Eye Tracking Fixation Monitoring via Retina Synchronization
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Solution Overview
Problem
Existing ophthalmic devices face challenges in accurately tracking and ensuring proper eye fixation during diagnostic and surgical procedures due to human error and interference from retina scanning systems, which can lead to inaccurate data acquisition and system malfunction.
Innovation Solution
A system combining eye tracking techniques with statistical evaluation and neural networks, utilizing a retina imaging system and an eye tracker to detect the fovea and track eye position and orientation, allowing for absolute fixation monitoring even when retina imaging is not available, and providing feedback to operators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If retina scanning and imaging analysis are used to track eye position and orientation, then measurement precision is improved, but device complexity increases and the system becomes inoperable during certain diagnostic phases
Solution Approach 1:
The patent divides the eye tracking function into two independent segments: (1) retina imaging system for high-precision absolute position detection, and (2) image capture device for continuous relative position tracking. Each segment operates independently during different phases of the diagnostic procedure, allowing the system to maintain measurement precision while reducing operational complexity.
Solution Approach 2:
The patent introduces an intermediary mechanism that transfers fixation information from the retina imaging system to the image capture device. When the retina imaging system detects fovea position, this information serves as a reference point for the image capture device to continuously track eye position without requiring the retina imaging system to remain active, thus resolving the operational complexity issue.
2Ease of operation
If a human operator monitors the patient during data acquisition, then ease of operation is maintained, but reliability decreases due to human error
Solution Approach 1:
The patent implements an automated feedback system where the image capture device continuously monitors eye position and provides real-time feedback to the operator. The system automatically detects when the eye is properly fixated on the target object and when it is not, eliminating human error in monitoring while maintaining ease of operation through automated alerts and indicators.
Solution Approach 2:
The system performs self-monitoring of eye fixation status through automated image analysis. The image capture device independently evaluates whether the patient is properly fixating without requiring continuous human observation, thereby improving reliability while keeping the system easy to operate through automated functionality.
3Measurement precision
If retina imaging system is used for eye tracking, then measurement precision is improved, but productivity decreases due to system shutdown during diagnostic procedures
Solution Approach 1:
The patent uses the retina imaging system to perform preliminary action by detecting the fovea position and establishing a reference fixation point before the main diagnostic procedure begins. This preliminary calibration allows the image capture device to perform continuous eye tracking independently during the diagnostic procedure, maintaining measurement precision while improving productivity by eliminating the need to shut down the imaging system.
Data Source
AI summary
Systems and methods for tracking eye movement during a diagnostic procedure include a retina imaging system configured to capture a first plurality of images of an eye and detect a presence of a fovea, an eye tracker configured to capture a second plurality of images of the eye and track a position and orientation of the eye; and a control processor configured to synchronize the first and second plurality of images, determine a time at which the fovea is detected in the first plurality of images, and determine one or more images from the second plurality of images to be classified as representative of fixation. The classified images are analyzed to determine eye fixation status during the diagnostic procedure based on eye tracking data.