Bio-kinematic Eye Tracking Model for Real-Time Performance Assessment
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Solution Overview
Problem
The scarcity of annotated data for training pilot monitoring systems and the labor-intensive nature of data collection pose challenges in achieving suitable performance, with no real-time method to assess model performance, especially when bootstrapping with annotated still images for video applications.
Innovation Solution
A computer system that records eye tracking data to generate a bio-kinematic model of eye movement and pupil dynamics, continuously adjusts and weights the model in real-time, and uses this data to produce performance metrics based on deviations from physically possible movements, allowing for automated annotation and enhanced data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If annotated still images are used to bootstrap the model for video applications, then the model can be initialized with some training data, but there is no real-time way to assess performance and the data collection remains labor-intensive
Solution Approach 1:
The system implements a feedback mechanism where the bio-kinematic model continuously processes eye tracking data and generates performance metrics in real-time. The model compares predicted eye movements against actual measurements, providing immediate feedback on model accuracy and pilot performance without requiring manual annotation of each frame.
Solution Approach 2:
The system performs self-assessment by automatically evaluating its own model performance through continuous comparison of predicted versus actual eye movement data. This self-service capability eliminates the need for external manual annotation and provides real-time performance metrics, allowing the system to bootstrap and improve autonomously.
2Measurement precision
If manually annotated data is collected to train the model, then suitable performance can be achieved, but the labor-intensive nature of data collection creates a non-trivial problem
Solution Approach 1:
The system automatically generates performance metrics and model assessments without requiring manual annotation of eye tracking data. The bio-kinematic model processes raw eye tracking data in real-time, performing self-evaluation and eliminating the labor-intensive manual annotation process while maintaining high measurement precision.
Solution Approach 2:
The system accelerates the data processing and model training process by using continuous real-time feedback from the bio-kinematic model. This allows rapid iteration and model improvement without the slow process of manual data annotation, effectively accelerating the productivity of data collection and model development.
3Measurement precision
If a bio-kinematic model is created to define physically possible eye movements, then performance metrics can be generated based on deviations, but the model requires continuous adjustment and weighting in real-time
Solution Approach 1:
The bio-kinematic model is designed to be dynamic and adaptive, continuously adjusting its parameters and weighting factors in real-time based on incoming eye tracking data. This dynamic adjustment allows the model to maintain high measurement precision for performance metrics while adapting to individual pilot characteristics without requiring complex manual reconfiguration.
Solution Approach 2:
The system uses continuous feedback from real-time eye tracking data to automatically adjust and weight the bio-kinematic model parameters. This feedback-driven adaptation simplifies the complexity of model tuning by using automated real-time adjustments rather than requiring complex pre-configured models or manual intervention.
Data Source
AI summary
A computer system records eye tracking data and identifies movements in the eye tracking data to generate a model of eye movement and pupil dynamics. The model is used to produce a performance metric for the user based on deviations of the predicted output from what is physically possible as defined by the bio-kinematic model. The system continuously stores eye tracking data to enhance the bio-kinematic models. Bio-kinematic models may be generalized or specific to a particular user. The system continuously adjusts and/or weights the bio-kinematic model in real-time based on eye tracking data.