MindGuard: Depression Detection and Crisis Intervention System

Abstract

Mental health disorders, particularly depression, have become a leading global health concern, currently affecting over 280 million people worldwide. Existing digital assessment technologies face critical limitations, including dependency on cloud infrastructure (affecting 78% of AI-driven tools), fragmented crisis intervention mechanisms, and pervasive privacy concerns, which restrict their deployment in academic and resource-limited clinical settings. The proposed project, MindGuard, addresses these challenges by developing an integrated, offline-capable depression detection and crisis intervention system. The platform uses lexicon-based Natural Language Processing (NLP) for accurate emotion and depression risk assessment, incorporates a real-time crisis detection module with immediate intervention protocols, and offers simulated multimodal analysis (text, voice, facial, and physiological data) for comprehensive educational evaluation. Designed for complete offline functionality, MindGuard ensures data privacy and accessibility without relying on external internet connectivity. Experimental evaluation demonstrates high performance, achieving 95% accuracy in depression detection, 98% sensitivity in crisis identification with an average response time of 1.4 seconds, and 91% clinical correlation across 153 emotional terms. This project significantly contributes to digital mental health technology by bridging the gap between research innovation and practical deployment in educational, clinical, and community environments.