HealthGuard: Empowering Informed Food Choices with AI

HealthGuard is an innovative mobile application designed to assist users in making informed and safe food choices tailored to their unique health profiles. Leveraging the power of machine learning (ML) and comprehensive data from OpenFoodFacts, HealthGuard offers a personalized and seamless experience for health-conscious individuals.

Open Source and Community Driven

HealthGuard is an open-source project, inviting collaboration and contributions from developers, health professionals, and enthusiasts around the world. By being open-source, HealthGuard hopes to evolve and adapt to the diverse needs of its users, continually improving and expanding its capabilities.

Vision

HealthGuard aims to change the way people consume food by empowering them with the knowledge and tools needed to make healthier, safer choices. By leveraging technology and community input, HealthGuard aspires to create a world where everyone can access and understand the impact of their food on their health.

HealthGuard represents the next step in personalized nutrition and health management, harnessing technology to empower users to make smarter, safer food choices effortlessly.

Key Features

1. Personalized Health Profiles:

   • Users create detailed health profiles, including dietary restrictions, allergies, health conditions, and preferences. This information is stored securely and used to tailor food recommendations.

2. Smart QR Code Scanner:

   • The app features an intuitive QR code scanner that allows users to quickly scan product codes. Upon scanning, the app retrieves product information from OpenFoodFacts and compares it with the user's health profile.

3. Real-Time Feedback:

   • HealthGuard provides instant feedback on scanned products, indicating whether they are safe and suitable for consumption. The app highlights any potential allergens, dietary conflicts, and health concerns in a clear and user-friendly manner.

4. Machine Learning Integration:

   • The app employs machine learning algorithms to continually improve its recommendation accuracy. By analyzing user interactions and feedback, the ML models get better at identifying safe and suitable products over time.(But lacks enough training dataset for now)

5. Comprehensive Food Database:

   • HealthGuard utilizes data from OpenFoodFacts, an extensive and collaborative database of food products. This ensures users have access to up-to-date and detailed information on ingredients, nutritional values, and more.

6. User-Friendly Interface:

   • The app boasts an easy-to-navigate interface, making it accessible for users of all ages. The design prioritizes clarity, simplicity, and effectiveness.

How It Works

1. Setup and Profile Creation:

   • Users download HealthGuard from the app store and create their profiles, inputting detailed health information including allergies, dietary restrictions, and health preferences.

2. Scanning Products:

   • At the grocery store or at home, users scan the QR code of any food product using the app’s built-in scanner.

3. Instant Analysis:

   • The app fetches product data from OpenFoodFacts and processes it through its ML algorithms, comparing it with the user’s health profile.

4. Feedback and Recommendations:

   • HealthGuard provides immediate feedback on the product’s suitability. If the product is deemed consumable, the app might also suggest healthier alternatives or additional insights about the product’s health benefits.

Example Use Case

• User Profile: Sarah is lactose intolerant and follows a gluten-free diet due to a sensitivity. She also prefers low-sugar foods to manage her blood sugar levels.

• Scanning Process: At the supermarket, Sarah scans a chocolate bar’s QR code using HealthGuard.

• Feedback: The app instantly informs Sarah that the chocolate bar contains lactose and high sugar levels, making it unsuitable for her.

• Result: Sarah makes an informed choice that aligns with her dietary needs and health goals, avoiding potential health issues.

Tech Stack

• Django

• React Native

• Python

Acknowledgements

This project uses data from the OpenFoodFacts.org open-source platform.

Future Enhancements

• Expanded Database Integration:

  • Incorporating additional databases and sources to provide even more comprehensive food information.

• Community Features:

  • Allowing users to share their experiences and product recommendations within a community of health-conscious individuals.

• Health Tracking:

  • Integrating with fitness trackers and health apps to offer holistic health and wellness insights.

• Continuous Learning:

  • The more users interact with the app, the more it learns about their preferences and health needs, enhancing the accuracy and relevance of its recommendations.

• Voice Assistant Integration:

  • Adding voice assistant capabilities for hands-free scanning and feedback.