Eye Tracking System for Detecting Pilot Attention Tunneling
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Solution Overview
Problem
Current methods for monitoring pilot fatigue and attention tunneling rely on self-assessment and best practices, lacking independent and objective measurement, which can lead to reduced pilot alertness and reaction times, and failure to monitor critical flight information.
Innovation Solution
An on-aircraft computer system that records and analyzes eye tracking data to identify involuntary eye movements, distinguishing active focus from attention tunneling, and alerts pilots or ground control personnel, using thresholds and user-specific profiles to trigger remedial actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If self-assessment and best practices are used to monitor pilot fatigue, then implementation is simple and requires minimal equipment, but measurement objectivity and reliability are insufficient
Solution Approach 1:
The patent replaces subjective self-assessment with objective biometric measurement using eye tracking technology. The system captures involuntary eye movements through optical sensors and processes them computationally to detect attention tunneling, substituting mechanical/optical measurement for human self-reporting.
Solution Approach 2:
The patent introduces an intermediary processing system that translates raw eye tracking data into meaningful attention state indicators. The computer system acts as a mediator between the pilot's physiological state and the monitoring objective, converting involuntary eye movements into detectable patterns that indicate attention tunneling.
2Reliability
If eye tracking data is recorded and analyzed to detect involuntary eye movements, then attention tunneling detection accuracy improves, but system complexity and computational requirements increase
Solution Approach 1:
The patent extracts specific diagnostic features from complex eye tracking data, focusing on involuntary eye movements as indicators of attention tunneling. By isolating and analyzing only the relevant movement patterns rather than processing all eye tracking information, the system achieves reliable detection with reduced computational complexity.
Solution Approach 2:
The patent transforms raw eye tracking parameters into meaningful indicators of attention state by analyzing changes in movement patterns, velocity, and amplitude. The system detects transitions from voluntary to involuntary eye movements through parameter analysis, enabling reliable attention tunneling detection.
Data Source
AI summary
An on aircraft computer system records and analyzes biometric data to identify indicia of impaired performance, such as pilot fatigue, attention tunneling, or cognitive overload. Such impairment is identified by alterations in pilot gaze or eye movement, head movement, facial parameters, eye lid position, heart rate, breathing, or brain wave patterns. Appropriate corrective action is applied based on the type of impaired performance identified, including altering a level of automation, contacting a ground dispatcher or ground pilot, or contacting a co-pilot or other crew member. Biometric data is continuously logged and correlated with data from other avionics systems to refine formulas relating biometric data to states of alertness and crew rest procedures.