VR Eye Tracking for Adaptive Vision Testing at Home
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Solution Overview
Problem
Traditional vision testing methods lack dynamic adjustment of test parameters and cannot be implemented in home environments using household devices, leading to less accurate assessments.
Innovation Solution
Implementing a virtual vision test using a head-mounted display (HMD) with integrated sensors and cameras to capture eye movements and biometric data, allowing real-time analysis and dynamic adjustment of visual stimuli based on user responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional vision testing methods are used, then the testing process is simple, but the measurement precision and adaptability are insufficient
Solution Approach 1:
The vision testing system integrates multiple functions into a single platform: visual acuity testing, eye movement tracking, pupil response monitoring, and dynamic stimulus adjustment. The system can operate in various environments (clinic, home, mobile) and accommodate different user needs through configurable test protocols, thereby achieving high measurement precision without proportionally increasing device complexity
Solution Approach 2:
The system continuously monitors user responses and physiological data (eye movements, pupil dilation) in real-time and uses this feedback to dynamically adjust test parameters such as stimulus size, contrast, and presentation timing. This closed-loop feedback mechanism enables precise adaptive testing while maintaining manageable system complexity through automated control
2Adaptability or versatility
If traditional vision testing methods are used, then the device structure is simple, but the adaptability to different environments and users is limited
Solution Approach 1:
The testing system features dynamic adaptability where test parameters (stimulus characteristics, presentation duration, intensity) are automatically adjusted based on real-time user responses and physiological measurements. The system can adapt to different environments by configuring appropriate stimulus displays and sensor sensitivities for various settings including clinical offices, home environments, and mobile devices
Solution Approach 2:
The system changes multiple parameters simultaneously to adapt to different users and environments: adjusting visual stimulus characteristics (size, contrast, color), sensor sampling rates, and test protocols based on detected user characteristics and environmental conditions. This parameter adaptation enables versatile operation without requiring complete system redesign for each application scenario
3Measurement precision
If traditional vision testing methods are used, then the testing time is short, but the dynamic adjustment capability and measurement accuracy are reduced
Solution Approach 1:
The system continuously collects physiological data and monitors user responses throughout the testing process without interruption. Eye movement tracking, pupil response monitoring, and stimulus presentation occur continuously with automated real-time analysis, eliminating idle periods between measurements and maintaining consistent testing rhythm to improve accuracy without excessive time loss
Solution Approach 2:
Real-time feedback from user responses and physiological measurements enables the system to dynamically adjust test parameters and stimulus presentation timing. The system can accelerate testing by skipping unnecessary stimulus presentations when user responses are clear, or extend specific measurement phases when additional precision is needed, thereby optimizing testing duration for maximum accuracy
Data Source
AI summary
This application is directed to tracking eye positions in a vision test in a virtual reality (VR) environment. An electronic device includes a head-mounted display (HMD) and a camera. The electronic device executes a user application configured to enable a virtual vision test, and generates a VR user interface corresponding to a three-dimensional (3D) virtual environment. The electronic device focuses the camera on an eye area of a user wearing the electronic device, and displays, on the user interface, a visual stimulus corresponding to the virtual vision test. While displaying the visual stimulus, in real time, the electronic device captures a sequence of eye images using the camera, determines eye movement information including a temporal sequence of eyeball positions based on the sequence of eye images, and compares the visual stimulus and the eye movement information to determine an eye health condition.