Retinal Imaging for Gaze Depth Tracking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing eye-tracking techniques struggle to accurately determine the depth of a user's gaze, leading to inaccuracies in tracking eye characteristics such as gaze direction and accommodation, which is crucial for enhancing extended reality (XR) experiences.
Innovation Solution
The method involves retinal imaging to track eye accommodation by analyzing changes in the retinal image, specifically through scaling and defocus effects, using a processor to produce light that reflects off the retina, and comparing the image with a representation of the eye to estimate defocus and geometric scaling, thereby determining the user's focused depth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing eye-tracking techniques analyze glints reflected off the user's eye, then the system can capture eye images using light from edge sources, but the accuracy in determining the depth of the viewer's gaze deteriorates
Solution Approach 1:
The patent introduces a light sheet as an intermediary element that passes through the pupil to illuminate the retina. This light sheet acts as a mediator between the external light source and the retinal tissue, enabling depth-dependent illumination patterns that encode accommodation information. The light sheet creates depth-resolved reflectance patterns on the retina that can be analyzed to determine gaze depth, thereby improving measurement precision without requiring complex multi-source configurations.
Solution Approach 2:
The patent exploits changes in retinal reflectance parameters (intensity and pattern) as a function of accommodation depth. By analyzing how the retinal image parameters change with different focal depths, the system can determine gaze depth. This approach transforms the static retinal reflection into a dynamic parameter set that varies systematically with accommodation, enabling accurate depth measurement through parameter analysis rather than complex hardware.
2Productivity
If head mounted systems use edge light sources for eye tracking, then the system structure is simplified, but the ability to track user's gaze depth in real-time deteriorates
Solution Approach 1:
The patent performs preliminary capture of retinal images at multiple accommodation depths during an enrollment phase. These pre-captured images serve as reference templates that encode the characteristic light sheet patterns at different depths. During real-time operation, the system compares incoming retinal images against these pre-established references, enabling rapid gaze depth tracking without requiring complex real-time computation or multiple light sources.
Solution Approach 2:
The patent creates copies of retinal images at different accommodation depths during enrollment and stores them as reference templates. These copied images represent the expected retinal appearance at various depths. During tracking, the system matches real-time retinal images against these copies to determine current accommodation depth, enabling fast real-time performance by avoiding complex iterative calculations and leveraging pre-computed reference data.
3Measurement precision
If retinal imaging is used to determine eye accommodation, then the accuracy of determining user's focused depth is improved, but the system requires producing light that reflects off the retina
Solution Approach 1:
The patent utilizes the eye's own optical system (cornea, lens, vitreous humor) to serve the dual purpose of focusing external light onto the retina and simultaneously creating the light sheet pattern needed for depth encoding. The eye's natural optics act as a self-service mechanism that converts incoming light into the required illumination pattern without requiring additional active illumination components, thereby improving measurement precision while minimizing energy consumption.
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
This approach improves the accuracy of eye-tracking, allowing for precise determination of the user's focused depth and enhancing the XR experience by adjusting the perceived depth of content to match the user's accommodation, thereby providing a more immersive interaction.
Implementation Method 1
producing light that reflects off a retina of an eye
Implementation Method 2
light that reflects off a retina of an eye
Implementation Method 3
Changes in eye accommodation induces two effects in the retinal image - scaling and defocus
Implementation Method 4
the image corresponding to a plurality of reflections of the light scattered from the retina of the eye
Data Source
AI summary
Various implementations disclosed herein include devices, systems, and methods that track an eye characteristic (e.g., gaze direction, eye orientation, etc.). For example, an example process may include producing light that reflects off a retina of an eye, receiving an image of a portion of the retina from an image sensor, the image corresponding to a plurality of reflections of the light scattered from the retina of the eye, obtaining a representation of the eye corresponding to a first accommodative state, the representation representing at least some of the portion of the retina, and tracking an eye characteristic based on a comparison of the image of the portion of the retina with the representation of the eye.