MINDGUARD: Offline AI-Based Mental Health Assessment System with Visual Analytics and Crisis Intervention Support

Abstract

Mental health issues such as stress, anxiety, and depression are increasing rapidly, yet early identification remains limited due to social stigma, lack of accessibility, and privacy concerns in existing online systems. This work presents MindGuard, an offline artificial intelligence-based system designed to analyze user-generated text and provide mental health assessment along with visual insights and crisis intervention support. The system focuses on practical usability by combining text analysis, emotion detection, wellness evaluation, and interactive dashboards within a single platform. The system operates through a structured workflow where user input is processed using natural language processing techniques to detect emotional patterns and compute a depression risk score. In addition, the platform integrates multiple assessment methods, including text-based analysis, simulated voice evaluation, facial expression input, and heart rate indicators, to provide a more comprehensive understanding of mental health conditions. A built-in crisis detection module identifies high-risk expressions and immediately provides support resources such as helpline recommendations. The implementation emphasizes a user-friendly interface with real-time dashboards, analytics panels, and interactive modules that allow users to monitor their emotional state over time. The system is designed to function entirely offline using local storage, ensuring that sensitive user data remains secure and private. Overall, MindGuard demonstrates how an AI-driven system can be transformed into a practical application with visual feedback, modular assessment tools, and immediate support mechanisms, making it suitable for academic environments, counseling centers, and offline deployment scenarios.