Mental alertness and mental proficiency level determination

• US 20070291232A1
• Filed: 06/12/2007
• Published: 12/20/2007
• Est. Priority Date: 02/23/2005
• Status: Active Grant

• Current Assignee: EyeTracking, Inc.
• Original Assignee: EyeTracking, Inc.
• Inventors: Marshall, Sandra
• Granted Patent: US 7,438,418 B2
• Time in Patent Office: Days
• US Class Current: 351/246
• CPC Class Codes: A61B 3/113 for determining or recording eye movement
• CPC Class Codes: A61B 5/16 Devices for psychotechnics using teaching or educational appliances G09B1/00 - G09B7/00; Testing reaction times ; Devices for evaluating the psychological state
• CPC Class Codes: A61B 5/163 by tracking eye movement, gaze, or pupil change
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G06V 40/19 Sensors therefor
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

A method for determining mental proficiency level by monitoring point of gaze, pupillary movement, pupillary response, and other parameters in a subject performing a task, collecting the data in a database, analyzing the data in the database, and assigning the subject to a score indicating the subject'"'"'s particular mental proficiency level in real time.

Claims

1. A method comprising:

obtaining eye tracking data characterizing pupil dilation and point of gaze for an individual;

associating the data with one or more of a plurality of predefined mental proficiency levels; and

identifying the associated predefined mental proficiency level.

2. The method of claim 1 wherein the plurality of predefined mental proficiency levels is generated by:

providing a controlled setting characterized by one or more mental proficiency levels;

subjecting one or more than one test individuals engaged in a task to the controlled setting;

obtaining eye tracking data from the test individuals;

performing calculations on the eye tracking data; and

modeling the eye tracking data to describe the one or more mental proficiency levels.

3. The method of claim 1, further comprising:

performing a discriminant function analysis of the eye tracking data.

4. The method of claim 1, further comprising:

performing a nonlinear classification analysis of the eye tracking data.

5. The method of claim 1, further comprising:

selecting a defined job, task, or activity for the individual to participate in.

6. The method of claim 5 wherein the defined job, task, or activity is selected from a group comprising:

driving a vehicle;

using a vehicle driving simulator;

undertaking problem solving;

or performing a visual search task.

7. The method of claim 1, wherein the eye tracking data is selected from a group comprising pupil size, eyelid opening and closing speed, vertical eyelid separation distance, vertical location of the pupil, or horizontal location of the pupil.

8. The method of claim 1, wherein

the eye tracking data are subjected to an analysis comprising one or more of:

calculating an Index of Cognitive Activity (ICA);

calculating a blink rate;

calculating a movement of the pupil in a horizontal and in a vertical plane; and

calculating a divergence of the horizontal location of the pupil.

9. The method of claim 1, wherein

the mental proficiency level is selected from a group comprising:

expert, intermediate, and novice.

10. The method of claim 1, wherein the data collection and analysis occur in real time.

11. The method of claim 1, wherein the data analysis is performed on pre-existing data.

12. The method of claim 1, wherein the monitoring of pupils occurs at a frequency of up to about 1000 observations per second.

13. The method of claim 1 further comprising:

monitoring eyes of one or more individuals.

14. The method of claim 1 further comprising:

recording the eye tracking data.

15. The method of claim 1 further comprising:

storing the eye tracking data.

16. The method of claim 1 further comprising:

reporting the mental proficiency level of the one or more individuals.

17. The method of claim 16, further comprising:

alerting a third party to the mental proficiency level of the individual.

18. The method of claim 16, further comprising:

providing the individual with feedback identifying the mental proficiency level.

19. A method comprising:

obtaining eye tracking data characterizing pupil dilation and point of gaze for each eye of an individual;

calculating metrics based on the data comprising;

index of cognitive activity, blink rate, rate of pupil movement, rate of change of point of gaze, and divergence between the point of gaze for each eye;

associating the metrics with one or more of at least two mental proficiency levels; and

identifying the associated predefined mental proficiency level.

20. A method comprising:

obtaining eye tracking data characterizing pupil dilation and point of gaze for an individual;

associating the eye tracking data with one or more of a plurality of predefined mental proficiency levels, the predefined mental proficiency levels being generated by providing a setting characterized by one or more mental proficiency levels, subjecting a plurality of test individuals engaged in a task to the setting, obtaining eye tracking data from the test individuals, performing calculations on the eye tracking data, and modeling the eye tracking data to describe the one or more mental proficiency levels; and

identifying the associated predefined mental proficiency level.

21. A method comprising:

obtaining eye tracking data characterizing pupil dilation and point of gaze for an individual;

subjecting the eye tracking data to an analysis comprising one or more of;

calculating an Index of Cognitive Activity (ICA);

calculating a blink rate;

calculating a movement of the pupil in a horizontal and in a vertical plane; and

calculating a divergence of the horizontal location of the pupil;

associating the analyzed eye tracking data with one or more of a plurality of predefined mental proficiency levels; and

identifying the associated predefined mental proficiency level.