Update README.md

README.md

@@ -6,6 +6,8 @@ tags:
 - emg
 - bio-signals
 - foundation-model
+base_model:
+- PulpBio/TinyMyo
 ---
 
 # TinyMyo: Tiny Foundation Model for EMG Signal Processing
@@ -21,44 +23,51 @@ tags:
 </p>
 
 ## 📖 Overview
-**TinyMyo** is a lightweight (3.6M parameters), Transformer-based foundation model designed specifically for surface electromyography (sEMG) signal processing. Unlike large-scale models, TinyMyo is purpose-built for **ultra-low-power edge deployment**, enabling real-time motor intent decoding, neuromuscular assessment, and human-machine interaction directly on microcontrollers like the GAP9.
+**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 **TinyissimoMyo**) 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.
 
 ## 🚀 Key Highlights
 *   **Generalist Foundation:** Pre-trained on a massive, heterogeneous corpus of >480 GB of EMG data (NinaPro DB6/7, EMG2Pose) using self-supervised masked reconstruction.
-*   **Edge-Ready:** The first EMG foundation model demonstrated on an ultra-low-power MCU (GAP9).
-*   **Highly Efficient:** Just 3.6M parameters, ensuring low latency and high energy efficiency (44.91 mJ per inference).
+*   **Edge-Ready:** The first EMG foundation model demonstrated on an ultra-low-power MCU (GAP9), achieving sub-100ms inference for real-time applications.
+*   **Highly Efficient:** Just 3.6M parameters (1.9M for TinyissimoMyo), ensuring low latency and high energy efficiency (~45 mJ per inference).
 *   **Versatile:** Achieves state-of-the-art (SoA) performance across hand gesture classification, kinematic regression, and speech processing.
 
 ## 🧠 Model Architecture
-*   **Core:** 8-layer bidirectional Transformer encoder.
+*   **Core:** 8-layer bidirectional Transformer encoder (4-layer for TinyissimoMyo).
 *   **Embeddings:** 192-dimensional latent space with 3 attention heads.
-*   **Tokenization:** Channel-independent patching (20 samples per patch) utilizing Rotary Position Embeddings (RoPE) to preserve temporal alignment across channels.
-*   **Deployment:** Optimized via multi-level tiling and INT8 quantization for execution on resource-constrained hardware.
+*   **Tokenization:** Channel-independent patching (20 samples per patch) utilizing Rotary Position Embeddings (RoPE) to preserve temporal alignment across channels without spurious cross-channel ordering.
+*   **Deployment:** Optimized via offline liveness analysis, multi-level memory tiling, and INT8 fixed-point quantization for resource-constrained hardware execution.
 
 ## 📊 Performance Benchmarks
 
 | Task | Dataset | Metric | TinyMyo Result |
 | :--- | :--- | :--- | :--- |
-| **Gesture Classification** | NinaPro DB5 | Accuracy | **89.41%** |
-| **Gesture Classification** | EPN-612 | Accuracy | **96.74%** |
-| **Gesture Classification** | UCI EMG | Accuracy | **97.56%** |
-| **Kinematic Regression** | NinaPro DB8 | MAE | **8.77°** |
+| **Gesture Classification** | NinaPro DB5 | Accuracy | **87.98%** |
+| **Gesture Classification** | EPN-612 | Accuracy | **96.57%** |
+| **Gesture Classification** | UCI EMG | Accuracy | **97.10%** |
+| **Gesture Classification** | Generic Neuromotor Interface | CLER | **0.142** |
+| **Kinematic Regression** | NinaPro DB8 | MAE | **8.8°** |
 | **Speech Synthesis** | Gaddy | WER | **33.54%** |
 | **Speech Recognition** | Gaddy | WER | **33.95%** |
 
 ## ⚡ Deployment (GAP9 MCU)
-TinyMyo bridges the gap between high-performance deep learning and wearable constraints:
-*   **Inference Time:** 0.785 s
+TinyMyo bridges the gap between high-performance deep learning and stringent wearable constraints. We provide two variants to balance the accuracy-latency trade-off:
+
+### TinyMyo (3.6M Parameters)
+*   **Inference Time (5s window):** 0.785 s
 *   **Energy Consumption:** 44.91 mJ
 *   **Power Envelope:** 57.18 mW
 
+### TinyissimoMyo (1.9M Parameters)
+*   **Inference Time (5s window):** 0.496 s
+*   **Inference Time (1s window):** **0.089 s** *(Sub-100ms regime, ideal for real-time prosthetic control)*
+
 ## 🛠️ Getting Started
-TinyMyo is part of the [BioFoundation](https://github.com/pulp-bio/BioFoundation) ecosystem.
+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).
 
-### Fine-tuning
+### Loading & Fine-tuning
 You can easily fine-tune the pre-trained weights for your specific task:
 
 ```bash
@@ -77,4 +86,4 @@ This model is licensed under **CC BY-ND 4.0**. If you find TinyMyo useful in you
       archivePrefix={arXiv},
       primaryClass={eess.SP}
 }
-```
+```