MindGuard: A Web-Based Mental Health Tracking Platform with Conversational Support and Research-Ready Data Collection

The online mental health surveillance system functions as a useful tool which enables early problem detection and assists users in understanding their mental state. The system functions at scale because it makes therapy accessible to people who cannot receive therapy at all times. I developed MindGuard as a prototype web-based system which integrates mood tracking and stress logs with an AI chat feature that provides users daily reflective conversations. Users can write down their emotional state and their observed behaviors before they begin the guided chat sessions. The guide in there maintains the session for its entire duration. Interactive analytics on the dashboards display well-being trends through their interactive features which enable users to track changes in their well-being over different periods. The system functions as a self-assessment tool which facilitates monitoring of personal progress. MindGuard required researchers to create its design as a research-ready system. The system automatically anonymizes and encrypts all data which allows studies to use exported data without violating privacy and ethical standards. The section becomes necessary because mental health data requires special protection. The project uses React for its frontend development and Flask with MySQL for its backend development which contains the database system. The system functions at a satisfactory level. MindGuard functions as a self-observation instrument which researchers can use to gather mental health data while maintaining user privacy. The system shows solid basic functions but I need to test its capacity for scaling in actual operations.