Context-Aware UI Adaptation for Zero-UI and Spatial Environments

This repository hosts the computational layouts, gaze-telemetry loggers, and context-aware adaptation logic for the Zero-UI Dynamic Interaction Framework. As computing shifts away from physical screens toward Augmented Reality (AR) glasses and smart contact lenses, static application layouts become obsolete. This project engineers a dynamic layout optimization model that reads a user's real-time environmental context, physical movement vectors, and eye-gaze tracking telemetry to organically adapt, resize, and project floating interface controls safely and privately within a 3D spatial field.

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📌 Research Vision & Core Concept

In a screenless ecosystem, user interfaces must respect both physical geometry and social boundaries. A standard button shouldn't block a user's view while they are walking down a crowded street, nor should confidential text float openly in a public train. This project introduces a three-layer adaptive engine:

1. Spatial Geometry Mapping: Using computer vision to detect physical constraints (walls, tables, moving obstacles) so interface modules never clip into real-world geometry.
2. Cognitive Gaze Telemetry: Predicting user intent by analyzing continuous dwell-time and saccadic eye movements to highlight or hide UI components intuitively.
3. Context & Privacy Adaptation: Shifting layout transparency, sizing, and position depending on behavioral states (e.g., automatically shrinking text fields or applying visual masks when high-density public crowds are detected).

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🛠️ Key Features & Methodology

1. Dynamic Spatial Anchoring Engine: Logic workflows calculating ideal placement coordinates for floating virtual elements using environment occlusion algorithms.
2. Gaze-Glint Intent Predictor: Deep learning models mapping rapid eye movements to specific layout component scaling behaviors.
3. Crowd-Responsive Privacy Filters: Automated heuristic filters that modify contrast ratios and rendering priority based on surrounding pedestrian proximity counts.
4. HCI Interaction Telemetry Suite: Real-time logging metrics measuring user task-completion times, error rates, and cognitive drift across variable environmental scenarios.

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📂 Repository Structure

├── src/
│   ├── gaze_telemetry/     # Eye-tracking processing, calibration, and blink filtering
│   ├── spatial_mapping/    # Scene parsing, obstacle detection, and plane tracking anchors
│   ├── adaptive_layout/    # Dynamic layout solvers, boundary optimization, and sizing nodes
│   └── privacy_filters/    # Contrast shift algorithms and real-time crowd masking rules
├── assets/                 # Lightweight UI design configurations and component mockups
├── simulations/            # Mock situational data profiles (Walking, Public Transit, Desk Work)
├── notebooks/              # Saccade heatmaps, layout response curves, and performance graphs
├── Literature_Review/      # Team research matrices and BibTeX reference files
└── README.md