Bayesian Adaptive Visual Sensitivity Measurement System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for assessing visual sensitivity, such as visual acuity tests, fail to adequately describe an individual's spatial visual abilities as they do not account for contrast and target size variations, leading to incomplete assessments of visual function.
Innovation Solution
The development of methods and devices for rapid measurement and classification of contrast sensitivity functions and spatiotemporal contrast sensitivity surfaces using Bayesian adaptive techniques, which select informative stimuli to minimize entropy and maximize information gain, allowing for efficient and precise estimation of visual sensitivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional visual acuity tests are used, then the assessment is simple and quick, but it fails to adequately describe spatial visual abilities and contrast sensitivity
Solution Approach 1:
The patent implements adaptive testing where stimulus parameters (contrast, spatial frequency) dynamically adjust based on subject responses. The system transitions from static fixed-threshold testing to dynamic adaptive testing that optimizes information gain at each step, resolving the contradiction by making the procedure complex only to the extent necessary for precise measurement.
Solution Approach 2:
The patent employs Bayesian feedback mechanisms where each subject response updates the probability distribution of threshold estimates. This feedback loop allows the system to concentrate trials on most informative stimulus conditions, achieving high measurement precision without requiring exhaustive testing across all possible parameters.
2Measurement precision
If comprehensive contrast sensitivity testing is performed across multiple spatial frequencies, then measurement precision improves, but testing time increases significantly
Solution Approach 1:
The patent performs preliminary Bayesian updating with each trial to predict which future stimulus conditions will provide maximum information gain. This preliminary action allows the system to skip redundant trials and focus on the most informative spatial frequencies and contrast levels, reducing total testing time while maintaining precision.
Solution Approach 2:
The patent dynamically changes stimulus parameters (spatial frequency, contrast level) based on accumulated data. Rather than testing all parameter combinations exhaustively, the system adapts parameter selection to maximize information gain about the contrast sensitivity function, achieving comprehensive measurement in fewer trials.
3Loss of information
If adaptive Bayesian methods are used to select informative stimuli, then information gain is maximized, but computational complexity increases
Solution Approach 1:
The patent extracts and implements only the essential Bayesian computational elements needed for adaptive stimulus selection. By focusing on calculating expected information gain for the next trial rather than performing full posterior analysis at each step, the system maximizes information gain while keeping computational requirements manageable.
Data Source
AI summary
The present invention relates to methods for efficient adaptive measurement and classification of contrast sensitivity functions and spatiotemporal contrast sensitivity surface by selecting the most informative stimulus before each trial. Also disclosed are devices for implementing such methods.