Eye Tracking Sensors for Adaptive Notification Timing
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Solution Overview
Problem
Head-mounted devices struggle to effectively manage user notifications based on the wearer's state, as existing systems lack accurate and real-time eye data analysis to determine when the user is engaged or distracted.
Innovation Solution
The use of sensors such as cameras, photodiodes, and LIDAR units to capture eye data, including pupil size and movement, to determine the user's state, allowing for the pausing or presentation of notifications in a near-eye display, with near-infrared light sources and filters enhancing the eye-tracking capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors capture eye data continuously to determine user state, then notification delivery accuracy is improved, but device complexity and energy consumption increase
Solution Approach 1:
The system performs preliminary actions by capturing eye data in advance and analyzing it to determine user state before notification delivery. This allows the system to prepare notification strategies ahead of time based on predicted user engagement, reducing the need for complex real-time processing during actual notification delivery moments.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring eye data and adjusting notification delivery strategies based on observed user state. The feedback loop processes eye tracking information, determines engagement level, and modifies notification behavior accordingly, improving accuracy while managing complexity through iterative refinement rather than monolithic complex systems.
2Productivity
If eye tracking is performed in real-time to assess engagement, then notification relevance is improved, but processing time and computational load increase
Solution Approach 1:
The system performs preliminary analysis of eye data patterns to pre-determine user engagement state before actual notification processing is needed. By analyzing eye movement patterns and pupil responses in advance, the system establishes engagement baselines that reduce real-time processing requirements during actual notification delivery moments.
Solution Approach 2:
The system employs periodic sampling of eye data at optimized intervals rather than continuous processing. By determining appropriate sampling frequencies based on user state and notification urgency, the system achieves sufficient engagement assessment with reduced computational load and processing time compared to continuous real-time analysis.
3Speed
If notifications are delivered regardless of user state, then notification delivery speed is improved, but user experience and engagement quality deteriorate
Solution Approach 1:
The system dynamically adjusts notification delivery parameters based on real-time user state assessment. When users are detected as engaged through eye tracking, the system delays or modifies notifications to avoid disruption. When users appear disengaged, the system accelerates delivery speed. This dynamic adaptation maintains fast delivery capability while significantly improving user experience through context-aware timing.
Solution Approach 2:
The system changes notification delivery parameters such as timing, urgency level, and presentation method based on determined user engagement state. By modifying these parameters dynamically rather than using fixed delivery protocols, the system achieves both speed and user experience optimization, delivering notifications quickly when appropriate and pausing when user engagement suggests otherwise.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This solution enables head-mounted devices to accurately assess the user's engagement level, allowing for intelligent notification management, ensuring that notifications are delivered or paused based on the user's state, thereby enhancing user experience by minimizing distractions.
Implementation Method 1
a near-infrared light source and a filter configured to filter out visible light and pass the near-infrared light
Implementation Method 2
a filter configured to filter out visible light and pass the near-infrared light
Data Source
AI summary
Eye data is captured with one or more sensors of the head mounted device. The one or more sensors are configured to sense an eyebox region. User notifications are controlled based on the eye data.