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Valquiria Space Analog Physiological Data Analysis Suite

Version 2.0.1

Author: Dr. Diego Malpica MD - Aerospace Medicine Specialist
Organization: Colombian Aerospace Force (FAC) / DIMAE
Project: Valquiria Crew Space Simulation - Physiological Research Platform

🚨 IMPORTANT DISCLAIMER

This is an ongoing research project developed for scientific and educational purposes only.

⚠️ NOT FOR OPERATIONAL DEPLOYMENT ⚠️

  • This software is NOT approved for military operations
  • This software is NOT approved for clinical diagnosis or treatment
  • This software is NOT approved for operational crew health monitoring
  • Use only for research, training, and educational purposes

For any operational or clinical applications, please consult with certified medical professionals and use validated, regulatory-approved systems.

Project Overview

The Valquiria Space Analog Physiological Data Analysis Suite is a comprehensive research platform designed to analyze physiological data collected during space analog simulations. The suite combines two powerful components:

  1. Hexoskin WAV File Analyzer - Complete physiological data processing and analysis
  2. Enhanced HRV Analysis System - Advanced heart rate variability analysis with machine learning

This platform was developed to support the Valquiria Space Analog Simulation research program, studying physiological adaptations and crew health monitoring in simulated space environments.

🌟 Key Features

Hexoskin WAV File Analyzer

  • Multi-format Data Loading: Load and decode Hexoskin WAV files containing ECG, respiration, and other physiological signals
  • Advanced Signal Processing: Automatic artifact detection, filtering, and signal quality assessment
  • Comprehensive Statistics: 15+ statistical tests including normality, parametric/non-parametric comparisons
  • Multi-dataset Analysis: Compare up to 15 datasets simultaneously with post-hoc analysis
  • Interactive Visualization: Real-time plotting with multiple time units and view controls
  • Export Capabilities: Save processed data, statistical results, and high-quality plots
  • Dual Interface: Both GUI and command-line interfaces available

Enhanced HRV Analysis System

  • Complete HRV Analysis: Time domain, frequency domain, and nonlinear metrics
  • Autonomic Nervous System Assessment: Advanced parasympathetic, sympathetic, and ANS balance analysis
  • Machine Learning Integration: Clustering for autonomic phenotyping and forecasting for adaptation prediction
  • Advanced Statistics: GAM trend analysis, mixed-effects modeling, bootstrap confidence intervals
  • Interactive Dashboards: Real-time visualization with Plotly-based interactive plots
  • Enterprise Performance: Intelligent caching, async processing, and database optimization
  • Research Analytics: Comprehensive statistical reporting and data quality assessment
  • 🆕 Mission Phases Boxplots: Compare physiological adaptation across Early, Mid, and Late mission phases

🏗️ Project Structure

Valquiria-Data-Analysis/
├── 📁 docs/                           # Documentation (all markdown files)
├── 📁 working_folder/                 # Main analysis workspace
│   ├── 📁 enhanced_hrv_analysis/      # Advanced HRV Analysis System
│   │   ├── 📁 core/                   # Core processing modules
│   │   ├── 📁 gui/                    # Graphical user interface
│   │   ├── 📁 ml_analysis/           # Machine learning components
│   │   ├── 📁 stats/                 # Advanced statistics
│   │   ├── 📁 visualization/         # Interactive plotting
│   │   └── 📁 tests/                 # Test suite
│   ├── 📁 Jupyter notebooks/         # Analysis notebooks
│   ├── 📁 hrv_results/              # Analysis outputs
│   └── 📁 scripts/                   # Utility scripts
├── 📁 DBs/                           # Database files (Sol data)
├── 📁 csv_joiner/                    # Data merging utilities
├── hexoskin_wav_loader.py            # Main Hexoskin analyzer
├── hexoskin_wav_example.py           # Usage examples
├── analyze_data.py                   # Data analysis scripts
├── requirements.txt                  # Python dependencies
└── setup.py                         # Installation script

🚀 Quick Start

System Requirements

  • Python: 3.8+ (tested up to 3.11)
  • Operating System: Cross-platform (Linux, macOS, Windows)
  • Memory: 8GB RAM minimum, 16GB recommended for large datasets
  • Storage: 2GB free space for installation and cache

Installation

  1. Clone the Repository
git clone <repository-url>
cd Valquiria-Data-Analysis
  1. Set Up Virtual Environment
# Create virtual environment
python -m venv venv

# Activate virtual environment
# Linux/macOS:
source venv/bin/activate
# Windows:
venv\Scripts\activate
  1. Install Dependencies
pip install -r requirements.txt
  1. Verify Installation
python test_libraries.py

