes received from the one or more workers, for the performed at least one task and activity; and
aggregating the votes received to determine operators associated with each node based on majority voting.

8. The computer implemented method of claim 2, wherein determining an optimum activity-task pair comprises:
measuring time taken by the one or more workers to complete the allocated at least one task;
continually updating the estimated initial probability based on the measured time to generate updated probability associated with each of the determined activity-task pairs;
generating a repository of the determined activity-task pairs mapped to the updated probability; and
fetching an optimum activity-task pair from the generated repository.

9. The computer implemented method of claim 8, wherein fetching an optimum activity-task pair comprises one of (i) fetching an activity-task pair having a maximum value for the initial probability or the updated probability for the one or more workers and correspondingly for the others thereof; (ii) fetching an activity-task pair based on a pre-determined optimum value for the initial probability or the updated probability of the one or more workers; and (iii) fetching an activity-task pair based on the product of the initial probability or the updated probability of all the one or more workers.

10. A system for optimized task allocation, the system comprising:
one or more internal data storage devices comprising instructions; and
one or more processors operatively coupled to the one or more internal data storage devices, the one or more processors being configured by the instructions to:
receive a first data pertaining to current activity of one or more workers;
receive a second data pertaining to at least one task to be allocated to the one or more workers;
determine activity-task pairs based on an activity feature vector corresponding to at least one human body part used during the current activity by the one or more workers and a task feature vector corresponding to at least one human body part required for the at least one task to be performed by the one or more workers;
estimate cognitive load on the one or more workers for the determined activity-task pairs;
determine an optimum activity-task pair based on the estimated cognitive load; and
allocate the at least one task to the one or more workers based on the determined optimum activity-task pair.

11. The system of claim 10, wherein the one or more processors are further configured to:
obtain a GOMS (a set of Goals, a set of Operators, a set of Methods for achieving the goals, and a set of Selections rules for choosing among competing methods for goals)-style goal sheet for each activity and task in the determined activity-task pairs;
simulate the current activity and the allocated at least one task using a QN-MHP (Queuing Network Model Human Processor) model for the one or more workers based on the corresponding obtained GOMS-style goal sheet; and
estimate an initial probability of completion of the at least one task allocated to the one or more workers based on a simulated time taken by the one or more workers.

12. The system of claim 11, wherein the one or more processors are further configured to obtain the GOMS-style goal sheet by one or more of (i) manually generating the GOMS-style goal sheet; (ii) using fNIRS (Functional near-infrared spectroscopy) scanners to detect brain activity; and (iii) using crowdsourcing inputs.

13. The system of claim 12, wherein the one or more processors are further configured to use fNIRS scanners by:
generating an ordered list of nodes of QN-MHP model for each activity and task in the determined activity-task pairs based on sequence of execution through the network thereof, the ordered list of nodes being based on the mapping of each of the nodes of the QN-MHP model with operators implemented therein and corresponding areas of human brain based on the detected brain activity by the fNIRS scanners associated with the one or more workers; and
determining operators associated with each of the nodes.

14. The system of claim 13, wherein the one or more processors are further configured to determine operators based on crowdsourcing inputs.

15. The system of claim 13, wherein the one or more processors are further configured to determine loops present in the sequence of execution and optimize the obtained GOMS-style goal sheet.

16. The system of claim 12, wherein the one or more processors are further configured to use crowdsourcing inputs by:
allocating the at least one task and activity in the determined activity-task pairs to the one or more workers;
mapping operators associated with each node of the QN-MHP model for each of the at least one task and activity based on votes received from the one or more workers, for the performed at least one task and activity; and
aggregating the votes received to determine operators associated with each node based on majority voting.

17. The system of claim 11, wherein the one or more processors are further configured to determine an optimum-task pair by:
measuring time taken by the one or more workers to complete the allocated at least one task;
continually updating the estimated initial probability based on the measured time to generate updated probability associated with each of the determined activity-task pairs;
generating a repository of the determined activity-task pairs mapped to the updated probability; and
fetching an optimum activity-task pair from the generated repository.

18. The system of claim 17, wherein the one or more processors are further configured to fetch the optimum activity-task pair by one of (i) fetching an activity-task pair having a maximum value for the initial probability or the updated probability for the one or more workers and correspondingly for the others thereof; (ii) fetching an activity-task pair based on a pre-determined optimum value for the initial probability or the updated probability of the one or more workers; and (iii) fetching an activity-task pair based on a maximum product of the initial probability or the updated probability of all the one or more workers.
, Description:As Attached