Eye Tracking Cognitive Level Estimation via Machine Learning
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Solution Overview
Problem
Existing methods for analyzing eye tracking data to determine cognitive and emotional levels related to visual information consumption are inefficient, unreliable, and costly, lacking precision and scalability for mass market applications.
Innovation Solution
A method and system using machine learning models, specifically deep neural networks, to analyze eye movements and facial expressions, recording bio-electrical signals and biometrical measurements to estimate cognitive and emotional responses by training on patterns of user reactions to various visual stimuli, allowing for the determination of comprehension and consumption depth of visual information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional eye tracking data analysis methods are used, then basic gaze tracking is achieved, but measurement precision and reliability of cognitive level estimation are insufficient
Solution Approach 1:
The patent combines multiple data sources including eye tracking data, facial expression data, and bio-electrical signals into a unified analysis system. This integration of multiple measurement modalities enhances both the precision and reliability of cognitive level estimation by cross-validating signals from different physiological sources.
Solution Approach 2:
The patent introduces machine learning models as intermediary components that process raw physiological signals and transform them into meaningful cognitive level estimates. These models act as mediators between the physical measurements and the interpreted cognitive states, improving measurement precision through pattern recognition.
2Measurement precision
If complex analysis systems are implemented to improve measurement precision, then estimation accuracy improves, but device complexity and cost increase
Solution Approach 1:
The system employs machine learning models that automatically process and interpret physiological signals without requiring manual analysis. The models self-adjust and optimize their parameters based on training data, reducing the need for complex manual configuration and lowering operational complexity despite the advanced analytics performed.
Solution Approach 2:
The patent replaces manual or rule-based analysis systems with automated machine learning models. This substitution eliminates the need for complex manual processing workflows while achieving superior measurement precision through algorithmic pattern recognition and automated feature extraction.
3Productivity
If traditional eye tracking methods are used, then basic functionality is maintained, but productivity and scalability for mass market applications are limited
Solution Approach 1:
The system performs preliminary processing of physiological signals by extracting relevant features and preprocessing data before main analysis. Machine learning models are pre-trained on large datasets to enable rapid inference during actual use, significantly reducing processing time and improving scalability for mass market applications.
Solution Approach 2:
The patent optimizes processing parameters including sampling rates, feature extraction windows, and model complexity to balance accuracy with processing speed. By dynamically adjusting these parameters based on application requirements, the system achieves high productivity while maintaining acceptable measurement precision.
Data Source
AI summary
Method of analyzing eye tracking data for estimating user's cognitive and emotional level of consumption of visual information. A training machine learning model is trained using a data set containing gaze information of known training users, their known cognitive levels and their EEG signal measurements. A calibrating machine learning model is trained using a data set of calibrating visual information displayed to a user, calibrating gaze tracks of that user, calibrating actions data of that user, and calibrating session data related to the session environment. The device displays to that user a target visual information and records target eye tracking data of that user in response to consuming the target information. The recorded target eye tracking data is calibrated via the calibrating machine learning model. The calibrated target eye tracking data is fed into the training machine learning model, which estimates the cognitive levels of consumption of the target visual information of that user.