The Non-Invasive Cholesterol Detection (NICD) project aims to provide a seamless and non-invasive method to measure cholesterol levels using IoT devices. The system utilizes a combination of hardware components and software applications to gather user data, process sensor inputs, and predict cholesterol levels using machine learning models.
cholestrol.mp4
- User Information Input: Start by scanning a QR code to input user details such as name, age, gender, weight, and lifestyle factors (drinker, smoker, blood pressure, hypertension).
- Sensor Integration: Utilizes TCS34725 and TCS3200 color sensors to gather data from the user.
- Machine Learning Prediction: Processes sensor data through a machine learning model to predict cholesterol levels.
- Cross-Platform Compatibility: Android app for user interaction and data transmission to Raspberry Pi.
- User-Friendly Interface: Display results on an LCD screen for easy reading.
- Color Sensors: TCS34725 & TCS3200
- Raspberry Pi 4
- Arduino Uno
- LCD Screen
- Frontend: Python Django
- Machine Learning: Python
- Mobile App: Java
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Clone the Repository:
git clone https://github.com/xn-coder/NICD.git
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Set Up Hardware: Connect the sensors and LCD screen to the Raspberry Pi and Arduino Uno as per the circuit diagram.
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Install Dependencies:
- For Python:
pip install -r requirements.txt
- For Java: Ensure you have the necessary Android development environment set up.
- For Python:
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Run the Application:
- Start the Django server for the frontend.
- Deploy the Android app on a compatible device.
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Usage:
- Scan the QR code to input user data.
- Follow on-screen instructions to place your finger on the sensors.
- View the predicted cholesterol level on the LCD screen.
This project is licensed under the GNU License - see the LICENSE file for details.
For any inquiries, please contact [email protected].