Eye Movement Synchronization for High-Resolution Oculometric Tracking
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Solution Overview
Problem
Existing technologies for measuring minute eye movements are costly, time-consuming, and require controlled lab settings, often failing to achieve the necessary resolution for neurological disorder assessments using standard cameras.
Innovation Solution
Systems and methods utilizing standard cameras and probabilistic methods, signal processing, and machine learning models to obtain high-resolution oculometric parameters, enabling remote and uncontrolled setting measurements of eye movements for neurological disorder diagnostics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If standard cameras are used to capture eye movements, then device cost and setup complexity are reduced, but measurement precision deteriorates
Solution Approach 1:
The patent replaces traditional mechanical eye tracking systems with a camera-based optical system combined with computational algorithms. Instead of using specialized infrared cameras and mechanical trackers, the invention uses standard webcams or smartphones captured images processed through machine learning models and signal processing techniques to extract eye movement data, thereby reducing hardware complexity while maintaining measurement precision
Solution Approach 2:
The patent transforms the measurement approach by changing from direct pixel-based detection to sub-pixel level parameter estimation. Through probabilistic methods and blind deconvolution algorithms, the system extracts oculometric parameters (pupil center, iris radius, eyelid coordinates) at a resolution finer than the camera's physical pixel grid, achieving high precision despite using standard camera hardware
2Measurement precision
If controlled lab settings are used for eye movement measurement, then measurement precision is improved, but ease of operation and accessibility deteriorate
Solution Approach 1:
The patent enables the system to automatically adapt to uncontrolled environmental conditions without requiring manual intervention or controlled settings. The machine learning models and signal processing algorithms self-correct for variations in lighting, camera position, and background conditions, allowing the system to operate autonomously in real-world settings while maintaining measurement precision
Solution Approach 2:
The system changes the operational parameters by transitioning from controlled laboratory conditions to uncontrolled environmental conditions. Through robust algorithms that can handle variable lighting and camera conditions, the patent enables accurate eye movement measurement in natural settings, thereby improving accessibility while maintaining precision
3Measurement precision
If specialized eye tracking devices are used, then measurement precision is improved, but device cost and time to setup increase
Solution Approach 1:
The patent employs inexpensive standard cameras (webcams, smartphone cameras) instead of costly specialized eye tracking hardware. By using readily available camera devices and processing them through computational algorithms, the system achieves the desired measurement precision without requiring expensive equipment, thereby reducing both device cost and setup time
Solution Approach 2:
The invention substitutes complex mechanical eye tracking systems with a simpler camera-based system enhanced by computational algorithms. Instead of using specialized infrared cameras, mechanical trackers, and complex synchronization hardware, the patent uses standard camera imaging combined with machine learning models and signal processing to extract eye movement parameters, significantly reducing setup complexity and time
4Ease of operation
If neural networks are used to obtain eye movements, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent merges multiple approaches by combining machine learning models with traditional signal processing methods and probabilistic algorithms. This hybrid approach integrates the ease of operation provided by neural networks with the precision of conventional image processing techniques, achieving both simplified operation and high measurement precision through a comprehensive multi-method framework
Data Source
AI summary
Disclosed are systems and methods for extracting high resolution oculometric parameters and eye movement parameters. A video stream having a video of a face of a user is processed to obtain a set of oculometric parameters, such as eyelid data, iris data (e.g., iris translation, iris radius and iris rotation), and pupil data (e.g., pupil center and pupil radius). The oculometric parameters are generated at a first temporal resolution. The oculometric parameters are up sampled to increase the temporal resolution to a second temporal resolution. The oculometric parameters are then processed to generate various eye movement parameters such as blink parameter, pupil response parameter, saccade parameter, anti-saccade parameter, fixation parameter, or smooth pursuit parameter. The oculometric parameters are synchronized with a video stimulus presented on a user device prior to generating the eye movement parameters.