Eye Gaze Impairment Detection for Human-Operated Control Access
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Solution Overview
Problem
Existing manual tests for detecting impairment, such as nystagmus caused by alcohol or drug consumption, are impractical and yield inconsistent results due to variability in administrator experience and training.
Innovation Solution
An automated system using a light and camera-based setup to determine eye gaze behavior, analyzing factors like smooth pursuit, nystagmus, and pupil size reaction using computer vision and deep learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual nystagmus testing is performed by police officers, then impairment detection can be conducted, but the results are imprecise and inconsistent due to variability in administrator experience and training
Solution Approach 1:
The patent replaces the manual mechanical testing system (officer moving a pen) with an automated optical system using cameras and light sources to track eye movements. This substitution eliminates human variability while maintaining the core testing function, directly resolving the contradiction between measurement precision and device complexity.
Solution Approach 2:
The system creates a digital copy of the manual testing process through automated image capture and analysis. By copying the essential measurements (eye position, movement patterns) into digital form and analyzing them through algorithms, the system achieves consistent, repeatable results without requiring administrator expertise.
2Measurement precision
If automated computer vision and deep learning techniques are used to analyze eye gaze behavior, then measurement precision and consistency are improved, but device complexity increases
Solution Approach 1:
The patent replaces complex human cognitive analysis with automated computer vision and deep learning algorithms. The system uses machine learning models trained on eye movement data to automatically detect patterns indicating impairment, substituting human judgment with algorithmic analysis that provides consistent, scalable precision.
Solution Approach 2:
The system performs self-calibration and self-analysis through automated image processing. The deep learning models automatically adjust to individual variations in eye anatomy and movement patterns, enabling the system to serve itself by adapting to each subject without requiring manual configuration or expert intervention.
3Reliability
If automated testing systems are deployed to eliminate human variability, then reliability of results is improved, but ease of operation may be reduced due to technical complexity
Solution Approach 1:
The automated system performs all testing, analysis, and determination functions autonomously. The system self-manages the entire workflow from capturing images to analyzing eye movements to determining impairment status, eliminating the need for trained administrators while maintaining high reliability through consistent automated execution.
Solution Approach 2:
The system incorporates real-time feedback mechanisms where the automated analysis results immediately inform the operational outcome (e.g., whether a driver is cleared to operate). This closed-loop feedback ensures reliability while keeping the operational interface simple, as the system automatically communicates its determination without requiring human interpretation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides consistent and precise results for determining impairment, enabling preventive measures such as locking vehicle operation until the driver is deemed safe to operate.
Implementation Method 1
obtain a sequence of images representative of a state of the user during the performance of the task
Data Source
AI summary
Approaches presented herein provide for the automated determination of a level of impairment of a person, as may be relevant to the performance of a task. A light and camera-based system can be used to determine factors such as gaze nystagmus that are indicative of inebriation or impairment. A test system can simulate motion of a light using a determined pattern, and capture image data of at least the eye region of a person attempting to follow the motion. The captured image data can be analyzed using a neural network to infer at least one behavior of the user, and the behavior determination(s) can be used to determine a capacity or level of impairment of a user. An appropriate action can be taken, such as to allow a person with full capacity to operate a vehicle or perform a task, or to block access to such operation or performance if the person is determined to be impaired beyond an allowable amount.