Eye tracking fixation monitoring systems and methods

1. A system comprising:

an eye tracker configured to capture a first plurality of images of a current eye; and

a control processor configured to:

detect an eye position and orientation in each of the first plurality of images;

determine an eye fixation position and orientation relative to an optical axis of the eye tracker by inputting the eye position and orientation of the eye in the first plurality of images into a neural network trained using a set of historical data to determine when a historical eye was fixating and to output a determination of the current eye fixation position and orientation relative to the optical axis, wherein the neural network is trained by constructing and analyzing one or more histograms of detected historical eye positions and orientations;

estimate eye fixation parameters based at least in part on the determined eye fixation position and orientation; and

track the eye position and orientation by analyzing one or more images from the first plurality of images to determine the eye position and orientation relative to the eye fixation parameters.

2. The system of claim 1 , wherein the eye fixation parameters comprise a reference position and orientation of the eye when fixated.

3. The system of claim 1 , wherein analyzing the histogram further comprises determining a relative maximum value and determining whether coordinates of the relative maximum value comprise a fixation position and orientation.

4. The system of claim 3 , wherein determining whether coordinates of the relative maximum value comprise a fixation position and orientation further comprise comparing the relative maximum value with a threshold and/or an average coordinate value of the histogram.

5. The system of claim 1 , further comprising:

a retina imaging system comprising an optical coherence tomography (OCT) scanner and/or a fundus camera configured to perform a retinal scan.

6. The system of claim 5 , wherein the retina imaging system is configured to capture a second plurality of images of an eye;

detect whether a fovea is present in one or more of the second plurality of images;

identify a first image from the second plurality of images having the detected fovea;

determine a second image from the first plurality of images having a temporal proximity to the first image; and

analyze the second image to determine eye fixation parameters.

7. The system of claim 1 , wherein the control processor is configured to track the eye position and orientation and calculate an offset from the eye fixation parameters and determine if the offset is less than a threshold value;

wherein when the offset is less than the threshold value the eye is determined to be fixated and the control processor generates an indication of fixation; and

wherein when the offset is greater than the threshold value the eye is determined to be out of alignment and the control processor generates an indication of no fixation.

8. The system of claim 1 , wherein the control processor is further configured to perform an eye diagnostic procedure, and track eye position using the eye tracker during the eye diagnostic procedure.

9. The system of claim 1 , further comprising a diagnostic device configured to perform an eye diagnostic procedure while tracking the position and orientation of the eye using an image capture device;

wherein the diagnostic device is further configured to receive data representative of fixation and eye position during the eye diagnostic procedure based, at least in part, on the data representative of fixation and eye position.

10. A method comprising:

capturing a first plurality of images of an eye;

detecting an eye position and orientation in each of the first plurality of images;

determining an eye fixation position and orientation relative to an optical axis of the eye tracker by inputting the eye position and orientation of the eye in the first plurality of images into a neural network trained using a set of historical data to determine when a historical eye was fixating and to output a determination of the current eye fixation position and orientation relative to the optical axis, wherein the neural network is trained by constructing and analyzing one or more histograms of detected historical eye positions and orientations;

estimating eye fixation parameters based at least in part on the determined eye fixation position and orientation; and

tracking the eye position and orientation by analyzing one or more images from the first plurality of images to determine the eye position and orientation relative to the eye fixation parameters.

11. The method of claim 10 , further comprising training a neural network to receive the first plurality of images and output a determination of an eye position.

12. The method of claim 10 , wherein the eye fixation parameters comprise a reference position and orientation of the eye when fixated.

13. The method of claim 10 , wherein analyzing the histogram further comprises determining a relative maximum value.

14. The method of claim 10 , further comprising:

performing a retina imaging scan of the eye using an optical coherence tomography (OCT) scanner.

15. The method of claim 14 , further comprising:

capturing a second plurality of images of an eye from the retina imaging scan;

detecting whether a fovea is present in one or more of the second plurality of images;

identifying a first image from the second plurality of images having the detected fovea;

determining a second image from the first plurality of images having a temporal proximity to the first image; and

analyzing the second image to determine eye fixation parameters.

16. The method of claim 10 , further comprising tracking the eye position and orientation and calculating an offset from the eye fixation parameters and determine if the offset is less than a threshold value;

wherein when the offset is less than the threshold value the eye is determined to be fixated and the control processor generates an indication of fixation; and

wherein when the offset is greater than the threshold value the eye is determined to be out of alignment and the control processor generates an indication of no fixation.

17. The method of claim 10 , further comprising performing an eye diagnostic procedure, and tracking eye position using an eye tracker during the eye diagnostic procedure.

18. The method of claim 10 , further comprising performing an eye diagnostic procedure while tracking the position and orientation of the eye using an image capture device; and

modifying the eye diagnostic procedure based, at least in part, on data representative of eye fixation parameters and a tracked eye position.