MeshAlyzer Device

Table of Contents

• Introduction
• Installation
• Usage
• Contributing
• License
• Contact
• Acknowledgements

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Introduction

The MeshAlyzer is an advanced testing device designed to simulate realistic coughing forces and pressures on a porcine abdominal wall. It enables researchers and surgeons to evaluate hernia mesh materials under dynamic conditions, facilitating informed decisions for hernia repair procedures.

Key Features

• Pressure Control System: Integrated pressure sensors with ADCs for precise measurements.
• Valve System: Two 3-position, 5-way valves controlled by four relays.
• User Interface: Customizable testing protocols and real-time data visualization.
• Data Logging: Continuous recording of test data for in-depth analysis.
• Tissue Simulation: Mimics coughing pressures with interchangeable balloon materials.

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Installation

The MeshAlyzer device requires a Raspberry Pi 5 or Raspberry Pi 3 for operation. Follow the instructions below to set up your device.

Raspberry Pi 5 Setup

1. Clone the repository:

   git clone https://github.com/your-username/MeshAlyzer.git
   cd MeshAlyzer

2. Install dependencies:

   sudo apt-get install python3-lgpio

3. Set up the virtual environment (optional but recommended):

    python3 -m venv venv
    source venv/bin/activate
    pip install -r requirements.txt

4. Start the application:

   python main.py

5. Connect hardware components:

   • Link pressure sensors to ADCs as shown in the wiring diagram.
   • Ensure relays and valves are connected to the Raspberry Pi GPIO pins as specified.

6. Launch the application:

   python main.py

Raspberry Pi 3 setup

1. Create automatic startupfile Create a file called '/etc/systemd/system/pi3-sensor.service' with the following content :

[Unit]
Description=Sensor Data Sender Service
After=network.target

[Service]
ExecStart=/usr/bin/python3 /home/pi/path/to/PressureSensorReader.py
Restart=always
User=pi

[Install]
WantedBy=multi-user.target

Then enable and start the service with:

sudo systemctl enable pi3-sensor.service
sudo systemctl start pi3-sensor.service

2. Run the script to test the sensor:

sudo nano /etc/systemd/system/spidev-load.service

add:

[Unit]
[Unit]
Description=Pressure Sensor Client
After=network.target

[Service]
ExecStartPre=/sbin/modprobe spidev
ExecStart=/usr/bin/python3 /home/lakelab/MeshAlyzer/PressureSensorReader_rasp3.py
Restart=always
User=lakelab
WorkingDirectory=/home/lakelab
StandardOutput=inherit
StandardError=inherit

[Install]
WantedBy=multi-user.target

Then enable and start the service with:

sudo systemctl daemon-reexec
sudo systemctl enable spidev-load.service
sudo systemctl start spidev-load.service

ADC Setup

The MeshAlyzer uses the ADS1256 ADC for pressure sensing. Follow these steps to set it up:

1. wiring diagram Connect the ADS1256 to the Raspberry Pi 3: VCC → 5V GND → GND DIN (MOSI) → GPIO 10 (SPI0 MOSI) DOUT (MISO) → GPIO 9 (SPI0 MISO) SCLK → GPIO 11 (SPI0 SCLK) CS (Chip Select) → GPIO 8 (SPI0 CE0) DRDY (Data Ready) → GPIO 7 RESET → GPIO 22

2. Install the required library:

sudo apt-get install python3-lgpio

Usage

1. Setup:

   • Place the porcine tissue sample on the testing platform.
   • Connect the device to a power source and ensure all components are operational.

2. Run Tests:

   • Open the user interface and select a protocol.
   • Start the simulation and observe the test in progress.

3. Analyze Data:

   • Access the recorded data logs for performance evaluation.
   • Use the insights to determine optimal mesh materials.

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Contributing

We welcome contributions to enhance the MeshAlyzer project!

How to Contribute

1. Fork the repository.
2. Create a new branch for your feature or bugfix:

   git checkout -b feature-name

3. Commit your changes:

   git commit -m "Description of your changes"

4. Push your branch and create a pull request:

   git push origin feature-name

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License

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

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Contact

For questions or feedback, please contact:

• Cole Hanan
• Alexander Gadin
• Evan Maples

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Acknowledgments

We would like to thank:

• Dr. Spencer Lake for his guidance and support.
• The BME Department at Washington University in St. Louis for resources and assistance.
• The Chemistry Machine Shop for their technical expertise.