Sundown

This project develops a VR-based research platform focused on immersive 360° video experiences and synchronized behavioral and physiological data collection. The system emphasizes stable VR playback, eye tracking, user controls, and UI consistency, followed by integration of wearable sensor data from a Samsung Galaxy Watch. The repository serves as a progress log and data archive supporting ongoing research and future analysis
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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Roadmap
5. Contributing
6. License
7. Contact
8. Acknowledgments

About The Project

Sundown is a virtual reality–based research platform for collecting synchronized physiological and behavioral signals during immersive VR experiences. The system integrates a standalone VR headset, a smartwatch, and a backend server to support controlled data capture, structured logging, and future AI/LLM-based analysis.

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Built With

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Getting Started

This section explains how to install and run the Sundown VR application APK on a standalone VR headset.

Prerequisites

Before installing the APK, make sure you have:.

• Standalone VR headset (HTC Vive Focus Vision)
• USB-C data cable
• Windows
• Android SDK Platform Tools (ADB) installed Anroid SDK Link

Installation

1. Download the APK

2. Enable Developer Mode on the headset

   • On the headset, open Settings → About
   • Tap Build Number 7 times to enable Developer Options
   • Go back and enable:
     • Developer Mode
     • *USB Debugging *Allow installation from unknown sources

3. Connect the headset to the computer

   • Use a USB-C data cable
   • Put on the headset and approve the USB debugging prompt

4. Verify ADB connection

   adb devices

   -You should see the headset listed.

5. Install the APK

   adb install -r Sundown.apk   

6. Launch Application

   • From the headset’s app library or

     adb shell monkey -p com.xdilab.sundown 1

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Usage

For more examples, please refer to the Documentation

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Roadmap

• Phase 0 – Initial Concept & Scope Definition
  • Defined goal of a calming VR environment paired with physiological data collection
  • Identified VR as the primary interaction surface
  • Chose wearable + VR headset as the core multi-modal setup
  • Scoped project as research infrastructure
• Phase 1 – Core VR Application & Playback Pipeline
  • Built initial Unity VR application
  • Implemented immersive 360° video playback
  • Integrated local video playback and playlist control
  • Established RenderTexture-based video rendering
  • Designed minimal, low-cognitive-load VR UI
  • Added session start/stop controls
  • Stabilized immersive playback pipeline
  • Integrated REDACTED YouTube 360 playback for content sourcing
• Phase 2 – Eye Tracking, Controls, and VR Stability
  • Integrated eye tracking into the VR application
  • Logged gaze direction, blink events, fixation, and head pose
  • Stabilized eye-tracking sampling and session timing
  • Implemented and refined VR input controls
  • Stabilized user movement and navigation behavior
  • Reduced unintended motion and visual discomfort
  • Refined UI flow and interaction responsiveness
  • Ensured consistent session start/stop behavior
• Phase 3 – Galaxy Watch Integration & Physiological Data
  • Integrated Samsung Galaxy Watch 7 with the VR system
  • Implemented BLE-based data streaming
  • Captured heart rate and inter-beat interval (IBI)
  • Logged raw accelerometer XYZ data
  • Synchronized watch data with VR session lifecycle
  • Integrated stress-related metrics from watch APIs
  • Logged physiological data to timestamped CSV files
• Phase 4 – Data Synchronization & Repository Structuring
  • Unified session IDs across VR and watch data
  • Improved BLE reliability and reconnection handling
  • Refined CSV schema and logging consistency
  • Uploaded screenshots, logs, notes, and Unity scripts
• Phase 5 – Current Focus (Ongoing)
  • Improve long-session stability across all systems
  • Validate timestamp alignment across eye tracking, movement, and watch data
  • Reduce data loss during intermittent connectivity
  • Prepare data for downstream analysis
    

See the open issues for a full list of proposed features (and known issues).

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License

© 2025 eXplainable Deep Intelligence Lab
Developed under Hamidzera Moradi.

Primary author: Kirsten Hefney.

This project is licensed under the GNU General Public License v3.0.
See the LICENSE file for full terms and conditions.

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Contact

Kirsten Hefney - khefney@aggies.ncat.edu

Project Link: https://github.com/xdilab/VR_Player

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Acknowledgments

This project was developed at eXplainable Deep Intelligence Lab under the supervision of Dr. Hamidzera Moradi.

The author would like to acknowledge the early contributors to this project:

• Ehsan Alam
• Kimora Mohan
• Elias Greene

The author would like to thank the lab leadership and members for guidance, technical discussions, and feedback throughout development.

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