An Integrated Depression Risk Identification and Crisis Intervention Model with Multimodal Analysis

Abstract

Depression has emerged as a major global health challenge, impacting more than 280 million individuals and creating severe social, economic, and psychological consequences. Existing digital assessment tools are often cloud-dependent, fragmented, and limited by high infrastructure costs, privacy concerns, and lack of integrated crisis management features. To overcome these challenges, the proposed research presents an integrated, offline-capable depression risk identification and crisis intervention system built using a lightweight NLP-based framework. The system performs real-time text analysis through a clinically validated emotion lexicon, detects potential depressive tendencies, and triggers immediate crisis-response protocols when high-risk indicators appear. It also provides simulated multimodal analysis—including text, voice, facial, and physiological cues—to enhance educational and research applications without compromising data privacy. Experimental validation demonstrates 95 % accuracy in depression detection, 98 % sensitivity for crisis identification, and 91 % correlation with clinical standards, while maintaining an average response time of 1.4 seconds. The platform operates entirely offline, ensuring full data confidentiality and suitability for academic institutions, rural healthcare facilities, and research environments. By integrating assessment, intervention, and reporting within a single privacy-preserving framework, the project bridges the gap between research innovation and practical deployment, offering a scalable, ethical, and accessible solution for global mental-health support.