Update README.md

README.md

@@ -2,18 +2,311 @@
 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
 ---
-<div align="center">
-  <h1>TinyMyo: a Tiny Foundation Model for EMG Signals at the Edge</h1>
-</div>
-<p align="center">
-  <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>
+
+<div align="center"> 
+  <h1>TinyMyo: a Tiny Foundation Model for Flexible EMG Signal Processing at the Edge</h1> 
+</div> 
+<p align="center"> 
+  <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> 
 </p>
 
-**TinyMyo** is a compact, Transformer-based **foundation model for surface EMG** signals, pre-trained on **more than 480 GB** of data drawn from multiple large-scale EMG datasets. It is optimized for **low-power, real-time signal decoding**, making it suitable for wearable and embedded applications.
+**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 **36.45 mW** average power consumption—the *first demonstration of an EMG foundation model running on an MCU*.
+
+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** (16×50)
+* 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→L2→L1)
+* Integer LayerNorm, GELU, softmax
+* Static memory arena via liveness analysis
+
+### Runtime (DB5 pipeline)
+
+* **Inference time:** 12.2 s
+* **Energy:** 0.44 J
+* **Average power:** **36.45 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:** to be released following review
+- **Code:** https://github.com/pulp-bio/BioFoundation  
+
+---
+
+# 📜 Citation
+
+Please cite TinyMyo using:
+
+```bibtex
+```
+
+---
+
+# 🧭 Contact & Support
+
+* Questions or issues?
+  Open an issue on the **BioFoundation GitHub repository**.