AI-Powered Health Monitoring System

Project Overview

The AI-Powered Health Monitoring System is designed to monitor users' health in real-time by leveraging data from wearable devices such as smartwatches and fitness trackers. The system analyzes key health metrics including heart rate, blood oxygen levels, and activity levels to detect anomalies and provide personalized health recommendations.

Key Features

• Real-Time Health Monitoring: Collect and analyze data from wearable devices.
• Anomaly Detection: Utilize AI to identify abnormal health conditions.
• Personalized Recommendations: Offer actionable health advice based on user data.
• User-Friendly Interface: Access health data and recommendations through a mobile or web app.

Project Structure

• data/: Contains data sources and information on how to access and utilize datasets.
• docs/: Includes detailed project reports covering objectives, methodology, results, and conclusions.
• models/: Outlines the machine learning models used, including algorithm descriptions and implementations.
• notebooks/: Contains Jupyter notebooks for exploratory data analysis and visualizations.
• src/: The main source code for the project, including data collection, preprocessing, anomaly detection, recommendations, and evaluation.
• tests/: Unit tests for various components of the system to ensure functionality.
• requirements.txt: Lists the dependencies required for the project.
• Dockerfile: Instructions for building a Docker image for deployment.
• .gitignore: Specifies files and directories to be ignored by Git.

Getting Started

1. Clone the Repository:

   git clone <repository-url>
   cd ai-powered-health-monitoring-system
   

2. Install Dependencies:

   pip install -r requirements.txt
   

3. Run the Application:

   • For the web app, navigate to the src/app directory and run:

   python -m flask run
   

Contributing

Contributions are welcome! Please submit a pull request or open an issue for any enhancements or bug fixes.

License

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

Tshimo's Part - Documentation

1. Documentation and Reporting

Expand the project report to include: Detailed setup instructions (how to run the app, install dependencies, etc.). Explanation of the folder structure and where to find key files. User manual: how to use the web/mobile app, what each feature does. API documentation (if any endpoints are exposed). Description of the testing process (unit, integration, user testing).

2. Visualizations and Screenshots

• Add visuals to the report and presentation:
• Screenshots of the user interface (web app).
• Example outputs (anomaly alerts, recommendations).
• Charts/graphs showing model performance (accuracy, confusion matrix, etc.).
• Diagrams of system architecture or data flow.

3. Presentation Preparation

• Prepare a slide deck for presenting the project:
• Project overview, objectives, and key features.
• Methodology and results.
• Visuals and screenshots.
• Conclusions and future work.

4. User Feedback Summary

• Summarize user testing and feedback:
• How user testing was conducted.
• Key feedback points from users.
• How feedback was used to improve the system.

5. Compliance, Ethics, and Privacy

• Write a section on data privacy and ethics:
• How user data is protected.
• Compliance with regulations (GDPR, HIPAA).
• Ethical considerations in handling health data.

6. References

• Compile a references section:
• Datasets used (e.g., PhysioNet).
• Libraries and frameworks.
• Any research papers or online resources referenced.

Summary for Assignment

• Expand and polish the documentation and report.
• Collect and insert screenshots/visuals.
• Prepare the presentation slides.
• Write the user feedback summary.
• Add a section on compliance, ethics, and privacy.
• Compile the references.