VISUALLY DIRECTED HUMAN-COMPUTER INTERACTION FOR MEDICAL APPLICATIONS

• US 20110270123A1
• Filed: 11/03/2009
• Published: 11/03/2011
• Est. Priority Date: 11/03/2008
• Status: Active Grant

• Current Assignee: Bruce Reiner
• Original Assignee: Bruce Reiner
• Inventors: Reiner, Bruce
• Granted Patent: US 9,841,811 B2
• Time in Patent Office: Days
• US Class Current: 600/558
• CPC Class Codes: A61B 3/113 for determining or recording eye movement
• CPC Class Codes: A61B 6/463 characterised by displaying multiple images or images and diagnostic data on one display
• CPC Class Codes: A61B 6/465 adapted to display user selection data, e.g. graphical user interface, icons or menus
• CPC Class Codes: A61B 6/467 characterised by special input means
• CPC Class Codes: A61B 6/468 allowing annotation or message recording
• CPC Class Codes: A61B 6/469 for selecting a region of interest [ROI]
• CPC Class Codes: A61F 4/00 Methods or devices enabling patients or disabled persons to operate an apparatus or a device not forming part of the body operating or control means for prostheses A61F2/48, A61F2/68
• CPC Class Codes: G06F 3/013 Eye tracking input arrangements G06F3/015 takes precedence

Abstract

The present invention relates to a method and apparatus of utilizing an eye detection apparatus in a medical application, which includes calibrating the eye detection apparatus to a user; performing a predetermined set of visual and cognitive steps using the eye detection apparatus; determining a visual profile of a workflow of the user; creating a user-specific database to create an automated visual display protocol of the workflow; storing eye-tracking commands for individual user navigation and computer interactions; storing context-specific medical application eye-tracking commands, in a database; performing the medical application using the eye-tracking commands; and storing eye-tracking data and results of an analysis of data from performance of the medical application, in the database. The method includes performing an analysis of the database for determining best practice guidelines based on clinical outcome measures.

Claims

1. A method of utilizing an eye detection apparatus in a medical application, comprising:

calibrating the eye detection apparatus to a user;

performing a predetermined set of visual and cognitive steps using the eye detection apparatus;

determining a visual profile of a workflow of the user;

creating a user-specific database to create an automated visual display protocol of said workflow;

storing eye-tracking commands for individual user navigation and computer interactions;

storing context-specific medical application eye-tracking commands, in a database;

performing the medical application using said eye-tracking commands; and

storing eye-tracking data and results of an analysis of data from performance of the medical application, in said database.

3. The method according to claim 1, further comprising:

making individual profiles of said eye-tracking commands available to other users for at least one of protocol optimization, education, or training.

4. The method according to claim 3, wherein said automated visual display protocol results in creating an automated visual workflow templates based upon data analysis of the user and reference peer groups.

5. The method according to claim 1, wherein said eye-tracking commands include at least one of blink, blink rate, gaze direction, or length of gaze.

6. The method according to claim 3, further comprising:

performing an analysis of said database for determining best practice guidelines based on clinical outcome measures.

7. The method according to claim 1, further comprising:

integrating the eye tracking apparatus with an affective computing and fatigue measuring system; and

correlating eye-tracking patterns of the user with fatigue and stress measurements.

8. The method according to claim 7, wherein the eye tracking apparatus becomes sensitized to changes in a user'"'"'s physical and emotional state and automatically adjusts its internal sensitivity settings to said changes.

9. The method according to claim 7, further comprising:

prompting the user when changes in a user'"'"'s physical and emotional state exceed a predetermined threshold.

10. The method according to claim 7, further comprising:

shutting down computer operations or mandating a rest period by the user until changes in the user'"'"'s physical and emotional state return below a predetermined threshold.

11. The method according to claim 7, further comprising:

initiating a sequence of training/feedback steps due to changes in a user'"'"'s physical and emotional state; and

re-adjusting internal visual recognition parameters due to completion of said sequence.

12. The method according to claim 1, wherein said eye-tracking commands include at least one of object selection, object movement, text scrolling, image scrolling, menu commands, or editing functions.

13. The method according to claim 1, wherein the medical application is performed using non-command based user interactions.

14. The method according to claim 4, further comprising:

performing an analysis of eye-tracking data, including providing automated data extraction analysis on eye fixations and saccades by one of eye positioning or eye velocity.

15. The method according to claim 14, wherein said medical application is a radiological application, and further comprising:

applying selective resolution and decompression algorithms, and specialized image processing of images obtained during the radiological application.

16. The method according to claim 15, further comprising:

modifying and refining individual user'"'"'s automated visual display protocol; and

processing automated visual workflow templates in accordance with the user'"'"'s modified automated visual display protocol and in context with the medical application.

17. The method according to claim 1, wherein eye-tracking metrics recorded by said eye-tracking apparatus include gaze rate;

gaze direction;

gaze percentage;

number of fixations;

scan path directionality;

number of instances of backtracking;

frequency of long duration dwells over areas of interest;

saccade length; and

fixation/saccade time ratio.

18. The method according to claim 1, further comprising:

performing feedback operations with the user to assess an efficiency of the user during the medical application.

19. The method according to claim 18, wherein indicators of inefficiency include backtracking over a same visual area, or fixation over a visual area.

20. The method according to claim 18, further comprising:

obtaining a baseline measure of visual acuity and fatigue of the user.

21. The method according to claim 17, further comprising:

recording visual metrics in numerical and graphical formats.

22. The method according to claim 21, further comprising:

creating visual maps, which graphically show each of said eye-tracking commands in chronological order, with timestamping of same.

23. The method according to claim 4, wherein a playback function cycles through the automated visual workflow template.

24. The method according to claim 14, further comprising:

providing an alert to users when deficiencies in said eye-tracking analysis is found.

25. The method according to claim 19, wherein a timing device forwards alerts to users when fixations are noted.

2. The method according to claim 2, further comprising:

performing a training program with the user, on the eye detection apparatus.

26. An apparatus for performing a radiological application, comprising:

an eye detection apparatus which detects eye movements of a user and creates a visual profile of a workflow of the user;

an imaging device, which takes images in a medical application, of the user; and

a database for recording eye-tracking information from the user and said visual profiles of the user, along with said images taken in the medical application.