Eye Tracking Calibration via Display Alteration
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Solution Overview
Problem
Existing eye tracking systems often provide inaccurate gaze location estimates and require time-consuming and distracting calibration processes, which are undesirable for users.
Innovation Solution
An eye tracking system that uses a processor to gather gaze data, alter display elements to provoke user reactions, and iteratively update calibration settings to improve accuracy, potentially incorporating machine learning for enhanced performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration processes are used to improve gaze location accuracy, then measurement precision is improved, but loss of time and user convenience deteriorate
Solution Approach 1:
The system performs automatic calibration without requiring active user participation. The eye tracking system continuously monitors gaze data and automatically adjusts calibration parameters based on detected gaze patterns, eliminating the need for users to manually follow calibration instructions or spend time on calibration procedures.
Solution Approach 2:
The system uses continuous feedback from gaze data to iteratively improve calibration accuracy. By monitoring user gaze patterns over time and comparing estimated gaze locations with actual fixation points, the system automatically refines calibration parameters to maintain high measurement precision without requiring repeated manual calibration sessions.
2Measurement precision
If traditional calibration processes are used to improve gaze location accuracy, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The calibration process operates autonomously without requiring user intervention. The system automatically captures gaze data, processes it through calibration algorithms, and updates calibration settings in the background, making the entire process transparent and convenient for users who simply need to view content normally.
Solution Approach 2:
The system performs calibration actions in advance and continuously in the background before and during content viewing. By pre-calibrating and continuously refining gaze location estimates without interrupting the user workflow, the system ensures high measurement precision is achieved before the user actually needs to interact with the content.
3Measurement precision
If gaze data is continuously collected and processed to improve accuracy, then measurement precision is improved, but use of energy and computational resources increases
Solution Approach 1:
The system maintains continuous gaze data collection and processing to ensure consistently high measurement precision throughout the viewing session. Rather than performing discrete calibration steps, the system continuously refines gaze location estimates in real-time, maintaining accuracy without requiring repeated interruptive calibration sessions that would consume more total energy.
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
The system increases the accuracy of gaze location estimation while reducing user distractions and calibration time, providing a more efficient and effective eye tracking experience.
Implementation Method 1
Common eye tracking systems utilize cameras or optical sensors (e.g., photodetectors, etc.) to detect visible or infrared light emitting or reflecting from the users
Data Source
AI summary
Systems and methods to monitor and interact with users viewing screens are disclosed. An example system includes a sensor to gather gaze data from a user viewing images on a display. The display has spatial coordinates. The system includes a processor communicatively coupled to the display and the sensor. The processor determines a first gaze location having first spatial coordinates based on the gaze data and calibration settings associated with determining gaze locations. The processor alters a portion of the display at or near the first gaze location. After the portion of the display has been altered, the processor determines a second gaze location having second spatial coordinates based on the gaze data and the calibration settings. The processor performs a comparison of the first gaze location to the second gaze location. If the comparison does not meet a threshold, the processor updates the calibration settings based on the comparison and determines a third gaze location having third spatial coordinates based on the gaze data and the updated calibration settings.