MoodBuddy

Inspiration

The importance of mental health is undeniable, yet many people lack the tools to understand and manage their emotions effectively. We wanted to create a solution that empowers individuals to prioritize their well-being by making mental health tracking simple, insightful, and accessible.

What it does

MoodBuddy enables users to:

1. Log their daily moods using an intuitive interface.
2. Track activities like sleep, exercise, and mindfulness to identify patterns.
3. Receive AI-generated insights and suggestions to improve mental well-being.
4. Visualize progress with interactive charts and reports.

How we built it

1. Frontend: Built with React Native/Flutter for a seamless cross-platform experience.
2. Backend: A Python-based REST API powered by Flask for handling data securely.
3. AI & Insights: Integrated machine learning models for sentiment analysis and trend detection using libraries like spaCy and scikit-learn.
4. Database: Used Firebase for real-time storage and synchronization.

Challenges we ran into

1. Building a robust AI model that accurately correlates mood and activity data.
2. Ensuring the app remains intuitive while providing in-depth insights.
3. Addressing privacy concerns and encrypting user data securely.
4. Managing data visualization for large datasets without overwhelming the user.

Accomplishments that we're proud of

1. Successfully implementing AI to deliver personalized mental health insights.
2. Designing a clean, user-friendly interface that simplifies mental health tracking.
3. Building a secure platform that prioritizes user privacy and data safety.

What we learned

1. The complexity of integrating AI to deliver meaningful, personalized insights.
2. The importance of user-centric design for mental health applications.
3. Effective strategies for ensuring privacy and security in a sensitive domain like mental health.
4. The value of teamwork and collaboration to bring an impactful idea to life.

What's next for MoodBuddy

As an improvement for MoodBuddy, we are in progress with integrating facial emotion recognition to enhance mood tracking with visual data.

Built With

• css3
• html5
• javascript
• python
• textblob