README.md

---
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 **TinyssimoMyo**) 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.

## ⚙️ Model Configuration
*   **Base model:** 8-layer bidirectional Transformer encoder.
*   **TinyssimoMyo:** 4-layer compact variant.
*   **Embedding dimension:** 192.
*   **Attention heads:** 3.
*   **Temporal patch size:** 20 samples.
*   **Default input:** 16 channels × 1000 samples.
*   **Maximum channels:** 16.
*   **Tokenization:** Channel-independent patching with 50 temporal patches per channel and 800 tokens for the default input.
*   **Position encoding:** Rotary Position Embeddings (RoPE), with temporal positions reset for each channel.
*   **Training objective:** Self-supervised masked patch reconstruction.

## 🧠 Model Architecture
TinyMyo uses channel-independent patch embeddings followed by a bidirectional
Transformer encoder. Tokens are ordered channel-major, while RoPE positions reset
for each channel so flattening does not introduce a false temporal distance between
channels. The learned channel embedding identifies a channel slot; it does not
encode physical electrode coordinates.

For deployment, the family can be paired with offline liveness analysis,
multi-level memory tiling, and INT8 fixed-point execution. See the paper and model
card for deployment measurements.

## ⚡ Deployment (GAP9 MCU)
TinyMyo is designed for resource-constrained deployment on platforms such as the
GAP9 MCU. The repository does not treat deployment measurements as model
configuration; see the [paper](https://arxiv.org/abs/2512.15729) and the
[Hugging Face model card](https://huggingface.co/PulpBio/TinyMyo) for current results.

### TinyMyo (3.6M Parameters)
*   8-layer encoder configuration.

### TinyssimoMyo (1.9M Parameters)
*   4-layer compact configuration.

## 🛠️ 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
Fine-tune a pretrained checkpoint using the BioFoundation training entry point:

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

Available checkpoint groups include pretraining, DB5, EPN-612, UCI EMG, and DB8.
Use the matching configuration and checkpoint for each task. The released
classification labels are:

| Dataset | Input channels | Classes | Label convention | Checkpoint |
| :--- | ---: | ---: | :--- | :--- |
| NinaPro DB5 | 16 | 53 | 52 gestures + resting | `DB5/DB5_finetune_5sec.safetensors` |
| EPN-612 | 8 | 6 | 5 gestures + hand relaxed | `EPN612/EPN_finetune_5sec.safetensors` |
| UCI EMG | 8 | 6 | Dataset-specific six-class gesture labels | `UCI_EMG/UCI_finetune_5sec.safetensors` |
| NinaPro DB8 | 16 | 5 | Regression outputs | `DB8/DB8_finetune_500ms.safetensors` |

The model configuration must match the checkpoint, especially `in_chans`,
`num_classes`, and `task`. The built-in BioFoundation `TinyMyo_finetune`
experiment currently points to UCI EMG data and can be launched from a
BioFoundation checkout with:

```bash
python -u run_train.py +experiment=TinyMyo_finetune \
  model.in_chans=8 \
  pretrained_safetensors_path=/absolute/path/to/TinyMyo/UCI_EMG/UCI_finetune_5sec.safetensors
```

DB5, EPN-612, and DB8 require matching dataset-specific data-module settings
in BioFoundation before training. Benchmark results
and the latest experimental protocols are maintained in the paper and model card.

## 📜 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},
      url={https://arxiv.org/abs/2512.15729},
}
```