MyoVerse 1.1.2

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[!TIP] Dive deeper into our features and usage with the official documentation.

MyoVerse - The AI toolkit for myocontrol research

What is MyoVerse?

MyoVerse is your cutting-edge research companion for unlocking the secrets hidden within biomechanical data! It's specifically designed for exploring the complex interplay between electromyography (EMG) signals, kinematics (movement), and kinetics (forces).

Leveraging the power of PyTorch and PyTorch Lightning, MyoVerse provides a comprehensive suite of tools, including:

• Data loaders and preprocessing filters tailored for biomechanical signals.
• Peer-reviewed AI models and components for analysis and prediction tasks.
• Essential utilities to streamline the research workflow.

Whether you're predicting movement from muscle activity, analyzing forces during motion, or developing novel AI approaches for biomechanical challenges, MyoVerse aims to accelerate your research journey.

[!IMPORTANT]
MyoVerse is built for research. While powerful, it's evolving and may not have the same level of stability as foundational libraries like NumPy. We appreciate your understanding and contributions!

Installation

For Users (Using MyoVerse in your project)

1. Install MyoVerse: Use pip to install the package directly from GitHub (replace main with a specific tag/branch if needed):

   pip install git+https://github.com/NsquaredLab/MyoVerse.git
   

2. Install PyTorch with GPU: After installing MyoVerse, install PyTorch.
   • For Windows: Visit the PyTorch installation guide and select the appropriate options (Stable, Windows, Pip, Python, your CUDA version). Copy the generated command, which will look something like this (example for CUDA 12.6):

     pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126 --upgrade
     

   • For Linux: Pytorch GPU is already installed by MyoVerse.

For Developers (Contributing to MyoVerse)

1. Clone the Repository:

   git clone https://github.com/NsquaredLab/MyoVerse.git # Replace with your actual repo URL if different
   cd MyoVerse
   

2. Install uv: If you don't have it yet, install uv. Follow the instructions on the uv GitHub page.
3. Set up Virtual Environment & Install Dependencies: Use uv to create and sync your virtual environment with the project's dependencies.

   uv sync --group dev
   

4. Install PyTorch with GPU: After syncing other dependencies, install PyTorch.
   • For Windows: Visit the PyTorch installation guide and select the appropriate options (Stable, Windows, Pip, Python, your CUDA version). Use uv to run the install command, for example:

     # Example for CUDA 12.6 - Get the correct command from PyTorch website!
     uv pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126 --upgrade
     

   • For Linux: Pytorch GPU is already installed by MyoVerse.

What is what?

This project uses the following structure:

• myoverse: This is the main package. It contains:
  • datasets: Contains data loaders, dataset creators, and a wide array of filters to preprocess your biomechanical data (e.g., EMG, kinematics).
  • models: Contains all AI models and their components, ready for training and evaluation.
  • utils: Various utilities to support data handling, model training, and analysis.
• docs: Contains the source files for the documentation.
• examples: Contains practical examples demonstrating how to use the package, including tutorials (01_tutorials) and specific use cases like applying filters (02_filters).
• tests: Contains tests to ensure package integrity and correctness.

What papers/preprints use this package?

Journal / Preprint Server	DOI
IEEE Transactions on Biomedical Engineering	10.1109/TBME.2024.3432800
International Journal of Computer Science and Information Security	10.33965/ijcsis_2024190101
medRxiv	10.1101/2024.05.28.24307964
Journal of Neural Engineering	10.1088/1741-2552/ad3498
IEEE Transactions on Neural Systems and Rehabilitation Engineering	10.1109/TNSRE.2023.3295060
2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)	10.1109/EMBC48229.2022.9870937