Eye Gaze Metrics for Real-Time Psychophysiological State Detection
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Solution Overview
Problem
Existing driver monitoring systems face challenges in accurately and non-intrusively assessing psychophysiological states such as fatigue, drowsiness, and stress due to the computational complexity of interpreting eye gaze data in real-time, often relying on intrusive sensors that are uncomfortable for drivers and provide inaccurate assessments.
Innovation Solution
A method and system that captures eye gaze vectors and eyelid openness levels to derive second order eye movement metrics, transforming them into a compact, machine-readable representation for real-time prediction of psychophysiological states using machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional physiological sensors are used to monitor psychophysiological state, then measurement accuracy is improved, but device complexity and driver comfort deteriorate due to intrusive sensors
Solution Approach 1:
The patent replaces intrusive physiological sensors with an optical-based eye tracking system that uses a camera to capture eye gaze vectors and eyelid openness levels. This substitution eliminates the need for physical contact with the driver's body while still enabling psychophysiological state detection through analysis of eye movement patterns
Solution Approach 2:
The patent introduces eye gaze data as an intermediary measure that indirectly reflects psychophysiological states. Instead of directly measuring physiological parameters like heart rate or brain activity, the system uses eye movement metrics (gaze direction, fixation duration, saccade frequency) as a mediator to infer cognitive load, stress, and fatigue levels
2Speed
If eye gaze data is processed in real-time to detect psychophysiological states, then response time is improved, but computational complexity increases
Solution Approach 1:
The patent extracts only the most relevant features from eye gaze data for real-time processing. Instead of analyzing all raw eye movement data, the system selectively processes key metrics such as fixation duration, saccade frequency, and gaze dispersion, which have been identified as strong indicators of psychophysiological states. This extraction reduces computational burden while maintaining detection accuracy
Solution Approach 2:
The patent segments the eye gaze data processing into distinct analytical components: (1) capturing eye gaze vectors and eyelid openness levels, (2) calculating second-order eye movement metrics, (3) transforming metrics into machine-readable representations, and (4) predicting psychophysiological states using trained models. This segmentation enables parallel processing and optimizes computational efficiency for real-time operation
Data Source
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AI summary
Disclosed herein are methods and systems for real-time detection of psychophysiological states from eye movement data. The methods involve capturing eye gaze vectors and eyelid openness levels over time using a user-facing camera (240). A sequence of discrete eye behaviors, including saccades, fixations, blinks, and long closures, is determined (304) from the eye gaze vectors and eyelid openness levels. The sequence of discrete eye behaviors is transformed (308) into a machine readable representation using a sliding time window. A mathematical or machine learning model (214) then maps the machine readable representation of eye behaviors to one or more psychophysiological states (310). This approach provides a computationally efficient mechanism for predicting psychophysiological states by compressing gaze data into a continuous, numerical representation of eye behavioral events correlated with human psychophysiological states, including drowsiness, cognitive load, stress, and others.