---
license: cc-by-nd-4.0
language:
- en
tags:
- emg
- bio-signals
- foundation-model
base_model:
- PulpBio/TinyMyo
---

# TinyMyo: Tiny Foundation Model for EMG Signal Processing

<div align="center">
  <img src="https://raw.githubusercontent.com/MatteoFasulo/BioFoundation/refs/heads/TinyMyo/docs/model/logo/TinyMyo_logo.png" alt="TinyMyo Logo" width="400" />
</div>

<p align="center">
  <a href="https://github.com/pulp-bio/BioFoundation"><img src ="https://img.shields.io/github/stars/pulp-bio/BioFoundation?color=ccf" alt="Github"></a>
  <a href="https://creativecommons.org/licenses/by-nd/4.0/"><img src="https://img.shields.io/badge/License-CC_BY--ND_4.0-lightgrey.svg" alt="License"></a>
  <a href="https://arxiv.org/abs/2512.15729"><img src="https://img.shields.io/badge/arXiv-2512.15729-b31b1b.svg" alt="Paper"></a>
</p>

## 📖 Overview
**TinyMyo** is a lightweight, Transformer-based foundation model designed specifically for surface electromyography (sEMG) signal processing. Unlike large-scale models, the TinyMyo family (including the 3.6M parameter base model and the ultra-compact 1.9M parameter **TinyissimoMyo**) is purpose-built for **ultra-low-power edge deployment**. It enables real-time motor intent decoding, neuromuscular assessment, and human-machine interaction directly on microcontrollers like the GAP9.

## 🚀 Key Highlights
*   **Generalist Foundation:** Pre-trained on a massive, heterogeneous corpus of >480 GB of EMG data (NinaPro DB6/7, EMG2Pose) using self-supervised masked reconstruction.
*   **Edge-Ready:** The first EMG foundation model demonstrated on an ultra-low-power MCU (GAP9), achieving sub-100ms inference for real-time applications.
*   **Highly Efficient:** Just 3.6M parameters (1.9M for TinyissimoMyo), ensuring low latency and high energy efficiency (~45 mJ per inference).
*   **Versatile:** Achieves state-of-the-art (SoA) performance across hand gesture classification, kinematic regression, and speech processing.

## 🧠 Model Architecture
*   **Core:** 8-layer bidirectional Transformer encoder (4-layer for TinyissimoMyo).
*   **Embeddings:** 192-dimensional latent space with 3 attention heads.
*   **Tokenization:** Channel-independent patching (20 samples per patch) utilizing Rotary Position Embeddings (RoPE) to preserve temporal alignment across channels without spurious cross-channel ordering.
*   **Deployment:** Optimized via offline liveness analysis, multi-level memory tiling, and INT8 fixed-point quantization for resource-constrained hardware execution.

## 📊 Performance Benchmarks

| Task | Dataset | Metric | TinyMyo Result |
| :--- | :--- | :--- | :--- |
| **Gesture Classification** | NinaPro DB5 | Accuracy | **87.98%** |
| **Gesture Classification** | EPN-612 | Accuracy | **96.57%** |
| **Gesture Classification** | UCI EMG | Accuracy | **97.10%** |
| **Gesture Classification** | Generic Neuromotor Interface | CLER | **0.142** |
| **Kinematic Regression** | NinaPro DB8 | MAE | **8.8°** |
| **Speech Synthesis** | Gaddy | WER | **33.54%** |
| **Speech Recognition** | Gaddy | WER | **33.95%** |

## ⚡ Deployment (GAP9 MCU)
TinyMyo bridges the gap between high-performance deep learning and stringent wearable constraints. We provide two variants to balance the accuracy-latency trade-off:

### TinyMyo (3.6M Parameters)
*   **Inference Time (5s window):** 0.785 s
*   **Energy Consumption:** 44.91 mJ
*   **Power Envelope:** 57.18 mW

### TinyissimoMyo (1.9M Parameters)
*   **Inference Time (5s window):** 0.496 s
*   **Inference Time (1s window):** **0.089 s** *(Sub-100ms regime, ideal for real-time prosthetic control)*

## 🛠️ Getting Started
TinyMyo is part of the[BioFoundation](https://github.com/pulp-bio/BioFoundation) ecosystem.

### Prerequisites
Install the required dependencies from the [BioFoundation repository](https://github.com/pulp-bio/BioFoundation).

### Loading & Fine-tuning
You can easily fine-tune the pre-trained weights for your specific task:

```bash
python run_train.py +experiment=TinyMyo_finetune pretrained_safetensors_path={*.safetensors}
```

## 📜 License & Citation
This model is licensed under **CC BY-ND 4.0**. If you find TinyMyo useful in your research, please cite our paper:

```bibtex
@misc{fasulo2026tinymyotinyfoundationmodel,
      title={TinyMyo: a Tiny Foundation Model for Flexible EMG Signal Processing at the Edge},
      author={Matteo Fasulo and Giusy Spacone and Thorir Mar Ingolfsson and Yawei Li and Luca Benini and Andrea Cossettini},
      year={2026},
      eprint={2512.15729},
      archivePrefix={arXiv},
      primaryClass={eess.SP}
}
```