Eye Tracking Scanpath Analysis via Feature Vector Clustering
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Solution Overview
Problem
Existing eye-tracking technologies face challenges in processing and comparing large volumes of scanpath data due to noise and varying scanning patterns across users, making it difficult to correlate and analyze effectively.
Innovation Solution
A computing system generates n-dimensional feature vectors from eye-tracking data, which includes trajectory information without assumptions on fixations or regions of interest, allowing for clustering and comparison of scanpaths to identify similarities and differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If eye tracking data is collected with high temporal resolution (e.g., one data point per millisecond), then the precision of scanpath measurement is improved, but the volume of data increases significantly making processing and comparison difficult
Solution Approach 1:
The patent segments the high-resolution eye tracking data into fixation events and saccadic transitions. By identifying discrete fixation points and the transitions between them, the continuous high-volume data stream is divided into meaningful, manageable segments that capture the essential scanpath information without retaining all intermediate high-frequency data points.
Solution Approach 2:
The patent extracts key features from the raw eye tracking data, specifically identifying fixation locations, durations, and saccadic movements. This extraction process isolates the most relevant information from the voluminous raw data, creating a reduced representation that maintains measurement precision while dramatically reducing data volume for subsequent processing and comparison.
2Measurement precision
If eye tracking data includes detailed positional coordinates and timestamps for every millisecond, then the accuracy of scanpath representation is improved, but the complexity of correlating scanpaths across multiple users increases
Solution Approach 1:
The patent transforms the raw eye tracking data by changing its parameters from continuous high-frequency positional coordinates to discrete fixation events characterized by location, duration, and sequence. This parameter transformation simplifies the data structure, making it much easier to correlate scanpaths across multiple users while preserving the essential accuracy of eye movement patterns through the retained fixation characteristics.
3Loss of information
If the eye tracking system captures comprehensive eye movement data including noise and variations in scanning patterns, then the completeness of scanpath information is improved, but the difficulty of processing and identifying meaningful patterns increases
Solution Approach 1:
The patent converts the natural variations and noise in eye tracking data, which initially appear as harmful complications, into beneficial indicators of different scanning strategies and cognitive processing patterns. By analyzing these variations rather than filtering them out, the system identifies meaningful patterns in how different users interact with visual information, transforming data complexity into analytical value.
Data Source
AI summary
Described herein are various technologies pertaining to analysis of eye tracking data. A head and/or eyes of an observer who is viewing a visual stimulus is monitored, and eye tracking data that is representative of the path of the eyes of the observer over time (a scanpath) is generated. The eye tracking data is time-series data that defines the location of the focal point, or other measurable characteristics, of the eyes of the observer on the visual stimulus over time. A feature vector is constructed based upon the eye tracking data, where the feature vector is representative of the eye tracking data, and is thus representative of the scanpath. The feature vector is compared with other feature vectors to identify scanpaths that correspond to the scanpath represented by the feature vector.