Quick Usage

Hexoskin WAV Analyzer

# GUI Mode (Recommended)
python hexoskin_wav_loader.py

# Command Line Mode
python hexoskin_wav_example.py path/to/your/file.wav

Enhanced HRV Analysis

# Launch Advanced HRV Analysis GUI
cd working_folder/enhanced_hrv_analysis
python launch_hrv_analysis.py

📊 Analysis Capabilities

Physiological Data Processing

  • Signal Quality Assessment: Automatic artifact detection and signal validation
  • Multi-parameter Analysis: Heart rate, SPO2, temperature, blood pressure, respiratory rate
  • Temporal Analysis: Time-series analysis with circadian rhythm detection
  • Data Integration: Merge multiple sessions and subjects for longitudinal studies

Heart Rate Variability (HRV) Analysis

  • Time Domain: RMSSD, SDNN, pNN50, triangular index, and 15+ metrics
  • Frequency Domain: VLF, LF, HF power analysis with Welch and AR methods
  • Nonlinear Analysis: Poincaré plots, DFA, entropy measures
  • Autonomic Balance: Advanced sympathetic/parasympathetic assessment

Advanced Analytics

  • Machine Learning: Unsupervised clustering for autonomic phenotyping
  • Predictive Modeling: Time-series forecasting for adaptation prediction
  • Statistical Modeling: GAM, mixed-effects, bootstrap confidence intervals
  • Multi-subject Analysis: Population-level analysis with individual profiles

Visualization & Reporting

  • Interactive Plots: Poincaré plots, PSD analysis, time-series visualization
  • Statistical Dashboards: Real-time analytics with performance monitoring
  • Export Options: HTML reports, CSV data, high-resolution plots
  • Research Reports: Automated generation of analysis summaries
  • 🆕 Mission Phases Analysis: Individual and group boxplots comparing crew adaptation across mission timeline

🔬 Scientific Features

Research-Grade Analysis

  • Artifact Detection: Multiple algorithms (Malik, Karlsson, Kamath, IQR)
  • Quality Metrics: Comprehensive signal quality assessment
  • Statistical Validation: 15+ normality tests and comparison methods
  • Confidence Intervals: Bootstrap and parametric confidence estimation

Space Medicine Applications

  • Adaptation Tracking: Longitudinal analysis of physiological adaptation
  • Stress Assessment: Autonomic nervous system stress indicators
  • Crew Monitoring: Individual and group health status analysis
  • Mission Planning: Predictive modeling for mission duration effects

🆕 Mission Phases Boxplots Analysis

NEW FEATURE: Temporal Mission Analysis

The Enhanced HRV Analysis System now includes mission phases boxplot analysis for comprehensive crew adaptation assessment:

Features:

  • Three Mission Phases: Automatically divides mission timeline into Early, Mid, and Late periods based on SOL sessions
  • Individual Crew Analysis: Compare each crew member's physiological adaptation patterns across mission phases
  • Group Population Analysis: Analyze crew-wide temporal trends and phase differences
  • Statistical Testing: Kruskal-Wallis H-test for comparing phases with p-value annotations
  • Effect Size Calculation: Eta-squared (η²) for practical significance assessment

Analysis Types:

  • Individual Boxplots: Side-by-side comparison of each crew member across all three mission phases
  • Group Boxplots: Population-level analysis comparing all crew members by phase
  • Comprehensive Reports: Combined analysis with statistical summaries and interpretations

Integration:

  • Seamless Workflow: Integrated into existing Enhanced HRV Analysis GUI
  • Real HRV Data: Uses computed SDNN, RMSSD, LF/HF ratios, and other HRV metrics
  • Publication Ready: Professional visualizations with statistical annotations
  • Export Capabilities: High-resolution plots and detailed text reports

Usage:

# Launch Enhanced HRV Analysis
cd src/hrv_analysis/enhanced_hrv_analysis
python launch_hrv_analysis.py

# 1. Run HRV Analysis for all subjects
# 2. Go to "Visualizations" tab
# 3. Look for green "Mission Phases" buttons:
#    • Mission Phases - Individual
#    • Mission Phases - Group  
#    • Mission Phases - Report

Output Location: All plots and reports saved to plots_output/ folder

💻 Technical Specifications

Performance Features

  • Intelligent Caching: LRU caching with compression (2-10x speed improvement)
  • Async Processing: Non-blocking analysis with timeout protection
  • Memory Management: Adaptive memory limits and garbage collection
  • Database Optimization: Connection pooling and query optimization

