Automated Infant Attention Measurement Using Gaze Maps
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current attention tests for infants and children are subjective and unreliable due to reliance on observational methods and limited use of eye trackers, which are affected by tester bias and calibration issues.
Innovation Solution
A method and apparatus for automatically measuring attention by segmenting objects of interest in video frames into component units, generating score distributions, and creating attention maps and movement maps based on gaze point frequency, distribution, and movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If observational methods with checklists are used for attention testing, then the test can be performed with simple equipment, but the reliability and objectivity of the test results deteriorate due to subjective factors
Solution Approach 1:
The patent replaces the mechanical/subjective observational system with an automated eye-tracking system that uses cameras and image processing algorithms to objectively measure attention. The eye tracker captures gaze points and the system automatically analyzes fixation duration, saccade patterns, and attention metrics, eliminating tester subjectivity while maintaining ease of use through automated software processing.
2Measurement precision
If eye trackers are used for attention measurement, then the objectivity of measurement is improved, but the device complexity and calibration requirements increase
Solution Approach 1:
The eye-tracking system incorporates automatic calibration features where the system performs self-calibration using the subject's natural eye movements during the test. The calibration process is integrated into the test procedure itself, requiring minimal separate setup and reducing the burden on operators while maintaining measurement precision.
Solution Approach 2:
The patent segments the attention measurement into distinct analytical components: fixation detection, saccade tracking, attention map generation, and quantitative metric calculation. This segmentation allows each function to be processed independently by specialized algorithms, reducing overall system complexity while improving measurement precision through focused analysis of specific eye movement parameters.
3Ease of operation
If traditional observational tests are used, then the test procedure is simple to perform, but the accuracy and consistency of attention assessment deteriorate
Solution Approach 1:
The system provides real-time feedback through attention maps that visually display where the subject is looking and for how long. This feedback mechanism allows operators to monitor attention patterns during the test and ensures consistent measurement criteria are applied throughout the assessment, improving both accuracy and operator confidence without complicating the procedure.
Data Source
AI summary
Disclosed herein is a method for supporting an attention test based on an attention map and an attention movement map. The method includes generating a score distribution for each segment area of frames satisfying preset conditions, among frames of video content (video) that is produced in advance so as to be suitable for the purpose of a test, generating an attention map corresponding to the frames based on the distribution of the gaze point of a subject, generating an attention movement map corresponding to the frames based on information about movement of the gaze point of the subject, and calculating the attention of the subject using the score distribution for each segment area, the attention map, and the attention movement map.