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Welcome to |logo|
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**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.
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Key Features
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* **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
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* **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
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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...
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.. toctree::
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:caption: Contents:
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auto_examples/index.rst
api_documentation.rst
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:caption: Development:
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contributing.rst
Changelog <../CHANGELOG.md>