HMD Vision Assessment With Dynamic Stimulus Adjustment
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Solution Overview
Problem
Traditional visual acuity assessment methods lack dynamic adjustment of test parameters and cannot be implemented for home use with household devices, leading to less accurate assessments.
Innovation Solution
Implementing methods and systems using a head-mounted display (HMD) with processors and memory to create a 3D virtual environment, display visual stimuli, and analyze eye images to determine visual processing performance, convergence, and other vision-related factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional visual acuity assessment methods are used, then the testing process is simple and can be implemented with household devices, but the assessment accuracy is reduced due to inability to dynamically adjust test parameters
Solution Approach 1:
The patent implements dynamic adjustment of test parameters including stimulus size, contrast, duration, and inter-stimulus intervals based on real-time eye tracking data and user responses. The system adapts stimulus presentation parameters dynamically to optimize visual acuity measurement precision while maintaining accessibility through consumer electronics devices.
Solution Approach 2:
The system incorporates real-time feedback loops where eye tracking data and user responses are continuously monitored to adjust subsequent stimulus presentation. This feedback mechanism enables the system to maintain optimal test conditions and improve measurement accuracy by adapting to individual user performance and eye movement patterns.
2Measurement precision
If traditional static visual acuity tests are used, then the testing procedure is straightforward, but the visual processing performance assessment is less accurate due to lack of dynamic parameter adjustment
Solution Approach 1:
The system dynamically adjusts multiple test parameters including stimulus presentation duration, inter-stimulus intervals, and stimulus characteristics based on real-time eye tracking measurements and user response patterns. This dynamic adaptation enables comprehensive visual processing performance assessment while the automated control maintains operational simplicity for users.
Solution Approach 2:
The system automatically adjusts test parameters and controls stimulus presentation based on real-time eye tracking data and user responses without requiring operator intervention. This self-adjusting capability maintains ease of operation while significantly improving the precision of visual processing performance assessment through adaptive testing protocols.
3Ease of operation
If home-based vision testing with household devices is implemented, then accessibility and affordability are improved, but measurement precision is reduced compared to clinical settings
Solution Approach 1:
The patent replaces complex clinical measurement equipment with consumer electronics devices (smartphones, tablets, HMDs) equipped with eye tracking cameras and display screens. This substitution maintains accessibility and affordability while achieving clinical-grade measurement precision through sophisticated software algorithms and eye tracking technology that compensate for the simpler hardware platform.
Solution Approach 2:
The system employs multiple parameter changes including stimulus characteristics (size, contrast, duration, temporal frequency), display parameters (brightness, resolution), and eye tracking parameters to optimize measurement precision for home-based testing conditions. These parameter adjustments enable accurate visual acuity and visual processing assessment using household devices rather than specialized clinical equipment.
Data Source
AI summary
A user's visual hallucination condition can be assessed in a virtual environment. An electronic device, such as a head-mounted display, can execute a visual assessment application, including displaying a user interface to create a 3D virtual environment. While displaying a sequence of visual hallucination patterns, the electronic device can obtain a stream of sensor data from one or more sensors. The electronic device can determine a plurality of user responses to the sequence of visual hallucination patterns based on the stream of sensor data and can further determine a type and a severity level of a first visual hallucination condition of a user associated with the electronic device.