Device for screening convergence insufficiency and related methods

1. A device for screening a person for convergence insufficiency (CI), the device comprising:

a binocular viewer comprising a first eyepiece to receive a first eye of the person, a second eyepiece to receive a second eye of the person, a first image sensor adjacent said first eyepiece, and a second image sensor adjacent said second eyepiece;

a display adjacent said binocular viewer; and

a processor and associated memory cooperating with said display and configured to

record, with said first image sensor, movement of the first eye,

record, with said second image sensor, movement of the second eye,

display on said display a first visual stimulus and a second visual stimulus,

cause, in alternating fashion, convergent movement and divergent movement in the first visual stimulus and the second visual stimulus along a visual stimulus path,

record with the first image sensor, dynamic movement of the first eye along the visual stimulus path,

record with the second image sensor, dynamic movement of the second eye along the visual stimulus path,

determine respective centroid positions of the second eye and the first eye during the convergent and divergent movement of the first visual stimulus and the second visual stimulus based on the recorded dynamic movements of the first eye and the second eye, and

calculate from the respective centroid positions a plurality of interpupillary distances (IPDs) over time, and from the plurality of the IPDs, generate dynamic IPDs over time, and

compare the dynamic IPDs with the visual stimulus path to serve as an indicator for whether the person has CI.

2. The device of claim 1 wherein a duration of each of the convergent movement and the divergent movement is 80-100 seconds.

3. The device of claim 1 further comprising a first infrared (IR) source configured to irradiate the first eye and a second IR source configured to irradiate second eye; and wherein said processor and memory are configured to generate a first plurality of video frames showing movement of the first eye, and generate a second plurality of video frames showing movement of the second eye.

4. The device of claim 3 wherein said processor and memory are configured to:

identify second blink pixels comprising second eye blinks in the second plurality of video frames;

form a third plurality of video frames by removing the second blink pixels from the second plurality of video frames;

identify first blink pixels comprising first eye blinks in the first plurality of video frames; and

form a fourth plurality of video frames by removing the first blink pixels from the first plurality of video frames.

5. The device of claim 3 wherein said processor and memory are configured to generate the first plurality of video frames by performing at least:

filtering each of the first plurality of video frames using a pixel intensity threshold to form a third plurality of video frames, each of the third plurality of video frames comprising a black background in combination with a white background, a first eye image, the third plurality of video frames comprising an N number of video frames;

filtering each of the second plurality of video frames using the pixel intensity threshold to form a fourth plurality of video frames, each of the fourth plurality of video frames comprising a black background in combination with a white background, a second eye image, the fourth plurality of video frames comprising an M number of video frames;

determining, for each of the third plurality of video frames, x and y coordinates for a first eye pupil centroid; and

generating a first plurality of x coordinate datasets;

wherein an ith x coordinate dataset from the first plurality of x coordinate datasets represents a location of an ith first pupil centroid;

wherein i is greater than or equal to 1 and less than or equal to N;

determining, for each of the fourth plurality of video frames, x and y coordinates for a second eye pupil centroid; and

generating a second plurality of x coordinate datasets;

wherein a jth x coordinate dataset from the second plurality of x coordinate datasets represents a location of a jth second pupilar centroid;

wherein j is greater than or equal to 1 and less than or equal to M.

6. The device of claim 5 wherein said processor and memory are configured to:

graphically display a first curve comprising N x coordinate datasets versus time; and

graphically display a second curve comprising M x coordinate datasets versus time.

7. The device of claim 6 wherein said processor and memory are configured to:

set i=1 and j=1;

subtract the ith x coordinate dataset from the jth x coordinate dataset to form a kth x coordinate dataset, each of the kth x coordinate dataset represents a hth dynamic IPD;

when i is less than N, set i=i+1;

when j is less than M, set j=j+1; and

repeat the setting and the subtracting until at least one of i=N and j=M is true.

8. The device of claim 7 wherein said processor and memory are configured to form a third curve comprising each of the hth dynamic IPD versus time.

9. The device of claim 8 wherein said processor and memory are configured to:

identify a first substantially linear portion of the third curve, the first substantially linear portion comprising a positive slope;

identify a second substantially linear portion of the third curve, the second substantially linear portion comprising a negative slope;

generate a graphical plot of the visual stimulus path, the graphical plot comprising a first linear portion comprising a positive slope and a second linear portion comprising a negative slope;

overlap the third curve onto the graphical plot of the visual stimulus path; and

adjust the third curve to fit onto graphical plot of the visual stimulus path.

