📝 Overview

This project focuses on developing a Portable Self-Assessment Audiometer using Raspberry Pi. The device enables individuals to perform hearing tests without requiring an audiologist, making hearing screening accessible, affordable, and convenient.

✨ Features

• 🔹 Uses Raspberry Pi 3 B+ for signal generation and data processing.
• 🔹 Pure Tone Audiometry implementation for hearing self-assessment.
• 🔹 Python-based software for tone generation and audiogram visualization.
• 🔹 Automated threshold detection using the Hughson Westlake method.
• 🔹 User-friendly interface developed with Tkinter for ease of use.
• 🔹 Portable & cost-effective alternative to traditional audiometers.
• 🔹 Data storage in CSV format for future reference.
• 🔹 Real-time result visualization with an interactive audiogram chart.
• 🔹 Remote accessibility through VNC for medical professionals.

🛠️ Hardware Requirements

• 🖥️ Raspberry Pi 3 B+ (or later versions)
• 🎧 Headphones (TDH-49 recommended for accurate testing)
• 🖱️ USB Mouse (for patient response input)
• 🔌 Power Supply (5V, 2.5A)
• 🌐 Ethernet Cable or WiFi Adapter (for remote access)
• 📺 External Display (Optional)

💾 Software Requirements

• 🖥️ Operating System: Raspbian OS (Raspberry Pi OS)
• 💻 Programming Language: Python 3
• 📦 Required Libraries:
  • 🟦 NumPy
  • 📊 Matplotlib
  • 📑 Pandas
  • 🎵 PyAudio (for sound processing)
  • 🖼️ Tkinter (for GUI development)
  • 🌍 PyVNC (for remote access)

📂 Project Files

This Google Drive has all the source code & report of the project.

Note: Large documents are present in the above drive as GitHub only offers a 25MB max per document.

🚀 Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/yourusername/Portable-Self-Assessment-Audiometer.git
cd Portable-Self-Assessment-Audiometer

2️⃣ Install Dependencies

pip install numpy matplotlib pandas pyaudio

3️⃣ Run the Audiometer Software

python audiometer.py

🔍 How It Works

• 🎵 Tone Generation: The system generates pure tones at different frequencies ranging from 125 Hz to 8 kHz.

• 🎧 User Interaction: The user listens to tones through headphones and responds by clicking the mouse.

• 🔊 Volume Adjustment: The program records responses and automatically adjusts the volume using the modified Hughson Westlake method.

• 📈 Audiogram Creation: An audiogram is generated based on the user’s responses.

• 💾 Data Storage: Results are stored in CSV format with date and time for future reference.

• 📊 Graphical Analysis: The software provides a graphical analysis of hearing loss stages and allows comparison over time.

  🎯 Applications

• 🏥 Hearing self-assessment for individuals.

• 👨‍⚕️ Preliminary hearing screening before professional diagnosis.

• 🧓 Early detection of hearing loss in elderly individuals.

• 🔗 Remote monitoring by audiologists via VNC.

• 🎓 Educational purposes for audiology students and researchers.

🔮 Future Enhancements

• 📱 Integration with mobile applications for better accessibility.
• 🎶 Support for bone conduction audiometry.
• 🤖 Enhanced machine learning-based threshold prediction.
• ☁️ Integration with cloud storage for result tracking.
• 🌎 Multi-language support for global usability.

📂 Sample Output

1. Thonny IDE

2. Instruction of the Project

2.1 Instruction

3. Patient Response Screen

4. Output (Audiogram)

4.1 Audiogram

5. Test Analysis

6. Portable Audiometer

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👨‍💻 Project Contributors

• 👤 Adhil M (Founder & Maintainer)
• 👤 Pranesh S
• 👤 Naveen S

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📜 License

This project is open-source and available under the MIT License.

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