Automatic Eye Gaze Calibration via Background Behavior Analysis
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Solution Overview
Problem
Current eye gaze tracking systems require time-consuming calibration processes that necessitate user cooperation and can be disrupted by environmental changes or eye fatigue, limiting their suitability for various applications.
Innovation Solution
An automatic eye gaze calibration method that monitors and correlates a user's gaze direction during predetermined tasks, calculating usage parameters without requiring directed user effort, using a combination of eye tracking systems and processing means to identify statistically significant patterns and reject outlying data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard calibration methods are used to achieve accurate gaze direction measurement, then measurement precision is improved, but user cooperation and time consumption increase
Solution Approach 1:
The system performs self-calibration by automatically analyzing user behavior patterns and gaze data without requiring active user participation. The calibration process occurs in the background while users perform normal tasks, eliminating the need for users to follow calibration instructions or maintain specific eye positions.
Solution Approach 2:
The system collects and analyzes gaze data during normal task performance to pre-calculate calibration parameters before they are needed for accurate measurement. This preliminary data collection and analysis phase enables subsequent accurate gaze direction determination without requiring dedicated calibration time.
2Measurement precision
If standard calibration methods are used to achieve accurate gaze direction measurement, then measurement precision is improved, but time consumption increases
Solution Approach 1:
The system performs self-calibration by automatically analyzing user behavior patterns and gaze data without requiring active user participation. The calibration process occurs in the background while users perform normal tasks, eliminating the need for users to follow calibration instructions or maintain specific eye positions.
Solution Approach 2:
The calibration process occurs continuously in the background during normal task performance rather than requiring a separate dedicated calibration session. This allows the system to accumulate calibration data and perform calculations without interrupting the user's productive activities.
3Measurement precision
If periodic re-calibration is performed to maintain accuracy under environmental changes, then measurement precision is improved, but user distraction increases
Solution Approach 1:
The system performs self-calibration by automatically analyzing user behavior patterns and gaze data without requiring active user participation. The calibration process occurs in the background while users perform normal tasks, eliminating the need for users to follow calibration instructions or maintain specific eye positions.
Solution Approach 2:
The system performs automatic re-calibration at periodic intervals or when environmental changes are detected, maintaining measurement accuracy without requiring user intervention. This periodic background calibration ensures the system adapts to changing conditions while keeping users focused on their primary tasks.
Data Source
AI summary
A method of calibrating the eye gaze direction of a user, the method including the steps of: (a) monitoring a user's eye gaze direction whilst carrying out a series of predetermined tasks, each of the tasks having an expected subject gaze direction; and (b) correlating the user's eye gaze direction with the expected direction for a statistically significant period of time; (c) calculating, from the correlating step, a series of likely eye gaze direction usage parameters associated with the user.