---
license: cc-by-nd-4.0
language:
- en
model-index:
- name: TinyMyo
  results:
  - task:
      type: gesture-classification
    dataset:
      type: ninapro_db5
      name: Ninapro DB5
    metrics:
    - name: acc@1
      type: acc@1
      value: 0.8941
      verified: false
    - name: f1
      type: f1
      value: 0.7797
      verified: false
  - task:
      type: gesture-classification
    dataset:
      type: epn612
      name: EPN-612
    metrics:
    - name: acc@1
      type: acc@1
      value: 0.9674
      verified: false
    - name: f1
      type: f1
      value: 0.9674
      verified: false
  - task:
      type: gesture-classification
    dataset:
      type: uci_emg
      name: UCI-EMG
    metrics:
    - name: acc@1
      type: acc@1
      value: 0.9756
      verified: false
    - name: f1
      type: f1
      value: 0.9755
      verified: false
  - task:
      type: gesture-classification
    dataset:
      type: gni_meta_rl
      name: Generic Neuromotor Interface (Discrete Gesture)
    metrics:
    - name: CLER
      type: classification-error-rate
      value: 0.153
      verified: false
  - task:
      type: kinematic-regression
    dataset:
      type: ninapro_db8
      name: Ninapro DB8
    metrics:
    - name: MAE
      type: mean-absolute-error
      value: 8.77
      verified: false
    - name: RMSE
      type: root-mean-square-error
      value: 13.35
      verified: false
    - name: R2
      type: r2
      value: 0.62
      verified: false
  - task:
      type: speech-synthesis
    dataset:
      type: gaddy_silent_speech
      name: Gaddy Silent Speech (MFCC to Audio)
    metrics:
    - name: WER
      type: word-error-rate
      value: 0.3354
      verified: false
  - task:
      type: speech-recognition
    dataset:
      type: gaddy_silent_speech
      name: Gaddy Silent Speech (EMG to Text)
    metrics:
    - name: WER
      type: word-error-rate
      value: 0.3395
      verified: false
tags:
- emg
- bio-signals
- foundation-model
---

<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" />
  <h1>TinyMyo: a Tiny Foundation Model for Flexible EMG Signal Processing at the Edge</h1>
</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>

**TinyMyo** is a **3.6M-parameter** Transformer foundation model for surface EMG (sEMG), optimized for ultra-low-power edge deployment (GAP9 MCU). It demonstrates state-of-the-art performance across gesture classification, kinematic regression, and speech synthesis.

---

## 🚀 Quick Start

TinyMyo is built as a specialized model within the [BioFoundation](https://github.com/pulp-bio/BioFoundation) framework.

### 1. Requirements
- **Preprocessing:** Dependencies for data scripts are in `scripts/requirements.txt`.
- **BioFoundation:** Full framework requirements for training/inference are in the [GitHub repository](https://github.com/pulp-bio/BioFoundation/blob/main/requirements.txt).

### 2. Preprocessing
Process raw datasets into HDF5 format:
```bash
python scripts/db5.py --data_dir $DATA_PATH/raw/ --save_dir $DATA_PATH/h5/ --seq_len 200 --stride 50
```
*See [scripts/README.md](scripts/README.md) for all dataset commands.*

### 3. Fine-tuning
```bash
python run_train.py +experiment=TinyMyo_finetune pretrained_safetensors_path=/path/to/base.safetensors
```

---

## 🧠 Architecture & Pretraining
- **Core:** 8-layer Transformer encoder (192-dim embeddings, 3 heads).
- **Tokenization:** Channel-independent patching (20 samples/patch) with RoPE.
- **Data:** Pretrained on >480 GB of EMG (NinaPro DB6/7, EMG2Pose).
- **Specs:** 3.6M parameters, 4.0 GFLOPs.

## 🎯 Benchmarks

| Task | Dataset | Metric | TinyMyo |
| :--- | :--- | :--- | :--- |
| **Gesture** | NinaPro DB5 | Accuracy | **89.41%** |
| **Gesture** | EPN-612 | Accuracy | **96.74%** |
| **Gesture** | UCI EMG | Accuracy | **97.56%** |
| **Regression**| NinaPro DB8 | MAE | **8.77°** |
| **Speech** | Gaddy (Speech Synthesis) | WER | **33.54%** |
| **Speech** | Gaddy (Speech Recognition) | WER | **33.95%** |

---

## ⚡ Edge Performance (GAP9 MCU)
- **Inference:** 0.785 s
- **Energy:** 44.91 mJ
- **Power:** 57.18 mW

---

## 📜 License & Citation
Weights are licensed under **CC BY-ND 4.0**. See [LICENSE](LICENSE) for details.

```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},
}
```