Eye Fatigue Detection in Electronic Displays
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Solution Overview
Problem
Conventional remote classes struggle to accurately assess eye fatigue levels of students, as teachers rely solely on image data from electronic device displays, making it difficult to detect and address user fatigue in real-time.
Innovation Solution
An electronic device equipped with a camera to capture eye data, a processing unit using a neural network to estimate eye fatigue levels based on blink frequency, blink duration, eyelid distance, and pupil area, and a display to output string information indicating fatigue levels, allowing for real-time feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If teachers rely solely on image data from display portions in synchronous interactive classes, then the class can be held with real-time interactive communication, but it becomes difficult to accurately evaluate student fatigue levels
Solution Approach 1:
The patent introduces a camera as an intermediary device to capture eye movement data, which then serves as a mediator between the student's physical state and the teacher's evaluation capability. The camera captures images of students' eyes, and the processing portion analyzes these images to detect fatigue indicators, enabling accurate fatigue assessment without requiring direct physical presence
Solution Approach 2:
The patent replaces the mechanical/visual inspection method (teacher directly observing students) with an automated optical detection system. The camera and processing portion automatically analyze eye movement parameters such as blink frequency, pupil area, and eyelid position to objectively measure fatigue levels, substituting human visual assessment with machine-based optical measurement
2Measurement precision
If the camera captures eye data repeatedly to generate multiple image data pieces, then the fatigue estimation accuracy is improved, but the data processing complexity increases
Solution Approach 1:
The processing portion performs preliminary actions by automatically detecting and extracting key eye movement parameters (blink frequency, pupil area, eyelid position) from captured images before fatigue estimation. This preliminary parameter extraction simplifies subsequent fatigue analysis by reducing raw image data to meaningful quantitative indicators that directly relate to fatigue states
Solution Approach 2:
The system implements feedback by displaying the estimated fatigue level back to the user through the display portion. This feedback loop allows users to monitor their own fatigue status in real-time, creating a closed system where the output of the complex processing (fatigue estimate) is immediately useful to the user, justifying the processing complexity through tangible benefit
Data Source
AI summary
An electronic device capable of estimating a user's situation is to be provided. The electronic device includes a camera, a processing portion, and a display portion. The camera has a function of capturing an image of a user's eye and his/her periphery repeatedly to generate a plurality of pieces image data. The processing portion has a function of detecting, from the plurality of pieces of image data, a change over time in information including at least one of a frequency of eye blinks, a time taken for one blink, a distance between an upper eyelid and a lower eyelid, a sight direction, and an area of a pupil, a function of estimating a level of user's eye fatigue on the basis of the change over time in information, and a function of generating string information in accordance with the estimated level of user's eye fatigue. The display portion has a function of displaying string information.