Adaptive Vision Rendering With HMD-Based Eye Deficiency Detection
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Solution Overview
Problem
Traditional methods for visual acuity assessment lack dynamic adjustment of test parameters and cannot be implemented for home use with household devices, leading to less accurate assessments.
Innovation Solution
Implementing a virtual vision test using a head-mounted display (HMD) with integrated cameras and electrodes, or wireless communication with wearable devices, to capture eye images and electrical signals, and determine eye health conditions through real-time analysis of eye movement and biometric data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional visual acuity assessment methods are used, then the testing process is simple, but the measurement precision and adaptability are insufficient
Solution Approach 1:
The system segments the visual acuity assessment into multiple independent test components (visual acuity test, visual field test, color vision test, etc.), each can be executed separately and dynamically adjusted. This allows high measurement precision through comprehensive testing while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The testing system implements dynamic adjustment of test parameters based on real-time eye movement tracking and user responses. The system can adaptively modify test difficulty, stimulus presentation, and testing sequence based on measured visual deficiencies, thereby improving measurement precision without requiring overly complex fixed protocols.
2Adaptability or versatility
If traditional visual acuity assessment methods are used, then the device requirements are minimal, but the adaptability for home use and personalized testing is limited
Solution Approach 1:
The system leverages existing household devices (smartphones, tablets, computers) with camera and display capabilities to perform comprehensive vision testing. By making the testing system universal and multi-functional, it can be implemented on various platforms without requiring specialized expensive equipment, thereby improving home testing capability while keeping device requirements manageable.
Solution Approach 2:
The system enables self-service vision testing at home through automated eye movement tracking and diagnostic algorithms. The device automatically captures eye images, processes visual stimuli, analyzes eye movement data, and generates diagnostic reports without requiring professional intervention, thereby improving adaptability for home use while managing device complexity through automation.
3Measurement precision
If dynamic adjustment of test parameters is implemented, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The system implements real-time feedback loops where eye movement data and user responses are continuously monitored and fed back into the testing algorithm. Based on this feedback, the system dynamically adjusts test parameters such as stimulus size, contrast, and presentation rate. This feedback mechanism improves measurement precision by adapting to individual user characteristics while managing complexity through automated decision-making algorithms.
Data Source
AI summary
Eye deficiency can be compensated in image rendering using an electronic device that includes a display and obtains the media content to be rendered on the display. The electronic device can obtain information of a visual deficiency of a user associated with the display. Based on the information of the visual deficiency of the user, the electronic device compensating the media content to generate compensated media content. The compensated media content is rendered on the display for the user. In some embodiments, the electronic device renders a sequence of visual stimuli on a user interface, obtains a plurality of user responses to the sequence of visual stimuli, and identifies the visual deficiency of the user based on the plurality of user responses.