The electrical signals in your muscles have a secret grammar that AI can learn just like a human language.
Muscle activity (EMG) can be treated as a physiological language, allowing AI to build foundation models for the human body. This model can generalize across different people and devices because it understands the underlying rules of how muscles fire. This breakthrough means that a prosthetic arm could work for a new user almost immediately, without the usual weeks of calibration. It treats the electrical noise of our bodies as a structured data source that can be translated into action. This technology bridges the gap between biological intent and mechanical response in a way that feels natural.
Learning Generalizable Action Representations via Pre-training AEMG
arXiv · 2605.03462
A fundamental role in decoding human motor intent and enabling intuitive human-computer interaction is played by electromyography (EMG). However, its generalization capability across subjects, devices, and tasks remains substantially limited by data heterogeneity, label scarcity, and the lack of a unified representational framework. To bridge this gap, we propose Any Electromyography (AEMG), the first large-scale, self-supervised representation learning framework for EMG. AEMG reconceptualizes n