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Hypoglycemia Prediction (GlyCare) App

Project Overview

This project presents the development of an intelligent real-time hypoglycemia prediction system designed to enhance diabetes management by providing accurate and timely predictions of hypoglycemic events. The system integrates deep learning algorithms within a mobile application that interacts with wearable devices, such as Continuous Glucose Monitoring (CGM) sensors and smartwatches, to collect real-time data on glucose levels, insulin dosages, physical activity, and sleep patterns.

Project Objectives

  • Develop an AI Model for Hypoglycemia Prediction: Create an advanced AI model capable of predicting the risk of hypoglycemic episodes for individual patients with T1D.

  • Seamless Integration of Sensor Data: Integrate data from the "Libre 2" glucose monitoring sensor, smartwatches, and sleep tracking sensors to provide a comprehensive and real-time view of the user's health status.

  • Accurate Predictions of Nocturnal Hypoglycemic Events: Implement AI and Deep Learning algorithms to enable accurate real-time predictions, specifically focusing on nocturnal hypoglycemic events.

  • Mobile Application Development: Develop an intuitive mobile application using Flutter, delivering timely alerts, personalized insights, and educational content to users.

  • Proactive Management of Type 1 Diabetes: Ensure the system empowers individuals with T1D to proactively manage their condition through real-time alerts and user-friendly features.

Features

  • Real-time Data Collection: Interacts with CGM sensors and smartwatches to collect real-time data on glucose levels, insulin dosages, physical activity, and sleep patterns.

  • AI-Powered Predictions: Utilizes Gated Recurrent Units (GRU) networks to predict hypoglycemic events with high accuracy.

  • User-friendly Mobile Application: Provides timely alerts, personalized insights, and educational content through an intuitive mobile application developed with Flutter.

Technology Stack

  • Mobile Development: Flutter

  • Backend: Firebase Firestore

  • Bluetooth Low Energy (BLE) Technology: For wireless connections with CGM sensors and fitness trackers

  • AI Model Deployment: TensorFlow Lite

Installation and Setup

1. Clone the Repository:

git clone https://github.com/Youssef-Remah/Hypoglycemia_Prediction.git

2. Navigate to the Project Directory:

cd hypoglycemia-prediction

3. Install Dependencies:

flutter pub get

4. Run the Application:

flutter run

Usage

  • Connect Devices: Ensure your CGM sensor and smartwatch are connected and paired with the application.

  • Monitor Data: View real-time glucose levels, insulin dosages, and other health metrics within the app.

  • Receive Alerts: Get notified of predicted hypoglycemic events and take preventive measures.

License

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

Acknowledgements