Binocular Coordination Analysis for Anticipatory Timing Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies lack effective methods to assess and improve anticipatory timing, which is crucial for sensory-motor coordination and cognitive functions, often impaired by conditions such as ADHD, schizophrenia, autism, and brain trauma, affecting everyday tasks and safety.
Innovation Solution
A system and method using binocular coordination analysis to measure eye movements while tracking a smoothly moving object, generating metrics like disconjugacy and tracking metrics, and providing feedback to diagnose and train anticipatory timing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If binocular coordination analysis is used to measure eye positions, then measurement precision of anticipatory timing is improved, but device complexity increases
Solution Approach 1:
The system segments the measurement process into distinct components: right eye position measurement, left eye position measurement, disconjugacy metric calculation, and baseline comparison. Each component is handled by separate software modules that process data independently before integration, reducing overall system complexity while maintaining high measurement precision.
Solution Approach 2:
The patent introduces intermediate computational metrics (disconjugacy metric, tracking metric) that serve as mediators between raw eye position data and final diagnostic conclusions. These intermediate representations simplify the analysis by transforming complex binocular coordination data into standardized, comparable values against predetermined baselines.
2Productivity
If feedback mechanisms are implemented for cognitive timing training, then productivity of cognitive training is improved, but device complexity increases
Solution Approach 1:
The system implements feedback by comparing measured eye coordination metrics against predetermined baselines and providing performance information to the subject. This feedback loop enables iterative training where subjects can adjust their anticipatory timing based on objective measurements, improving cognitive training productivity through data-driven performance optimization.
Solution Approach 2:
The system enables self-service cognitive training by automatically measuring eye positions, calculating performance metrics, comparing them to baselines, and providing feedback without requiring external intervention. Subjects can independently engage in training sessions and track their own progress, increasing accessibility and training efficiency.
Data Source
AI summary
A method of testing a subject for impairment includes presenting the subject with a display of a smoothly moving object, repeatedly moving over a tracking path and, while presenting the display to the subject, measuring the subject's right eye positions and measuring the subject's left eye positions. The method further includes generating a disconjugacy metric by comparing the measured right eye positions with the measured left eye positions, comparing the disconjugacy metric with a predetermined baseline to determine whether the disconjugacy metric is indicative of an impairment, and generating a report based on the disconjugacy metric.