Add README

README.md

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+---
+language: en
+tags:
+  - emg
+  - gesture-recognition
+  - prosthetic-hand
+  - cnn-lstm
+  - myo-armband
+  - quick-calibration
+  - real-time
+license: mit
+---
+
+# EMG Hand Control — Prosthetic Hand Gesture Recognition
+
+Real-time EMG-based hand gesture recognition for prosthetic arm control using CNN+LSTM with Quick Calibration. Works for any new user after ~2 minutes of calibration.
+
+## Performance
+
+| Gesture | Accuracy (Self) | Accuracy (New User after Calibration) |
+|---------|----------------|---------------------------------------|
+| rest | 100% | ~85% |
+| fist | 100% | ~85% |
+| grasp | 99% | ~80% |
+| index | 97% | ~75% |
+| middle | 100% | ~85% |
+| ring | 100% | ~85% |
+| pinky | 95% | ~75% |
+| thumb | 100% | ~85% |
+| wrist_rotate_out | 92% | ~80% |
+| wrist_rotate_in | 90% | ~80% |
+| **Overall** | **97.2%** | **~85%** |
+
+## Architecture
+Input: 150 samples × 8 EMG channels (750ms @ 200Hz)
+↓
+CNN Block:
+Conv1D(64)  → BatchNorm → ReLU
+Conv1D(128) → BatchNorm → ReLU → MaxPool → Dropout(0.3)
+Conv1D(256) → BatchNorm → ReLU → MaxPool → Dropout(0.3)
+↓
+Bidirectional LSTM (hidden=128, layers=2, dropout=0.3)
+↓
+FC: Dense(128) → ReLU → Dropout(0.4) → Dense(10)
+↓
+Output: 10 gesture classes
+
+## Quick Calibration System
+
+New user calibration in ~2 minutes:
+
+User wears Myo Armband
+Performs each gesture 3 times × 5 seconds
+System fine-tunes last layer on user data (30s)
+Ready — no full retraining needed
+
+
+## Gestures
+
+| Label | Gesture |
+|-------|---------|
+| 0 | rest |
+| 1 | fist |
+| 2 | grasp (cylindrical grip) |
+| 3 | index finger extension |
+| 4 | middle finger extension |
+| 5 | ring finger extension |
+| 6 | pinky finger extension |
+| 7 | thumb extension |
+| 8 | wrist rotate out (pronation) |
+| 9 | wrist rotate in (supination) |
+
+## Dataset
+
+- 4 personal recording sessions
+- 10 gestures × 10 rounds × 5 seconds each
+- ~400,000 EMG samples
+- Myo Armband — 8 channels — 200Hz
+
+## Training Details
+
+| Parameter | Value |
+|-----------|-------|
+| Window | 150 samples (750ms) |
+| Step | 75 samples (50% overlap) |
+| Optimizer | AdamW (lr=5e-4) |
+| Loss | Weighted CrossEntropy + Label Smoothing |
+| Epochs | 100 (best at epoch 78) |
+| Batch size | 128 |
+| Normalization | Global fixed (saved as .npy files) |
+
+## Key Technical Contributions
+
+1. **Block-level train/test split** — prevents data leakage from overlapping windows
+2. **Fixed global normalization** — ensures train/inference consistency (critical fix that improved accuracy from 2% to 94% on rest gesture)
+3. **Quick calibration via last-layer fine-tuning** — enables cross-user generalization without full retraining
+4. **Hysteresis voting** — stable real-time predictions for actuator control
+
+## Files
+
+| File | Description |
+|------|-------------|
+| `models/best_model_hand.pt` | Trained PyTorch weights |
+| `models/hand_norm_mean.npy` | Global normalization mean |
+| `models/hand_norm_std.npy` | Global normalization std |
+| `code/train_hand.py` | Training pipeline |
+| `code/quick_calibration.py` | Calibration + real-time inference |
+| `code/collect_hand_data.py` | Guided data collection |
+
+## Usage
+
+```python
+# Quick Calibration for new user
+python3 quick_calibration.py
+# Records 2 min → fine-tunes → real-time inference
+```
+
+## Hardware
+
+- Sensor: Myo Armband (8 EMG channels, 200Hz, Bluetooth)
+- Controller: Raspberry Pi (deployment)
+- Prosthetic: 3D-printed hand with servo motors + tendon system
+- Communication: Serial (115200 baud)
+
+## Related
+
+- Drone Control Model: [malansi/EMG-Gesture-Recognition](https://huggingface.co/malansi/EMG-Gesture-Recognition)
+- Dataset: [malansi/EMG-Gesture-Dataset](https://huggingface.co/datasets/malansi/EMG-Gesture-Dataset)
+
+## Author
+
+Mohammed Alansi
+AI & Biomechatronics Research — EMG-Based Prosthetic Arm Control