This project, VIGMA, is designed to visualize and analyze gait data, providing a comprehensive tool for understanding mobility and balance in various patient populations. VIGMA integrates data processing, analysis, and visualization within a user-friendly framework, making it accessible to researchers, clinicians, and practitioners. The system supports the exploration of disease progression, comparisons across multiple patient groups, and offers advanced visual analytics capabilities. Our goal is to facilitate deeper insights into gait disorders and aid in the development of tailored treatment plans and rehabilitation strategies. VIGMA is open-source and available for use and contribution at github.com/komar41/VIGMA.

This guide provides detailed steps to install and set up the VIGMA system, including software installations, project setup, and running the application.

• Follow this guide to install Anaconda.
• Follow this link to install Node. Prebuilt binaries/installer is the easiest way to perform installation.

Download a zipped version of VIGMA from here. Check the image below for reference on how to download the zip file from the link.

Unzip the folder. Now, open a terminal inside the VIGMA folder.

• Run the following commands in the terminal to install the necessary dependencies for the VIGMA Python library (Ignore the lines with hash (#). Those are comments explaining each command):

  # Navigate to the notebooks directory
  cd notebooks
  
  # Create a new conda environment
  conda create --prefix ./envs
  conda config --set env_prompt '({name})'
  
  # Activate the conda environment
  conda activate ./envs
  
  # Install necessary Python libraries
  conda install numpy pandas scipy scikit-learn matplotlib ipykernel fuzzywuzzy plotly notebook
  
  # Install additional Python packages via pip
  pip install c3d
  pip install --upgrade nbformat
  

• Now, run the following commands in the terminal to install the necessary dependencies for the server side setup.

  # Navigate to the backend directory
  cd ../backend
  
  # Create a new conda environment
  conda create --prefix ./envs
  conda config --set env_prompt '({name})'
  
  # Activate the conda environment
  conda activate ./envs
  
  # Install necessary Python libraries
  conda install flask flask_cors pandas numpy scipy scikit-learn

• Finally, run the following command in the terminal to install the necessary dependencies for the client side setup.

  # Navigate to the frontend directory
  cd ../frontend
  
  # Install the necessary client side dependencies.
  npm install --legacy-peer-deps

• Open a terminal inside the VIGMA folder. Run the following commands in the terminal:

  # Navigate to the backend directory
  cd backend
  
  # Activate the conda environment
  conda activate ./envs
  
  # Start the server
  python server.py

• Open another terminal inside the VIGMA folder. Run the following commands in the terminal:

  # Navigate to the frontend directory
  cd frontend
  
  # Start the web application
  npm start

  This will open a tab in your browser and you're all set to use VIGMA visual analytics (VA) system. We added some mock data inside the "vigma/backend/data" folder to illustrate the usage of the system. You should follow the same hierarchy and naming conventions for data storage displayed in the image below:

  

Follow this link to learn how to use the VIGMA python API.