---
license: cc-by-nd-4.0
language:
- en
model-index:
- name: TinyMyo
  results:

  # -------------------------
  # Hand Gesture Classification
  # -------------------------
  - 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

  # -------------------------
  # Generic Neuromotor Interface (Meta RL)
  # -------------------------
  - 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

  # -------------------------
  # Hand Kinematic Regression
  # -------------------------
  - 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

  # -------------------------
  # Silent Speech Synthesis
  # -------------------------
  - 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

  # -------------------------
  # Silent Speech Recognition
  # -------------------------
  - 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
---

<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-based **foundation model for surface EMG (sEMG)**.
It is pretrained on >480 GB of EMG data and optimized for **ultra-low-power, real-time deployment**, including **microcontrollers (GAP9)** where it achieves an inference time of **0.785 s**, energy of **44.91 mJ** and power envelope of **57.18 mW**.

TinyMyo is built for **broad generalization** across datasets, sensor configurations, movement tasks, subjects, and domains (gesture, kinematics, speech).

---

# 🔒 License & Usage (Model Weights)

The released TinyMyo weights are licensed under **CC BY-ND 4.0**.
This summary is not legal advice—please read the full license.

### ✅ You may

* **Use** and **redistribute** the **unmodified** TinyMyo weights (including commercially) **with attribution**.
* **Fine-tune/modify internally** for research or production without redistributing modified weights.
* **Publish code, configs, evaluations, and papers** using TinyMyo.

### 🚫 You may not

* **Share or host modified weights** in any form (including LoRA/adapter deltas, pruned/quantized models).
* **Claim endorsement** from the TinyMyo authors without permission.
* **Use the TinyMyo name** for derivative models.

### 🤝 Contributing Improvements

To upstream improvements, submit a **PR** to the
**[BioFoundation repository](https://github.com/pulp-bio/BioFoundation)** with:

1. Full reproducibility artifacts (configs, logs, seeds, environment).
2. Evaluation on standard protocols (e.g., DB5, EPN-612, UCI EMG, DB8, Silent Speech).
3. Comparison to TinyMyo’s reported metrics.

Approved PRs will be retrained and released as **official TinyMyo** checkpoints under CC BY-ND.

---

# 🔎 1. Default Input & Preprocessing

Unless specified otherwise, TinyMyo expects:

* **Channels:** 16
* **Sampling rate:** 2000 Hz
* **Segment length:** 1000 samples (0.5 s)
* **Windowing:** 50% overlap (pretraining)
* **Preprocessing:**

  * 4th-order **20–450 Hz bandpass**
  * **50 Hz notch filter**
  * **Min–max normalization** (pretraining)
  * **Z-score normalization** (downstream)

Datasets with <16 channels are **zero-padded (pretraining only)**.

---

# 🔬 2. Pretraining Overview

TinyMyo is pretrained via masked reconstruction on **three large-scale EMG datasets**:

| Dataset     | Subjects | fs      | Channels | Size    |
| ----------- | -------- | ------- | -------- | ------- |
| Ninapro DB6 | 10       | 2000 Hz | 14       | 20.3 GB |
| Ninapro DB7 | 22       | 2000 Hz | 12       | 30.9 GB |
| EMG2Pose    | 192      | 2000 Hz | 16       | 431 GB  |

## Tokenization: Channel-Independent Patches

Unlike EEG FMs that mix channels early, TinyMyo uses **per-channel patching**:

* Patch length: **20 samples**
* Patch stride: **20 samples**
* Tokens/channel: **50**
* Total seq length: **800 tokens** (16 x 50)
* Positional encoding: **RoPE (rotary)**

This preserves electrode-specific structure while allowing attention to learn cross-channel relationships.

## Transformer Encoder

* **8 layers**, **3 heads**
* Embedding dim: **192**
* Pre-LayerNorm
* Dropout & drop-path: **0.1**

## Lightweight Decoder

A **single linear layer** (~3.9k params) reconstructs masked patches.
Following SimMIM, this forces the encoder to learn robust latent structure.

## Masking Objective

* **50% random masking** with a learnable `[MASK]` token
* Loss: **Smooth L1** with small penalty on visible patches
$$
  \mathcal{L} = \mathcal{L}*{\text{masked}} + 0.1,\mathcal{L}*{\text{visible}}
$$

## Training Setup

* Optimizer: **AdamW** (β=(0.9,0.98), wd=0.01)
* LR: **1e-4** with cosine decay
* Batch size: **512** (with grad accumulation)
* Epochs: **50**, warm-up: 10
* Hardware: **4× NVIDIA GH200 GPUs**

---

# 🧠 3. Architecture Summary

### Model Variant

| Variant | Params   | (Layers, Dim) |
| ------- | -------- | ------------- |
| TinyMyo | **3.6M** | (8, 192)      |

---

# 🎯 4. Downstream Tasks

TinyMyo generalizes across **gesture classification**, **kinematic regression**, and **speech EMG**—with state-of-the-art or competitive results.

