Eye tracking system for determining user activity
Embodiments relate to an eye tracking system. A headset of the system includes an eye tracking sensor that captures eye tracking data indicating positions and movements of a user's eye. A controller (e.g., in the headset) of the tracking system analyzes eye tracking data from the sensors to determine eye tracking feature values of the eye during a time period. The controller determines an activity of the user during the time period based on the eye tracking feature values. The controller updates an activity history of the user with the determined activity.
1 . A method comprising:
analyzing eye tracking data to determine first eye tracking feature values for a first eye tracking feature of an eye of a user of a headset during a time period, the first eye tracking feature of the eye being a first characteristic of the eye, wherein the eye tracking data is determined from an eye tracking system on the headset;
analyzing the eye tracking data to determine second eye tracking feature values for a second eye tracking feature of the eye of the user of the headset during the time period, the second eye tracking feature of the eye being a second characteristic of the eye and being different than the first eye tracking feature;
determining an activity of the user during the time period based on the determined first eye tracking feature values and the determined second eye tracking feature values, wherein the activity of the user is determined without referencing an outward facing camera image, and wherein the determined activity of the user is not the first eye tracking feature values or the second eye tracking feature values;
updating an activity history of the user with the determined activity;
monitoring changes in the first eye tracking feature values and monitoring changes in the second eye tracking feature values; and
determining that the user transitions from the activity to a second activity based on the monitored changes of the first eye tracking feature values and the second eye tracking feature values.
2 . The method of claim 1 , wherein determining the activity comprises identifying first eye tracking feature values and second eye tracking feature values that correspond to the activity.
3 . The method of claim 2 , wherein the first eye tracking feature values include movements of the eye, and determining the activity comprises identifying movements of the eye that correspond to the activity.
4 . The method of claim 1 , wherein determining the activity of the user comprises determining eye tracking feature vectors representing first eye tracking feature values and second eye tracking feature vectors for points in time during the time period, wherein at least one of the determined eye tracking feature vectors represents one of the first eye tracking feature values and one of the second eye tracking feature values for a point in time during the time period.
5 . The method of claim 4 , wherein the activity is determined by analyzing a distribution of the eye tracking feature vectors over the time period.
6 . The method of claim 4 , wherein determining the activity comprises:
applying a vector clustering model to the eye tracking feature vectors to form activity clusters; and
determining the activity based on activity clusters at points in time during the time period.
7 . The method of claim 1 , wherein multiple activities performed by the user throughout a day are determined.
8 . A non-transitory computer-readable storage medium comprising stored instructions, the instructions when executed by a computer device, causing the computer device to:
analyze eye tracking data to determine first eye tracking feature values for a first eye tracking feature of an eye of a user of a headset during a time period, the first eye tracking feature of the eye being a first characteristic of the eye, wherein the eye tracking data is determined from an eye tracking system on the headset;
analyzing the eye tracking data to determine second eye tracking feature values for a second eye tracking feature of the eye of the user of the headset during the time period, the second eye tracking feature of the eye being a second characteristic of the eye and being different than the first eye tracking feature;
determine an activity of the user during the time period based on the determined first eye tracking feature values and the determined second eye tracking feature values, wherein the activity of the user is determined without referencing an outward facing camera image, and wherein the determined activity of the user is not the first eye tracking feature values or the second eye tracking feature values;
update an activity history of the user with the determined activity;
monitoring changes in the first eye tracking feature values and monitoring changes in the second eye tracking feature values; and
determining that the user transitions from the activity to a second activity based on the monitored changes of the first eye tracking feature values and the second eye tracking feature values.
9 . The non-transitory computer-readable storage medium of claim 8 , wherein to determine the activity, the non-transitory computer-readable storage medium further comprises instructions that cause the computer device to identify first feature values and second feature values that correspond to the activity.
10 . The non-transitory computer-readable storage medium of claim 9 , wherein:
the first feature values include movements of the eye; and
to determine the activity, the non-transitory computer-readable storage medium further comprises instructions that cause the computer device to identify movements of the eye that correspond to the activity.
11 . The non-transitory computer-readable storage medium of claim 8 , wherein to determine the activity, the non-transitory computer-readable storage medium further comprises instructions that cause the computer device to determine eye tracking feature vectors representing first eye tracking feature values and second eye tracking feature vectors for points in time during the time period, wherein at least one of the determined eye tracking feature vectors represents one of the first eye tracking feature values and one of the second eye tracking feature values for a point in time during the time period.
12 . The non-transitory computer-readable storage medium of claim 11 , wherein to determine the activity, the non-transitory computer-readable storage medium further comprises instructions that cause the computer device to analyze a distribution of the eye tracking feature vectors over the time period.
13 . The non-transitory computer-readable storage medium of claim 11 , wherein to determine the activity, the non-transitory computer-readable storage medium further comprises instructions that cause the computer device to:
apply a vector clustering model to the eye tracking feature vectors to form activity clusters; and
determine the activity based on activity clusters at points in time during the time period.
14 . The non-transitory computer-readable storage medium of claim 8 , wherein the non-transitory computer-readable storage medium comprises instructions that cause the computer device to determine and track multiple activities of the user throughout a day.
15 . A headset comprising:
one or more sensors embedded into a frame of the headset and configured to capture eye tracking data indicating positions and movements of an eye of a user of the headset; and
a controller configured to:
analyze eye tracking data from the one or more sensors to determine first eye tracking feature values for a first eye tracking feature of the eye during a time period, the first eye tracking feature of the eye being a first characteristic of the eye;
analyzing the eye tracking data to determine second eye tracking feature values for a second eye tracking feature of the eye of the user of the headset during the time period, the second eye tracking feature of the eye being a second characteristic of the eye and being different than the first eye tracking feature;
determine an activity of the user during the time period based on the determined first eye tracking feature values and the determined second eye tracking feature values, wherein the activity of the user is determined without referencing an outward facing camera image, and wherein the determined activity of the user is not the first eye tracking feature values or the second eye tracking feature values;
update an activity history of the user with the determined activity;
monitoring changes in the first eye tracking feature values and monitoring changes in the second eye tracking feature values; and
determining that the user transitions from the activity to a second activity based on the monitored changes of the first eye tracking feature values and the second eye tracking feature values.
16 . The headset of claim 15 , wherein to determine the activity, the controller is further configured to identify first feature values and second eye tracking feature values that correspond to the activity.