Mental Wellness Companion with AI Mood Analysis
Investment
$89 CAD
Course structure
Development Stages
- User authentication and secure data storage setup
- Natural language processing integration for journal analysis
- Mood tracking interface with data visualization components
- AI model training using sentiment analysis datasets
- Personalized recommendation engine implementation
- Crisis detection and resource directory integration
- Privacy compliance and data encryption protocols
- Beta testing with focus on user experience refinement
Students receive mentorship from clinical psychology advisors to ensure ethical AI application in mental health contexts.
Technical Stack Overview
React Native for cross-platform development, TensorFlow Lite for on-device ML, Firebase for backend services, and Python for model training.
What you'll build
This project focuses on building a mental wellness application that uses AI to recognize emotional patterns from user inputs. The app analyzes daily mood entries, journal text, and behavioral data to identify triggers and suggest coping strategies.
Students work with natural language processing to interpret journal entries and sentiment analysis to track emotional fluctuations over time. The interface includes a chat feature where users receive AI-generated responses based on cognitive behavioral therapy principles.
The development process covers data privacy considerations, secure storage of sensitive information, and ethical AI implementation. Students learn to balance helpful intervention with appropriate boundaries, ensuring the app suggests professional help when patterns indicate serious concerns.
Included in this program
By the numbers
67
Code samples
14
Integration patterns
8
AI model types
22
Technique demonstrations