SNO-Relax: An AI-Based Mental Health Support Framework

Abstract

Mental health challenges such as stress, anxiety, and depression are increasing globally, while access to conventional mental health care remains limited due to stigma, cost, and shortages of trained professionals [25],[12]. This paper presents SnoRelax, an AI-driven framework for digital mental health support that integrates machine learning (ML) and natural language processing (NLP) for context-aware and empathetic interaction. The proposed system follows a structured Software Development Life Cycle (SDLC), incorporating a five-layer modular architecture, a Hybrid Empathy Model (HEM), privacy-preserving design, and a therapist escalation mechanism. Prototype-level testing demonstrates the system\'s ability to detect emotional states, generate empathetic responses, and escalate high-risk cases. SnoRelax provides a structured reference architecture for scalable, ethical AI-based mental health support.

Introduction

The paper presents SnoRelax, an AI-powered digital mental health support system designed to provide personalized stress management and relaxation interventions. It addresses the growing global prevalence of anxiety, stress, and depression, while overcoming barriers such as stigma, cost, and limited access to mental health professionals. SnoRelax integrates Machine Learning (ML), Natural Language Processing (NLP), emotion modeling, and conversational AI to deliver adaptive, empathetic, and privacy-preserving support.

The system was developed using a structured Software Development Life Cycle (SDLC) approach, including requirements analysis, system design, model development, implementation, testing, and deployment. Its core intelligence is the Hybrid Empathy Model (HEM), which combines an LSTM network for tracking emotional trends with a Transformer-based model for context-aware dialogue generation. The framework supports multimodal inputs such as text, speech, and handwritten notes (via OCR), enabling more inclusive and personalized interactions.

SnoRelax employs a five-layer architecture consisting of: User Interaction, Data Processing, AI & Analytics, Privacy & Security, and Therapist & Escalation layers. Security is ensured through AES-256 encryption, OTP authentication, and anonymized user identifiers, while future enhancements include Federated Learning for privacy-preserving model updates. When severe emotional distress or self-harm indicators are detected, the system can notify therapists or connect users to emergency support services.

Prototype evaluation demonstrated promising results, achieving approximately 82% emotion classification accuracy, an average empathy score of 4.1/5, and 90% precision in identifying high-risk cases requiring escalation. Compared to existing mental health chatbots, SnoRelax offers stronger personalization, improved privacy protection, better empathy modeling, and enhanced clinical support mechanisms.

The framework has applications in personal wellness, clinical support, education, corporate wellness programs, telehealth, and wearable-device integration. Future work focuses on explainable AI, multilingual support, wearable sensor integration, cross-cultural empathy modeling, and decentralized learning technologies to further improve accessibility, transparency, and user trust.

Conclusion

This paper presented SnoRelax, a modular AI-powered framework for personalized mental health support developed through a structured SDLC process. The five-layer architecture integrates LSTM-based sequential emotion analysis, Transformer-driven contextual understanding, AES-256 privacy protection, and a therapist escalation mechanism. Prototype-level evaluation demonstrated 82% emotion classification accuracy, an average empathy score of 4.1/5, and 90% escalation precision. Comparative analysis confirms that SnoRelax substantially outperforms existing systems across accessibility, personalization, privacy, empathy modeling, engagement, and clinical validity. Future research will focus on cross-cultural validation, multilingual NLP, federated learning, and clinical benchmarking to enhance global applicability.

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Copyright

Copyright © 2026 Suryakant Mishra, Shivam Kumar Dubey, Kavya Singh, Ratan Rajan Srivastava. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.