Determining digital markers indicative of a neurological condition

1. A computer-implemented method for efficiently determining a digital marker indicative of a neurological condition, comprising:

detecting, using one or more processors, a region of interest in an image frame of a face of a user, wherein the region of interest includes an eye of the user and the image frame is obtained from a video stream;

generating a point spread function (PSF) based on acquired data and a portion of the image frame specified by the region of interest, and oculometric parameters;

performing deconvolution on the portion of the image frame specified by the region of interest using the PSF;

determining a set of oculometric parameters of the eye of the user using the deconvolved image; and

determining one or more digital markers of the user based on the set of oculometric parameters, wherein the one or more digital markers are indicative of a neurological condition of the user.

2. The computer-implemented method of claim 1 , wherein the oculometric parameters include a pupil center, iris data, and eyelid data.

3. The computer-implemented method of claim 1 , wherein the acquired data comprises stimulus data and device data;

wherein the stimulus data represents spatio-temporal information on a stimulus presented on a display of a device; and

wherein the device data includes orientation information of the device.

4. The computer-implemented method of claim 1 , further comprising:

prior to the performing deconvolution on the portion of the image frame, determining a pre-set of oculometric parameters based on the portion of the image frame; and

wherein the generating the PSF comprises:

generating the PSF based on the pre-set of oculometric parameters.

5. The computer-implemented method of claim 1 , wherein the determining the set of oculometric parameters comprises:

detecting a plurality of boundaries in the deconvolved image based on a change in intensity between data points in the deconvolved image; and

identifying, from a plurality of quadratic curves, a quadratic curve that fits an upper eyelid and a lower eyelid based on the plurality of boundaries.

6. The computer-implemented method of claim 5 , wherein the identifying comprises:

identifying the quadratic curve based on a maximal likelihood estimation.

7. The computer-implemented method of claim 1 , further comprising:

iteratively repeating the steps of detecting, generating, performing, and determining the set of oculometric parameters to improve an accuracy of the set of oculometric parameters.

8. The computer-implemented method of claim 1 , wherein determining the one or more digital markers comprises:

determining a pupillary response function based on the set of oculometric parameters, wherein the pupillary response function is determined by performing iterative blind deconvolution to estimate two or more parameters that define the pupillary response function.

9. The computer-implemented method of claim 1 , wherein the oculometric parameters comprise eyelid data or a pupil center.

10. A system for efficiently determining a digital marker indicative of a neurological condition, comprising:

one or more memories; and

at least one processor each coupled to at least one of the memories and configured to perform operations comprising:

detecting a region of interest in an image frame of a face of a user, wherein the region of interest includes an eye of the user and the image frame is obtained from a video stream;

generating a point spread function (PSF) based on acquired data, a portion of the image frame specified by the region of interest, and oculometric parameters;

performing deconvolution on the portion of the image frame specified by the region of interest using the PSF;

determining a set of oculometric parameters of the eye of the user using the deconvolved image; and

determining one or more digital markers of the user based on at least the set of oculometric parameters, wherein the one or more digital markers are indicative of a neurological condition of the user.

11. The system of claim 10 , wherein the oculometric parameters include a pupil center, iris data, and eyelid data.

12. The system of claim 10 , wherein the acquired data comprises stimulus data and device data;

wherein the stimulus data represents spatio-temporal information on a stimulus presented on a display of a device; and

wherein the device data includes orientation information of the device.

13. The system of claim 10 , the operations further comprising:

prior to the performing deconvolution on the portion of the image frame, determining a pre-set of oculometric parameters based on the portion of the image frame; and

wherein the generating the PSF comprises:

generating the PSF based on the pre-set of oculometric parameters.

14. The system of claim 10 , wherein the determining the set of oculometric parameters further comprises:

detecting a plurality of boundaries in the deconvolved image based on a change in intensity between data points in the deconvolved image; and

identifying, from a plurality of quadratic curves, a quadratic curve that fits an upper eyelid and a lower eyelid based on the plurality of boundaries.

15. The system of claim 14 , wherein the identifying comprises:

identifying the quadratic curve based on a maximal likelihood estimation.

16. The system of claim 10 , the operations further comprising:

iteratively repeating the steps of detecting, generating, performing, and determining the set of oculometric parameters to improve an accuracy of the set of oculometric parameters.

17. A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising:

detecting a region of interest in an image frame of a face of a user, wherein the region of interest includes an eye of the user and the image frame is obtained from a video stream;

generating a point spread function (PSF) based on acquired data, a portion of the image frame specified by the region of interest, and oculometric parameters;

performing deconvolution on the portion of the image frame specified by the region of interest using the PSF;

determining a set of oculometric parameters of the eye of the user using the deconvolved image; and

determining one or more digital markers of the user based on at least the set of oculometric parameters, wherein the one or more digital markers are indicative of a neurological condition of the user.

18. The non-transitory computer-readable medium of claim 17 , wherein the oculometric parameters include a pupil center, iris data, and eyelid data.

19. The non-transitory computer-readable medium of claim 17 , wherein the acquired data comprises stimulus data and device data;

wherein the stimulus data represents spatio-temporal information on a stimulus presented on a display of a device; and

wherein the device data includes orientation information of the device.

20. The non-transitory computer-readable medium of claim 17 , the operations further comprising:

prior to the performing deconvolution on the portion of the image frame, determining a pre-set of oculometric parameters based on the portion of the image frame; and

wherein the generating the PSF comprises:

generating the PSF based on the pre-set of oculometric parameters.

21. The non-transitory computer-readable medium of claim 17 , wherein the determining the set of oculometric parameters comprises:

detecting a plurality of boundaries in the deconvolved image based on a change in intensity between data points in the deconvolved image; and

identifying, from a plurality of quadratic curves, a quadratic curve that fits an upper eyelid and a lower eyelid based on the plurality of boundaries.

22. The non-transitory computer-readable medium of claim 17 , the operations further comprising:

iteratively repeating the steps of detecting, generating, performing, and determining the set of oculometric parameters to improve an accuracy of the set of oculometric parameters.