Pupil-to-Iris Video Analysis for Non-Contact Psychosensory Evaluation
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Solution Overview
Problem
Conventional pupilometers for evaluating pupillary psychosensory responses are expensive, require trained clinicians, and are not standardized, limiting their application and reliability, especially in detecting subtle changes in pupil diameter due to cognitive and emotional stimuli.
Innovation Solution
An electronic device using image processing techniques, including machine learning and deep learning, to analyze video data and calculate pupil-to-iris ratios, allowing for non-contact, scalable, and accurate assessment of psychosensory responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional pupilometers are used to detect psychosensory responses, then measurement precision is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent uses standard camera images as a copy or substitute for specialized pupilometer imaging. By processing regular video frames through image processing algorithms, the system replicates the measurement capability of expensive pupilometers without requiring specialized hardware, thereby reducing device complexity while maintaining measurement precision
Solution Approach 2:
The patent replaces the mechanical/optical measurement system of conventional pupilometers with a computational image processing system. Instead of using specialized optical instruments to measure pupil diameter, the system uses standard cameras combined with machine learning algorithms to detect and measure pupil changes, substituting mechanical measurement with computational analysis
2Reliability
If conventional pupilometers require trained clinicians for operation, then measurement reliability is improved, but ease of operation deteriorates
Solution Approach 1:
The system performs automated pupil detection and psychosensory response evaluation through machine learning algorithms without requiring human clinicians to manually measure or interpret pupil changes. The algorithm automatically processes video data, detects pupil boundaries, calculates pupil-to-iris ratios, and generates diagnostic information, making the system self-sufficient and eliminating the need for trained operators
Solution Approach 2:
The system incorporates automated feedback mechanisms where the machine learning model continuously learns from processed images and refines its detection accuracy. The system provides real-time feedback on pupil measurements and psychosensory responses, enabling non-experts to obtain reliable measurements without requiring specialized training
3Measurement precision
If conventional pupil systems require controlled ambient lighting conditions, then measurement precision is improved, but adaptability deteriorates
Solution Approach 1:
The system dynamically adapts to varying lighting conditions by using machine learning algorithms that can adjust to different ambient light levels in real-time. Rather than requiring fixed lighting conditions, the system learns to detect pupil boundaries and measure diameters across a range of lighting environments, making the measurement process adaptable to diverse settings including natural light conditions
4Measurement precision
If conventional pupilometers require one-on-one observations with proper alignment, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The system is designed to process multiple video streams simultaneously and can evaluate multiple subjects in parallel without requiring individualized attention or precise one-on-one alignment. The machine learning framework handles multiple inputs concurrently, enabling high-throughput screening while maintaining measurement accuracy across all subjects
Data Source
AI summary
The present disclosure is directed to systems and methods for measuring and analyzing pupillary psychosensory responses. An electronic device is configured to receive video data with at least two frames. The electronic device then locates one or more eye objects in the video data and determine pupil and iris sizes of the one or more eye objects. The electronic device determines the pupillary psychosensory responses of the one or more eye objects by tracking a ratio of pupil diameter to iris diameter throughout the video. Several metrics for the pupillary psychosensory responses can be determined (e.g., velocity of change of the ratio, peak to peak amplitude of the change in ratio over time, etc.). These metrics can be used as measures of an individual's cognitive ability and mental health in a single session or tracked throughout multiple sessions.