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metadata

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

TinyMyo: a Tiny Foundation Model for Flexible EMG Signal Processing at the Edge

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 with:

Full reproducibility artifacts (configs, logs, seeds, environment).
Evaluation on standard protocols (e.g., DB5, EPN-612, UCI EMG, DB8, Silent Speech).
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:

Residual downsampling
TinyMyo encoder
Linear projection → 26-dim MFCC
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.

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:

@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.