---

## 4.1 Hand Gesture Classification

Evaluated on:

* **Ninapro DB5** (52 classes, 10 subjects)
* **EPN-612** (5 classes, 612 subjects)
* **UCI EMG** (6 classes, 36 subjects)
* **Meta Neuromotor Interface** (9 gestures)

### Preprocessing

* EMG filtering: **20–90 Hz bandpass + 50 Hz notch**
* Window sizes:

  * **200 ms** (best for DB5)
  * **1000 ms** (best for EPN, UCI)

### Linear Classification Head

* Input: **C × 192**
* Params: **<40k**

### Performance (Fine-tuned)

| Dataset                  | Metric | Result            |
| ------------------------ | ------ | ----------------- |
| **Ninapro DB5** (200 ms) | Acc    | **89.41 ± 0.16%** |
| **EPN-612** (1000 ms)    | Acc    | **96.74 ± 0.09%** |
| **UCI EMG** (1000 ms)    | Acc    | **97.56 ± 0.32%** |
| **Neuromotor**           | CLER   | **0.153 ± 0.006** |

TinyMyo achieves **new state-of-the-art** on DB5, EPN-612, and UCI.

---

## 4.2 Hand Kinematic Regression (Ninapro DB8)

* Predict **5 joint angles**
* Windows: **200 ms** or **1000 ms**
* Normalization: z-score only

### Regression Head (~788k params)

* Depthwise + pointwise convs
* Upsampling
* Global average pooling
* Linear projection to 5 outputs

### Performance

* **MAE = 8.77 ± 0.12°** (1000 ms)

Note: Prior works reporting ~6.9° MAE are **subject-specific**; TinyMyo trains a **single cross-subject model**, a significantly harder setting.

---

## 4.3 Speech Production & Recognition (Silent Speech)

Dataset: **Gaddy Silent Speech**
(8 channels, 1000 Hz, face/neck EMG)

### Speech Production (EMG → MFCC → HiFi-GAN → Audio)

Pipeline:

1. Residual downsampling
2. TinyMyo encoder
3. Linear projection → **26-dim MFCC**
4. HiFi-GAN vocoder

**WER:** **33.54 ± 1.12%**
≈ state-of-the-art with **>90% fewer params** in the transduction model.

### Speech Recognition (EMG → Text)

* TinyMyo encoder
* Linear projection → **37 characters**
* **CTC** loss
* 4-gram LM + beam search

**WER:** **33.95 ± 0.97%**

TinyMyo is EMG-only, unlike multimodal systems like MONA-LISA.

---

# ⚡ 5. Edge Deployment (GAP9 MCU)

TinyMyo runs efficiently on **GAP9 (RISC-V)** via:

* **INT8 quantization**, including attention
* Multi-level streaming (L3 to L2 to L1)
* Integer LayerNorm, GELU, softmax
* Static memory arena via liveness analysis

### Runtime (DB5 pipeline)

* **Inference time**: **0.785 s**
* **Energy**: **44.91 mJ**
* **Average power**: **57.18 mW**

This is the **first EMG foundation model demonstrated on a microcontroller**.

---

# 📊 6. Results Summary

### Pretraining

* Smooth L1 reconstruction with high fidelity
* Total compute ≈ **4.0 GFLOPs**

### Downstream Highlights

* **DB5:** 89.41%
* **EPN-612:** 96.74%
* **UCI EMG:** 97.56%
* **Neuromotor:** 0.153 CLER
* **DB8 Regression:** MAE 8.77°
* **Silent Speech Production:** 33.54% WER
* **Silent Speech Recognition:** 33.95% WER

TinyMyo matches or exceeds state-of-the-art performance, while being smaller and more efficient than all prior EMG foundation models.

---

# 🛠️ Code & Usage

To fine-tune TinyMyo on downstream tasks, follow the examples in the
**[BioFoundation repository](https://github.com/pulp-bio/BioFoundation)**.

```bash
python -u run_train.py +experiment=TinyMyo_finetune \
    pretrained_safetensors_path=/path/to/model.safetensors
```

Environment variables:

* `DATA_PATH` → dataset path
* `CHECKPOINT_DIR` → checkpoint to load

---

## 🔗 Resources

- **Code:** https://github.com/pulp-bio/BioFoundation  

---

# 📜 Citation

Please cite TinyMyo using:

```bibtex
@misc{fasulo2025tinymyotinyfoundationmodel,
      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={2025},
      eprint={2512.15729},
      archivePrefix={arXiv},
      primaryClass={eess.SP},
      url={https://arxiv.org/abs/2512.15729}, 
}
```

---

# 🧭 Contact & Support

* Questions or issues?
  Open an issue on the **BioFoundation GitHub repository**.