Pupil dynamics, physiology, and context for estimating vigilance
A computer system records eye tracking data and identifies movements in the eye tracking data to determine gaze and pupil dynamics. Eye tracking data is correlated with a current task and predetermined vigilance requirements. The system determines if the user is exhibiting an appropriate level of vigilance based on the task or is becoming fixated. When fixation is detected, the system may engage in remedial action. A task flow diagram represents the operator tasks. Interactions between the user and the instrumentation are used to estimate the probability distribution of the task the user is currently conducting. The system correlates eye tracking data and physiological data such as electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRs) to determine neuroactivity. Monitoring neuroactivity reduces the probability of a false positive for fixation.
1. A computer apparatus comprising:
at least one eye tracking camera;
one or more physiological data recording devices including at least a functional near-infrared spectroscope (fNIRs) and an electroencephalogram (EEG); and
at least one processor in data communication with a memory storing processor executable code; and
wherein the processor executable code configures the at least one processor to:
receive an image stream from the at least one eye tracking camera;
receive physiological data from the one or more physiological data recording devices; and
identify gaze and pupil dynamics from the image stream;
correlate the physiological data with the gaze and pupil dynamics;
monitor neuroactivity via the fNIRs and EEG to reduce a probability of a false positive for fixation;
correlate the gaze and pupil dynamics with a current task; and
determine when a duration of gaze is within a threshold of acceptable attention for the current task with reference to the physiological data.
2. The computer apparatus of claim 1 , wherein:
the processor executable code further configures the at least one processor to receive a task or user specific profile of gaze, pupil dynamics, and physiological data; and
determining when the duration is within a threshold of acceptable attention includes reference to the task or user specific profile.
3. The computer apparatus of claim 1 , further comprising a data storage element in data communication with the at least one processor, wherein the processor executable code further configures the at least one processor to continuously store the gaze and pupil dynamics in the data storage element.
4. The computer apparatus of claim 3 , wherein the processor executable code further configures the at least one processor to:
analyze the stored gaze and pupil dynamics over time to identify a user specific profile of a user; and
subsequently compare the gaze and pupil dynamics to the user specific profile.
5. The computer apparatus of claim 1 , wherein the processor executable code further configures the at least one processor as a machine learning neural network.
6. A method comprising:
receiving an image stream from at least one eye tracking camera;
receiving physiological data from one or more physiological data recording devices including at least a functional near-infrared spectroscope (fNIRs) and an electroencephalogram (EEG); and
identifying gaze and pupil dynamics from the image stream;
correlating the physiological data with the gaze and pupil dynamics;
monitoring neuroactivity via the fNIRs and EEG to reduce a probability of a false positive for fixation;
correlating the gaze and pupil dynamics with a current task; and
determining when a duration of gaze is within a threshold of acceptable attention for the current task with reference to the physiological data.
7. The method of claim 6 , further comprising receiving a task or user specific profile of gaze, pupil dynamics, and physiological data, wherein determining when the duration of gaze is within the threshold includes reference to the task or user specific profile.
8. The method of claim 6 , further comprising continuously storing the gaze and pupil dynamics in a data storage element.
9. The method of claim 8 , further comprising:
analyzing the stored gaze and pupil dynamics over time to identify a user specific profile of a user; and
subsequently comparing the gaze and pupil dynamics to the user specific profile.
10. The method of claim 6 , wherein determining when the duration of gaze is within the threshold includes comparing the duration to a probability distribution of previously recorded user's executing the current task.
11. The method of claim 6 , further comprising initiating a remedial action when the gaze and pupil dynamics are characterized as stimuli triggered and no anticipatory response is identified.
12. A simulator comprising:
at least one eye tracking camera;
one or more physiological data recording devices including at least a functional near-infrared spectroscope (fNIRs) and an electroencephalogram (EEG); and
at least one processor in data communication with a memory storing processor executable code; and
wherein the processor executable code configures the at least one processor to:
receive an image stream from the at least one eye tracking camera;
receive physiological data from the one or more physiological data recording devices; and
identify gaze and pupil dynamics from the image stream;
correlate the physiological data with the gaze and pupil dynamics;
monitor neuroactivity via the fNIRs and EEG to reduce a probability of a false positive for fixation;
correlate the gaze and pupil dynamics with a current task; and
determine when a duration of gaze is within a threshold of acceptable attention for the current task with reference to the physiological data.
13. The simulator of claim 12 , wherein:
the processor executable code further configures the at least one processor to receive a task or user specific profile of gaze, pupil dynamics, and physiological data; and
determining when the duration is within a threshold of acceptable attention includes reference to the task or user specific profile.
14. The simulator of claim 12 , further comprising a data storage element in data communication with the at least one processor, wherein the processor executable code further configures the at least one processor to continuously store the gaze and pupil dynamics in the data storage element.
15. The simulator of claim 12 , wherein the processor executable code further configures the at least one processor to: analyze the stored gaze and pupil dynamics over time to identify a user specific profile of a user; and subsequently compare the gaze and pupil dynamics to the user specific profile.