Welcome to

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.

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…