Data Handling

  • Large Datasets: Chunked processing for millions of records
  • Multiple Formats: CSV, SQLite, WAV files with auto-detection
  • Data Validation: Multi-stage quality assessment and cleaning
  • Export Options: JSON, CSV, HTML with customizable formats

📚 Documentation

All project documentation is organized in the docs/ folder:

  • docs/User_Manual.md - Complete user guide
  • docs/API_Documentation.md - Developer reference
  • docs/Scientific_Methods.md - Analysis methodologies
  • docs/Installation_Guide.md - Detailed setup instructions

🧪 Testing & Validation

The project includes comprehensive test suites:

# Run all tests
cd working_folder/enhanced_hrv_analysis/tests
python run_all_tests.py

# Run specific component tests
python test_core_functionality.py
python test_advanced_statistics.py
python test_ml_analysis.py

🤝 Contributors & Acknowledgments

Special Thanks

Making This Mission Possible:

  • Valquiria Crew - The brave participants who made this research possible through their dedication during the space analog simulation missions
  • Women AeroSTEAM - Educational partnership and support for advancing women in aerospace science, technology, engineering, arts, and mathematics
  • Centro de Telemedicina de Colombia - Technical collaboration, medical expertise, and telemedicine infrastructure support

Research Collaboration:

  • Valquiria Space Analog Simulation Team - Mission planning, data collection, and scientific methodology
  • Colombian Aerospace Force (FAC) - Mission support and aerospace medicine expertise
  • DIMAE (Aerospace Medicine Division) - Clinical oversight and physiological monitoring protocols

Technical Contributors

  • Advanced HRV Analysis Architecture
  • Statistical Methods Implementation
  • Machine Learning Integration
  • Performance Optimization

This research would not have been possible without the courage and commitment of the Valquiria Crew members who participated in the space analog simulation missions, pushing the boundaries of human space exploration research.

📚 How to Cite This Project

If you use this software in your research or publications, please cite it using the following APA format:

APA Citation

Software Citation:

Malpica, D. (2024). Valquiria Space Analog Physiological Data Analysis Suite (Version 2.0.1) [Computer software]. Colombian Aerospace Force (FAC), Aerospace Medicine Division (DIMAE). https://github.com/strikerdlm/hexoskin-wav-analyzer

Research Program Citation:

Malpica, D. (2024). Valquiria Crew Space Simulation: Physiological Research Platform for space analog studies. Colombian Aerospace Force (FAC), Aerospace Medicine Division (DIMAE).

Sample In-Text Citation

The physiological data analysis was conducted using the Valquiria Space Analog Physiological Data Analysis Suite (Malpica, 2024), which provides comprehensive heart rate variability analysis and autonomic nervous system assessment capabilities for space analog research.

BibTeX Entry

For LaTeX users:

@software{malpica2024valquiria,
  author = {Malpica, Diego},
  title = {Valquiria Space Analog Physiological Data Analysis Suite},
  version = {2.0.1},
  year = {2024},
  organization = {Colombian Aerospace Force (FAC), Aerospace Medicine Division (DIMAE)},
  url = {https://github.com/strikerdlm/hexoskin-wav-analyzer},
  note = {Physiological Research Platform for space analog studies}
}

Acknowledgment in Publications

When publishing research that uses this software, please also acknowledge:

  • The Valquiria Crew participants for their contribution to space analog research
  • The Colombian Aerospace Force (FAC) and DIMAE for supporting this research
  • Any specific analysis methods or features used from the software suite

📄 License & Usage

This project is provided as open-source software for research and educational purposes only.

Permitted Uses:

✅ Academic research and publications
✅ Educational training and demonstrations
✅ Method development and validation
✅ Non-commercial scientific collaboration

Prohibited Uses:

❌ Military operational deployment
❌ Clinical diagnosis or treatment
❌ Commercial health monitoring services
❌ Safety-critical applications

📞 Contact & Support

Primary Contact:
Dr. Diego Malpica MD
Aerospace Medicine Specialist
Colombian Aerospace Force (FAC)
Email: dlmalpicah@unal.edu.co

Project Information:
For questions about the Valquiria Space Analog Simulation or this software platform, please contact the development team through the official channels.

🔄 Version History

Version 2.0.0 (Current)

  • Enhanced HRV Analysis System with ML capabilities
  • Advanced statistical methods and GAM analysis
  • Interactive visualization dashboard
  • Performance optimization with caching
  • Comprehensive test suite
  • 🆕 Mission Phases Boxplots: Temporal analysis comparing crew adaptation across Early, Mid, Late mission phases

Version 1.0.0

  • Hexoskin WAV File Analyzer
  • Basic statistical analysis
  • GUI and command-line interfaces
  • Multi-dataset comparison

⭐ If this software contributes to your research, please cite appropriately and acknowledge the Valquiria Space Analog Simulation project.

