---
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)

## Demo

https://youtube.com/shorts/JOfaNj-Af6Q

## Author

Mohammed Alansi
AI & Biomechatronics Research — EMG-Based Prosthetic Arm Control