Adaptive Eye Tracking Sensor Coverage Using an Eye Model
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Gaze tracking systems in augmented reality devices face challenges in simultaneously performing gaze tracking and determining sensing coverage, often requiring gaze tracking to be stopped to re-determine sensing regions, and are prone to failure due to slippage or external factors like noise and pupil occlusion.
Innovation Solution
A method and device for gaze tracking that includes a light emitter and receiver to obtain eyeball data, create an eye model, and dynamically determine a sensing region based on this data, allowing continuous gaze tracking and robust sensing coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If gaze tracking is performed using a fixed sensing region determined by maximum hardware range, then the device can support the maximum sensing coverage, but gaze tracking must be stopped to re-determine sensing coverage when it fails or when eyeball position changes
Solution Approach 1:
The sensing region is dynamically adjusted based on the eye model and real-time eyeball position data. Instead of using a fixed sensing region determined by maximum hardware range, the system continuously adapts the sensing region boundaries to match the actual eye position and characteristics, enabling gaze tracking to continue without interruption even when eyeball position changes or device slippage occurs
Solution Approach 2:
The system uses feedback from the eye model (which is continuously updated with eyeball position data) to adjust the sensing region. This closed-loop approach allows the sensing region to be automatically repositioned and resized based on real-time eye tracking data, eliminating the need to stop gaze tracking for re-determination of coverage
2Area of stationary object
If the sensing region is fixed at maximum hardware range, then the initial sensing coverage is maximized, but the system becomes vulnerable to failure when eyeball position moves due to device slippage or external factors
Solution Approach 1:
The sensing region transitions from a static fixed area to a dynamic adaptive area that automatically adjusts its boundaries based on the eye model. The system continuously monitors eyeball position and recalculates the sensing region parameters (center position, radius, or boundary coordinates) to maintain optimal coverage, preventing tracking failure even when the eye moves outside the original fixed sensing region
Solution Approach 2:
The system changes the parameters of the sensing region (position, size, shape) based on the eye model data. Instead of maintaining a fixed sensing region, the parameters are continuously updated to match the actual eye position and characteristics, making the sensing coverage adaptive rather than static and thereby improving reliability under varying conditions
3Device complexity
If gaze tracking is performed in a fixed sensing coverage, then the system structure remains simple, but the tracking accuracy decreases when external factors like noise or pupil occlusion occur
Solution Approach 1:
The sensing region becomes dynamic and adapts to external factors by using the eye model as a reference. When noise or pupil occlusion occurs, the system can adjust the sensing region boundaries based on the eye model's predicted eye position and characteristics, maintaining tracking accuracy without requiring complex additional hardware or processing
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables continuous and accurate gaze tracking by adaptively adjusting the sensing region, enhancing tracking success and speed while maintaining accuracy despite external factors.
Implementation Method 1
the at least one light receiver obtains first eyeball data, based on light received after being reflected by the eyeball among light emitted by the at least one light emitter
Data Source
AI summary
A method of determining a sensing region of a gaze tracking sensor may include obtaining first eyeball data from a first sensing region. According to an embodiment of the disclosure, a method of determining a sensing region of a gaze tracking sensor may include obtaining an eye model, based on the obtained first eyeball data. According to an embodiment of the disclosure, a method of determining a sensing region of a gaze tracking sensor may include determining a second sensing region, based on the obtained eye model.