🔬 Developed for advancing our understanding of human physiological adaptation in extreme environments.

🧭 Start Here: No Experience Required (Windows/macOS/Linux)

If you have never used Git, Python, or pip before, follow these simple steps. You will download a ZIP file, install Python, and run the program with a few copy‑paste commands.

1) Download the program (no Git needed)

  • Go to the project page: GitHub project page
  • Click the green "Code" button, then click "Download ZIP"
  • When the download finishes, open your Downloads folder and unzip the file
    • The unzipped folder will be named something like hexoskin-wav-analyzer-main (or similar)

2) Install Python (one time)

  • Windows:
    • Visit python.org downloads and download the latest Python 3 for Windows (64‑bit)
    • Run the installer and check "Add Python to PATH" on the first screen, then click Install
    • After installation, restart Command Prompt if it was open
  • macOS:
    • Visit python.org downloads and download the macOS installer (Universal2)
    • Open the .pkg file and follow the prompts
  • Linux (Ubuntu/Debian):
    • Open Terminal and run:
sudo apt update && sudo apt install -y python3 python3-venv python3-pip python3-tk
  • Linux (Fedora/RHEL/CentOS):
sudo dnf install -y python3 python3-pip python3-tkinter

3) Open a terminal and go to the project folder

  • Windows:
    • Press the Windows key, type "Command Prompt", and open it
    • Type cd then a space, paste the folder path (or drag the folder into the window), then press Enter
    • Example:
cd %HOMEPATH%\Downloads\hexoskin-wav-analyzer-main
  • macOS:
    • Open the Terminal app (Applications → Utilities → Terminal)
cd ~/Downloads/hexoskin-wav-analyzer-main
  • Linux:
cd ~/Downloads/hexoskin-wav-analyzer-main

Tip: Your folder name may differ. Use your actual unzipped folder name.

4) Create a safe Python environment (virtual environment)

  • Windows (Command Prompt):
py -3 -m venv venv
venv\Scripts\activate

If py doesn’t work, try:

python -m venv venv
venv\Scripts\activate
  • macOS/Linux:
python3 -m venv venv
source venv/bin/activate

You’ll know it worked if your prompt starts with (venv).

5) Install the required packages

  • Windows:
python -m pip install --upgrade pip
python -m pip install -r requirements.txt
  • macOS/Linux:
python3 -m pip install --upgrade pip
python3 -m pip install -r requirements.txt

If you see an error about tkinter or tk, install it (Linux):

sudo apt install -y python3-tk     # Ubuntu/Debian
sudo dnf install -y python3-tkinter  # Fedora/RHEL

6) Run the program (easy mode)

Use the simple launcher main.py so you don’t need to change folders.

  • Check your setup:
    • Windows:
python main.py info
  • macOS/Linux:
python3 main.py info
  • Start the Hexoskin WAV Analyzer (GUI):
    • Windows:
python main.py hexoskin
  • macOS/Linux:
python3 main.py hexoskin
  • Start the Enhanced HRV Analysis system (GUI):
    • Windows:
python main.py hrv
  • macOS/Linux:
python3 main.py hrv

Notes:

  • If python doesn’t work on Windows, try py instead.
  • On macOS/Linux, if python3 is not found, install Python from python.org downloads.

7) Next steps

  • Inside the app, use the GUI to open your files (e.g., Hexoskin .wav).
  • When you’re done, you can close the app and type deactivate in the terminal to exit the environment.

Later: How to run it again (next time)

  1. Open your terminal/Command Prompt and go to the same project folder
  2. Activate the environment:
    • Windows: venv\Scripts\activate
    • macOS/Linux: source venv/bin/activate
  3. Start the app:
    • Hexoskin: python main.py hexoskin (Windows) or python3 main.py hexoskin (macOS/Linux)
    • HRV: python main.py hrv (Windows) or python3 main.py hrv (macOS/Linux)

Troubleshooting (quick help)

  • “python is not recognized” on Windows: Try py instead of python.
  • “pip not found”: Use python -m pip ... (Windows) or python3 -m pip ... (macOS/Linux).
  • “tkinter/tk error” on Linux: Install python3-tk as shown above, then retry.
  • Permission errors on Windows PowerShell: Use Command Prompt, or run PowerShell as Administrator.

That’s it—you’re up and running without needing Git or prior Python experience.

About

An advanced application for analyzing physiological data from Hexoskin / Astroskin WAV, CSV and HRV metrics

strikerdlm.github.io/hexoskin-wav-analyzer/

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medicine aerospace hrv physiology

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