10. The device of claim 1 wherein said processor and memory are configured to compare the dynamic IPDs with the visual stimulus path by performing at least:

optimizing a graph of the dynamic IPDs with at least one parameter; and

merging the optimized graph of the dynamic IPDs with the visual stimulus path.

11. A method for screening a person for convergence insufficiency (CI), the method comprising:

recording, with a first image sensor, movement of a first eye of the person;

recording, with a second image sensor, movement of a second eye of the person;

displaying on a display a first visual stimulus and a second visual stimulus;

causing, in alternating fashion, convergent movement and divergent movement in the first visual stimulus and the second visual stimulus along a visual stimulus path;

recording with the first image sensor, dynamic movement of the first eye along the visual stimulus path;

recording with the second image sensor, dynamic movement of the second eye along the visual stimulus path;

determining respective centroid positions of the second eye and the first eye during the convergent and divergent movement of the first visual stimulus and the second visual stimulus based on the recorded dynamic movements of the first eye and the second eye; and

using a processor and memory associated with the display, and the first and second image sensors for calculating from the respective centroid positions a plurality of interpupillary distances (IPDs) over time, and from the plurality of the IPDs, generating dynamic IPDs over time; and

comparing the dynamic IPDs with the visual stimulus path to serve as an indicator for whether the person has CI.

12. The method of claim 11 wherein a duration of each of the convergent movement and the divergent movement is 80-100 seconds.

13. The method of claim 11 further comprising:

using a first infrared (IR) source configured to irradiate the first eye and a second IR source configured to irradiate second eye; and

generating a first plurality of video frames showing movement of the first eye, and generating a second plurality of video frames showing movement of the second eye.

14. The method of claim 13 further comprising:

identifying second blink pixels comprising second eye blinks in the second plurality of video frames;

forming a third plurality of video frames by removing the second blink pixels from the second plurality of video frames;

identifying first blink pixels comprising first eye blinks in the first plurality of video frames; and

forming a fourth plurality of video frames by removing the first blink pixels from the first plurality of video frames.

15. The method of claim 13 wherein the generating of the first plurality of video frames comprises:

filtering each of the first plurality of video frames using a pixel intensity threshold to form a third plurality of video frames, each of the third plurality of video frames comprising a black background in combination with a white background, a first eye image, the third plurality of video frames comprising an N number of video frames;

filtering each of the second plurality of video frames using the pixel intensity threshold to form a fourth plurality of video frames, each of the fourth plurality of video frames comprising a black background in combination with a white background, a second eye image, the fourth plurality of video frames comprising an M number of video frames;

determining, for each of the third plurality of video frames, x and y coordinates for a first eye pupil centroid; and

generating a first plurality of x coordinate datasets;

wherein an ith x coordinate dataset from the first plurality of x coordinate datasets represents a location of an ith first pupil centroid;

wherein i is greater than or equal to 1 and less than or equal to N;

determining, for each of the fourth plurality of video frames, x and y coordinates for a second eye pupil centroid; and

generating a second plurality of x coordinate datasets;

wherein a jth x coordinate dataset from the second plurality of x coordinate datasets represents a location of a jth second pupilar centroid;

wherein j is greater than or equal to 1 and less than or equal to M.

16. The method of claim 15 further comprising:

graphically displaying a first curve comprising N x coordinate datasets versus time; and

graphically displaying a second curve comprising M x coordinate datasets versus time.

17. The method of claim 16 further comprising:

setting i=1 and j=1;

subtracting the ith x coordinate dataset from the jth x coordinate dataset to form a kth x coordinate dataset, each of the kth x coordinate dataset represents a hth dynamic IPD;

when i is less than N, setting i=i+1;

when j is less than M, setting j=j+1; and

repeating the setting and the subtracting until at least one of i=N and j=M is true.

18. The method of claim 17 further comprising forming a third curve comprising each of the hth dynamic IPD versus time.

19. The method of claim 18 further comprising:

identifying a first substantially linear portion of the third curve, the first substantially linear portion comprising a positive slope;

identifying a second substantially linear portion of the third curve, the second substantially linear portion comprising a negative slope;

generating a graphical plot of the visual stimulus path, the graphical plot comprising a first linear portion comprising a positive slope and a second linear portion comprising a negative slope;

overlapping the third curve onto the graphical plot of the visual stimulus path; and

adjusting the third curve to fit onto graphical plot of the visual stimulus path.

20. The method of claim 11 wherein the comparing of the dynamic IPDs with the visual stimulus path comprises:

optimizing a graph of the dynamic IPDs with at least one parameter; and

merging the optimized graph of the dynamic IPDs with the visual stimulus path.