Wearable Echomyography System

A comprehensive Python implementation of a wearable ultrasound-based muscle monitoring system, inspired by "A wearable echomyography system based on a single transducer." Nature Electronics 7.11 (2024): 1035-1046."

Overview

This system uses echomyography (ultrasound-based muscle monitoring) to track muscle activity through a single transducer, offering significant advantages over traditional electromyography (EMG):

• Higher signal stability - Actively transmitted ultrasound waves
• Better spatial resolution - Direct tissue interface detection
• Deeper tissue penetration - Can monitor muscles 6+ cm deep
• No skin preparation required - Unlike EMG electrodes
• Real-time monitoring - 50 Hz frame rate for responsive tracking

Applications

1. Diaphragm Monitoring

• Continuous breathing pattern analysis
• Respiratory disease monitoring
• Ventilator weaning assessment
• Sleep apnea detection

2. Hand Gesture Recognition

• 13 degrees of freedom tracking
• Prosthetic control interfaces
• Virtual reality interactions
• Rehabilitation monitoring

System Architecture

Wearable Echomyography System                

  Single Ultrasound Transducer                                
     ├── Piezoelectric Layer (4MHz center frequency)             
     ├── Backing Layer (vibration damping)                       
     └── Flexible Electrodes (serpentine design)                 

  Signal Processing Pipeline                                  
     ├── RF Signal Acquisition (12 MHz sampling)                 
     ├── Bandpass Filtering (2-6 MHz)                            
     ├── Envelope Detection (Hilbert transform)                  
     └── Tissue Boundary Detection                               

  Analysis Modules                                            
     ├── DiaphragmMonitor (breathing patterns)                   
     └── HandGestureClassifier (deep learning)                  

  Output Interface                                           
     ├── Real-time Monitoring                                   
     ├── Analysis Reports                                       
     └── Visualization Tools                                    

Quick Start

Installation

# Clone the repository
git clone https://github.com/pradosh94/Ultrasound-Gesture-Recognition
cd Ultrasound-Gesture-Recognition

# Install dependencies
pip install numpy tensorflow scipy matplotlib pandas

Basic Usage

from echomyography_system import EchomyographySystem
import numpy as np

# Initialize for diaphragm monitoring
system = EchomyographySystem("diaphragm")

# Process a single RF frame (1024 samples at 12 MHz)
rf_signal = np.random.randn(1024)  # Replace with actual data
results = system.process_real_time_frame(rf_signal)

print(f"Diaphragm thickness: {results['thickness']:.2f} mm")
print(f"Breathing mode: {results['breathing_mode']}")
print(f"DTF: {results['dtf']:.3f}")

Core Components

1. UltrasoundSignalProcessor

Purpose: Core signal processing for RF ultrasound data

Key Features:

• Bandpass filtering (2-6 MHz for muscle imaging)
• Envelope detection using Hilbert transform
• Tissue boundary detection with peak finding
• Automatic thickness calculation

processor = UltrasoundSignalProcessor(sampling_rate=12_000_000)

# Process RF signal
filtered_signal = processor.apply_bandpass_filter(rf_data)
envelope = processor.extract_envelope(filtered_signal)
boundaries = processor.detect_tissue_boundaries(envelope)
thickness = processor.calculate_tissue_thickness(boundaries)

2. DiaphragmMonitor

Purpose: Specialized breathing pattern analysis

Key Metrics:

• DTF (Diaphragm Thickening Fraction): Primary breathing assessment metric
• Respiratory Rate: Breaths per minute calculation
• Breathing Mode Classification:
  • abdominal: DTF > 0.25 (deep diaphragmatic breathing)
  • mixed: DTF 0.10-0.25 (combined breathing)
  • thoracic: DTF < 0.10 (shallow chest breathing)

monitor = DiaphragmMonitor(signal_processor)

# Analyze breathing over time
for rf_frame in rf_data_stream:
    results = monitor.process_rf_frame(rf_frame)
    
    if results['breathing_mode'] == 'thoracic':
        print(" Shallow breathing detected!")

3. HandGestureClassifier

Purpose: Deep learning-based gesture recognition

Architecture:

• 8-layer 1D CNN for feature extraction
• Tracks 13 degrees of freedom:
  • 10 finger joint angles (MCP, PIP, IP)
  • 3 wrist rotations (roll, pitch, yaw)
• Real-time prediction at 50 Hz

classifier = HandGestureClassifier()

# Train model (if you have training data)
# classifier.train_model(rf_signals, joint_angles)

# Predict gesture
gesture_results = classifier.predict_gesture(rf_signal)
joint_angles = gesture_results['joint_angles']
print(f"Wrist pitch: {joint_angles[-2]:.1f}°")

Usage Examples

Diaphragm Monitoring Example

import matplotlib.pyplot as plt

# Initialize system
diaphragm_system = EchomyographySystem("diaphragm")

# Collect data over time
thickness_history = []
dtf_history = []

for i in range(300):  # 6 seconds at 50 Hz
    # Get RF signal from hardware (simulated here)
    rf_signal = get_ultrasound_frame()  # Your hardware interface
    
    # Process frame
    results = diaphragm_system.process_real_time_frame(rf_signal)
    
    thickness_history.append(results['thickness'])
    dtf_history.append(results['dtf'])

# Generate comprehensive report
report = diaphragm_system.generate_report()
print(f"Average DTF: {report['average_dtf']:.3f}")
print(f"Breathing mode: {report['dominant_breathing_mode']}")

# Visualize results
diaphragm_system.visualize_results()

Hand Gesture Tracking Example

# Initialize gesture system
gesture_system = EchomyographySystem("gesture")

# Real-time gesture tracking
for rf_frame in realtime_rf_stream():
    results = gesture_system.process_real_time_frame(rf_frame)
    
    # Extract specific joint angles
    angles = results['predictions_dict']
    wrist_pitch = angles['wrist_pitch']
    index_mcp = angles['index_mcp']
    
    # Control external device
    if wrist_pitch > 45:
        robot_arm.move_up()
    elif abs(index_mcp) > 30:
        robot_arm.grasp()

Clinical Applications

Respiratory Monitoring

• COPD patients: Detect breathing pattern changes
• Ventilator weaning: Assess diaphragm function recovery
• Sleep studies: Monitor breathing disorders
• Exercise physiology: Breathing efficiency analysis

Rehabilitation

• Hand therapy: Objective progress tracking
• Prosthetic training: Natural control interfaces
• Stroke recovery: Motor function assessment
• Sports medicine: Movement pattern analysis

Customization & Extension

Adding New Breathing Patterns

def custom_breathing_classifier(dtf_sequence):
    """Custom breathing pattern classification"""
    if detect_irregular_pattern(dtf_sequence):
        return "pathological"
    elif detect_exercise_pattern(dtf_sequence):
        return "exercise"
    return "normal"

# Integrate into monitor
monitor.custom_classifier = custom_breathing_classifier

Training Custom Gesture Models

# Prepare training data
rf_training_data = load_rf_signals("training_data.npz")
gesture_labels = load_joint_angles("gesture_labels.npz")

# Train model
classifier = HandGestureClassifier()
history = classifier.train_model(rf_training_data, gesture_labels, epochs=200)

# Save trained model
classifier.model.save("custom_gesture_model.h5")

Research References

Primary Research: Gao, X. et al. "A wearable echomyography system based on a single transducer." Nature Electronics 7, 1035–1046 (2024).

License

This project is licensed under the MIT License. See LICENSE file for details.

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