Neural Network Eyelid Localization for Quantitative Blink Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies lack robust methods for quantitatively analyzing the blink reflex using high-speed image capture and machine learning to diagnose neurological conditions.
Innovation Solution
A neural network, such as a convolutional or residual neural network, is trained to localize eyelids in images using a training dataset, updating parameters based on measured coordinates to improve eyelid localization accuracy and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-speed image capture is used to record blink reflexes, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex mechanical image capture systems with a neural network-based computational system. Instead of using sophisticated hardware to track eyelid movement, the invention uses a trained neural network that processes standard images to automatically locate and measure eyelid position, substituting mechanical complexity with intelligent algorithmic processing.
Solution Approach 2:
The neural network system performs self-training and self-optimization through automated training procedures. The system uses training images with known eyelid locations to automatically adjust its internal parameters, eliminating the need for manual calibration or complex external measurement equipment, thereby reducing overall system complexity while maintaining high measurement precision.
2Productivity
If machine learning-based image analysis is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent applies preliminary action by training the neural network in advance using extensive training datasets before deployment. The neural network is pre-trained to recognize eyelid locations in various conditions, so that during actual use, it can rapidly analyze images without requiring complex real-time processing or adjustment, achieving high productivity through prior preparation.
Solution Approach 2:
The invention utilizes parameter changes in the neural network's internal structure and configuration to optimize performance. By adjusting parameters such as network architecture, learning rates, and training data characteristics, the system achieves high analysis speed while managing complexity through systematic parameter optimization rather than hardware complexity.
3Measurement precision
If quantitative analysis of blink reflex is implemented, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The neural network system performs self-service by automatically performing all steps of eyelid location detection and blink reflex measurement without manual intervention. The system autonomously processes images, locates eyelids, measures blink parameters, and generates quantitative results, eliminating the need for operators to perform complex manual measurements while maintaining high precision.
Solution Approach 2:
The patent replaces manual measurement procedures with automated neural network-based analysis. Instead of requiring operators to manually measure eyelid positions from high-speed images, the system uses the neural network to automatically detect and measure blink reflex parameters, substituting complex manual operations with intelligent automated processing that is both precise and easy to operate.
Data Source
AI summary
Systems and methods for localizing an upper eyelid in an image of a subject are provided. An image of an eye of the subject is obtained in electronic format. The image is inputted into a trained neural network comprising at least 10,000 parameters, thereby obtaining a set of coordinates for an upper eyelid in the image. This obtaining and inputting can be repeated over the course of a non-zero duration thereby obtaining a corresponding set of coordinates for the upper eyelid in each image in a plurality of images. Each corresponding set of coordinates for the upper eyelid from each image in the plurality of images can be used to determine whether the subject is afflicted with a neurological condition.