Oculometric Impairment Vector for Sensorimotor Diagnosis
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Solution Overview
Problem
Current oculometric approaches lack the ability to convert multi-dimensional deficits in eye movement measurements into standardized units for comparing the severity of sensorimotor impairments across different dimensions and combining them into a single scalar measure for diagnosing conditions like traumatic brain injury (TBI) or alcohol intoxication.
Innovation Solution
A computer-implemented method that uses a set of oculometric measures to create an impairment vector, which is compared to search templates to produce an impairment index, allowing for the quantification of sensorimotor functional status and likelihood of specific disease or injury states through eye-movement assessment tests, including a randomized radial tracking task.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple oculometric measures are collected to assess sensorimotor impairment, then measurement precision is improved, but device complexity increases due to the need to process multi-dimensional data across disparate units
Solution Approach 1:
The patent combines multiple oculometric measures (saccadic latency, pursuit gain, smooth pursuit variability, etc.) into a single scalar impairment index through vector summation. Each measure is converted to a standardized unit representing impairment magnitude, then combined into one comprehensive metric that assesses overall sensorimotor function while eliminating the complexity of handling disparate units.
Solution Approach 2:
The patent transforms oculometric measures from their native units (milliseconds, degrees per second squared, dimensionless ratios) into a common standardized unit of impairment magnitude. This parameter transformation enables direct comparison and combination of diverse measures while maintaining the sensitivity of each individual metric.
2Ease of operation
If qualitative oculomotor patterns are converted into standardized units, then ease of operation is improved for clinical interpretation, but measurement precision may be reduced through loss of detailed information
Solution Approach 1:
The patent introduces an intermediary transformation process that converts detailed oculometric patterns into standardized impairment units through a systematic mapping function. This intermediary step preserves the essential information needed for clinical interpretation while translating complex multi-dimensional data into a clinically actionable single metric that maintains fidelity to the original measurements.
3Productivity
If a single scalar impairment index is produced from multi-dimensional oculomotor data, then productivity is improved for rapid diagnosis, but measurement precision may be compromised by oversimplification
Solution Approach 1:
The patent merges multiple oculometric measures into a single impairment index through vector summation, where each measure contributes its impairment magnitude in standardized units. This combining process accelerates diagnosis by providing one comprehensive metric while preserving the information content of all individual measures through their weighted contribution to the final index.
Data Source
AI summary
Various conditions, such as traumatic brain injuries (TBI), diseases, injuries, and/or other impairments, may be diagnosed by deriving a sensitive overall indicator of impairment of sensorimotor functional status based on results of an eye-movement assessment test that includes an appropriately randomized, radial tracking task together with a broad set of oculometric measures. The oculometric measures may be combined to yield the sensitive overall indicator of a particular impairment state. More specifically, the oculometric measures may be vectorized to help diagnose both the type of TBI, disease, or impairment and the extent thereof.