Eye-tracking system for detection of cognitive load

Patent metadata

Field	Value
Publication number	US-11666258-B2
Application number	US-201916523147-A
Country	US
Kind code	B2
Filing date	Jul 26, 2019
Priority date	Jul 27, 2018
Publication date	Jun 6, 2023
Grant date	Jun 6, 2023

Abstract

A visual tracking system, comprises an eye-tracking device and a cognitive load detection device disposed in electrical communication with the eye-tracking device, the cognitive load detection device comprising a controller having a memory and a processor. The controller is configured to receive eye-movement data from the eye-tracking device, the eye-movement data comprising pupil dilation event data and at least one of saccade event data and fixation event data, apply a classification function to the eye-movement data to detect a cognitive load associated with the eye-movement data and corresponding to a visual location of a field of view of the user, and output a notification regarding the cognitive load associated with the eye-movement data.

First claim

What is claimed is:

1. A visual tracking system, comprising:
an eye-tracking device; and
a cognitive load detection device disposed in electrical communication with the eye-tracking device, the cognitive load detection device comprising a controller having a memory and a processor, the controller configured to:
receive eye-movement data from the eye-tracking device, the eye-movement data comprising pupil dilation event data taken during a saccade event and pupil dilation event data taken during a fixation event,
apply a processing function to the eye-movement data to generate processed eye-movement data as a ratio of the pupil dilation event data taken during the saccade event relative to the pupil dilation event data taken during the fixation event,
apply a classification function to the processed eye-movement data to detect a cognitive load associated with the processed eye-movement data and corresponding to a visual location of a field of view of the user, the cognitive load indicating an amount of cognitive resources used by a user when processing information, and
output a notification regarding the cognitive load associated with the processed eye-movement data.

2. The visual feature tracking system of claim 1 , wherein when outputting the notification regarding the cognitive load, the controller is configured to output a user notification to the user, the user notification providing feedback regarding the detected cognitive load.

3. The visual feature tracking system of claim 1 , wherein when outputting the notification regarding the cognitive load, the controller is configured to output a service provider notification to a service provider, the service provider notification identifying the eye-movement data and the detected cognitive load.

4. The visual feature tracking system of claim 1 , wherein the controller is configured to:
build a training data set comprising a set of eye movement inputs and corresponding set of task condition inputs; and
train a classification framework with the training data set to generate the classifier function.

5. The visual tracking system of claim 1 , wherein:
when receiving the eye-movement data from the eye-tracking device, the controller is configured to receive a stream of eye-movement data in substantially real time from the eye-tracking device; and
when applying the classification function to the eye-movement data, the controller is configured to apply the classification function to each data element of the stream of eye-movement data to detect the cognitive load over a time period.

6. In a cognitive load detection device, a method for detecting cognitive load, comprising:
receiving eye-movement data from an eye-tracking device, the eye-movement data comprising pupil dilation event data taken during a saccade event and pupil dilation event data taken during a fixation event;
applying a processing function to the eye-movement data to generate processed eye-movement data as a ratio of the pupil dilation event data taken during the saccade event relative to the pupil dilation event data taken during the fixation event;
applying a classification function to the processed eye-movement data to detect a cognitive load associated with the processed eye-movement data and corresponding to a visual location of a field of view of the user, the cognitive load indicating an amount of cognitive resources used by a user when processing information; and
outputting a notification regarding the cognitive load associated with the processed eye-movement data.

7. The method of claim 6 , wherein outputting the notification regarding the cognitive load comprises outputting a user notification to the user, the user notification providing feedback regarding the detected the cognitive load.

8. The method of claim 6 , wherein outputting the notification regarding the cognitive load comprises outputting a service provider notification to a service provider, the service provider notification identifying the eye-movement data and the detected cognitive load.

9. The method of claim 6 , further comprising:
building a training data set comprising a set of eye movement inputs and corresponding set of task condition inputs; and
training a classification framework with the training data set to generate the classifier function.

10. The method of claim 6 , wherein:
receiving the eye-movement data from the eye-tracking device comprises receiving a stream of eye-movement data in substantially real time from the eye-tracking device; and
applying the classification function to the eye-movement data comprises applying the classification function to each data element of the stream of eye-movement data to detect the cognitive load over a time period.

11. A computer program product having a non-transitory computer-readable medium including computer program logic encoded thereon that, when performed on a controller of a cognitive load detection device causes the cognitive load detection device to:
receive eye-movement data from an eye-tracking device, the eye-movement data comprising pupil dilation event data taken during a saccade event and pupil dilation event data taken during a fixation event;
applying a processing function to the eye-movement data to generate processed eye-movement data as a ratio of the pupil dilation event data taken during the saccade event relative to the pupil dilation event data taken during the fixation event;
apply a classification function to the processed eye-movement data to detect a cognitive load associated with the processed eye-movement data and corresponding to a visual location of a field of view of the user, the cognitive load indicating an amount of cognitive resources used by a user when processing information; and
output a notification regarding the cognitive load associated with the processed eye-movement data.

12. The visual tracking system of claim 1 wherein the pupil dilation event data comprises one of a diameter of a user's pupil and a rate of change of a user's pupil dilation.

13. A visual tracking system, comprising:
an eye-tracking device; and
a cognitive load detection device disposed in electrical communication with the eye-tracking device, the cognitive load detection device comprising a controller having a memory and a processor, the controller configured to:
receive eye-movement data from the eye-tracking device, the eye-movement data comprising pupil dilation event data taken during a saccade event and pupil dilation event data taken during a fixation event,
apply a processing function to the eye-movement data to generate processed eye-movement data as a ratio of a standard deviation of the pupil dilation event data taken during the saccade event relative to a standard deviation of the pupil dilation event data taken during the fixation event,
apply a classification function to the processed eye-movement data to detect a cognitive load associated with the processed eye-movement data and corresponding to a visual location of a field of view of the user, the cognitive load indicating an amount of cognitive resources used by a user when processing information, and
output a notification regarding the cognitive load associated with the processed eye-movement data.

14. A visual tracking system, comprising:
an eye-tracking device; and
a cognitive load detection device disposed in electrical communication with the eye-tracking device, the cognitive load detection device comprising a controller having a memory and a processor, the controller configured to:
receive eye-movement data from the eye-tracking device, the eye-movement data comprising pupil dilatio

Assignees

• Worcester Polytech Inst

Inventors

• Shojaeizadeh Mina
• Djamasbi Soussan
• Paffenroth Randy C
• Trapp Andrew C

Classifications

• G06V40/19

  Sensors therefor · CPC title

• G06F18/214

  Generating training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title

• A61B3/113

  for determining or recording eye movement · CPC title

• A61B5/163 Primary

  by tracking eye movement, gaze, or pupil change · CPC title

• A61B2503/24

  Computer workstation operators · CPC title