.. |logo| image:: _static/myoverse_logo.png :height: 80px :align: middle Welcome to |logo| =========================== **The AI toolkit for myocontrol research** 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 for researchers and developers working with myoelectric signal analysis and AI-driven biomechanical applications. .. raw:: html
Key Features ----------- * **Data loaders** and **preprocessing filters** tailored for biomechanical signals * Peer-reviewed **AI models** and components for analysis and prediction tasks * Comprehensive visualization tools * Essential **utilities** to streamline the research workflow .. 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. Package Structure ---------------- * **myoverse**: Main package containing: * **datasets**: Data loaders, dataset creators, and preprocessing filters * **models**: AI models and components for training and evaluation * **utils**: Support for data handling, model training, and analysis * **examples**: Practical examples including tutorials and use cases Research ---------------------- MyoVerse has been used in several publications: * IEEE Transactions on Biomedical Engineering (10.1109/TBME.2024.3432800) * Journal of Neural Engineering (10.1088/1741-2552/ad3498) * IEEE Transactions on Neural Systems and Rehabilitation Engineering (10.1109/TNSRE.2023.3295060) * And more... .. raw:: html .. toctree:: :maxdepth: 2 :caption: Contents: :hidden: auto_examples/index.rst api_documentation.rst .. toctree:: :maxdepth: 1 :caption: Development: :hidden: contributing.rst Changelog <../CHANGELOG.md>