Eye-Tracking Heat Maps Using Multi-Parameter Gaze Metrics
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Solution Overview
Problem
Traditional heat maps generated by eye tracking systems are limited in their ability to illustrate data beyond gaze point and gaze duration, failing to capture additional parameters that provide deeper insights into user behavior and mental workload.
Innovation Solution
An eye tracking system that captures and generates heat maps based on additional parameters such as saccade velocity, saccade start and stop positions, regressions, and mental workload, allowing for more detailed visualization of user behavior and mental state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional heat map methods are used to visualize gaze data, then the visualization is simple and easy to generate, but the ability to illustrate data beyond gaze points and gaze duration is limited
Solution Approach 1:
The patent segments the observed region into multiple sections and processes different gaze parameters (gaze points, gaze duration, fixation count, pupil diameter, saccade velocity) separately for each section. This segmentation allows comprehensive information representation while maintaining manageable processing complexity through modular computation.
Solution Approach 2:
The patent transitions from traditional 2D heat map visualization to a multi-dimensional representation by incorporating multiple gaze parameters simultaneously. Each parameter can be visualized as a separate heat map layer or combined into a composite visualization, adding informational dimensions without proportionally increasing processing complexity.
2Loss of information
If multiple gaze parameters are collected and processed to form comprehensive heat maps, then the information representation capability is improved, but the data processing complexity increases
Solution Approach 1:
The processing system divides the observed region into discrete sections and processes each gaze parameter independently for each section. This segmentation strategy reduces overall processing complexity by breaking down the complex task of multi-parameter analysis into manageable, parallelizable sub-tasks while preserving complete information representation.
Solution Approach 2:
The patent transforms multiple raw gaze parameters (pupil diameter, saccade velocity, fixation duration) into standardized normalized values that can be directly compared and visualized. This parameter transformation simplifies the processing complexity by creating a unified data structure while maintaining the completeness of original information.
3Measurement precision
If gaze data is normalized and processed with multiple parameters, then the measurement precision is improved, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent applies normalization transformations to convert raw gaze parameters into standardized metrics with consistent scales. This parameter change approach improves measurement precision by eliminating scale differences between parameters while the automated normalization process manages the detection complexity through algorithmic standardization.
Solution Approach 2:
The patent replaces manual measurement and analysis methods with automated computational processing of gaze parameters. This substitution improves measurement precision through consistent algorithmic application while reducing the difficulty of detecting and measuring multiple parameters simultaneously through automated data collection and processing systems.
Data Source
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AI summary
Disclosed herein is a method for an eye tracking system comprising at least one camera and it is configured to provide a heat map based on an observation of at least one user comprising processing data related to a first gaze metric comprising gaze point data and gaze duration data of the user, the first gaze metric being determined in relation to the region (8-8‴) comprising the stimulus (10-10d) and over a duration of time; and a second metric, the second metric comprising data different from the first gaze metric, the second metric being determined in relation to said region (8-8‴) and over a duration of time, by allocating values to the sections, so that a heatmap can be generated based on these allocated values and the sections; and mapping the processed data to the plurality of sections and generating a heat map for visually illustrating the processed data.