Eye Tracking Dominance Detection via Differential Accuracy Scoring
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Solution Overview
Problem
Existing eye tracking systems lack the ability to account for individual differences in eye dominance, which can lead to inaccuracies in tracking and diagnostic assessments, such as Strabismus and Amblyopia, due to equal weighting of data from both eyes.
Innovation Solution
A method to determine eye dominance by calculating separate accuracy scores for each eye using predefined formulas or algorithms based on image data, allowing for differential weighting of eye tracking data and enhancing diagnostic capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data from both eyes are used with equal weighting for eye tracking, then the quantity of tracking data is increased, but the measurement precision deteriorates due to eye dominance differences
Solution Approach 1:
The patent applies local quality by differentiating the treatment of data from each eye based on individual eye dominance characteristics. Instead of uniform processing, the system assigns different weights to left and right eye data according to their respective quality metrics, thereby optimizing tracking precision while preserving the quantity advantage of binocular data
Solution Approach 2:
The system dynamically changes the weighting parameter for each eye based on calculated quality metrics. By adjusting these parameters adaptively rather than using fixed equal weights, the system resolves the contradiction between utilizing abundant binocular data and maintaining high tracking precision accounting for eye dominance variations
2Measurement precision
If separate accuracy scores are calculated for each eye, then the measurement precision is improved, but the device complexity increases
Solution Approach 1:
The patent segments the eye tracking evaluation into separate accuracy score calculations for each eye. By dividing the overall assessment into independent monocular evaluations followed by integration, the system achieves precise eye-specific quality metrics without requiring fundamentally complex additional hardware or processing architecture
Solution Approach 2:
The system uses universal image processing and quality metric calculations that can be applied to each eye independently. The same computational framework processes both eyes' data, reducing the need for eye-specific complex algorithms and thereby limiting the increase in device complexity while maintaining high measurement precision
3Reliability
If eye dominance determination is implemented, then the reliability of diagnostic assessments is improved, but the difficulty of detecting and measuring increases
Solution Approach 1:
The system implements feedback by using the calculated accuracy scores from calibration procedures to determine eye dominance. The quality metrics generated during normal calibration operations are fed back into the eye dominance determination logic, eliminating the need for separate complex diagnostic measurements and improving diagnostic reliability through existing data streams
Data Source
AI summary
The invention relates to an eye tracking system (10) and a method for operating an eye tracking system (10) for determining if one of a left and a right eye of a user is dominant, wherein at least one image of the left and the right eye of the user is captured (S30), based on the at least one image and according to a predefined accuracy function a left accuracy score for the left eye (S34a) and a right accuracy score for the right eye (S34c) is determined and it is determined if one of the left and the right eye of the user is dominant in dependency of at least the left and the right accuracy score (S36). Thereby user-specific properties relating to his eyes can be provided and considered when performing eye tracking so that the robustness and accuracy of eye tracking can be enhanced.