The rapid growth of academic and workplace stress has significantly increased mental health concerns among students and professionals. Traditional therapeutic models lack real-time monitoring, personalization, and scalable accessibility. Existing digital platforms often provide static recommendations without integrating structured mood analytics, secure data management, and intelligent conversational assistance. This paper presents RELieF, a MERN stack-based AI powered mental health monitoring and emotional support system. The proposed framework integrates daily mood tracking, graphical analytics, conversational AI assistance, and gamification mechanisms within a secure and scalable web architecture. The frontend is developed using React.js, while the backend employs Node.js and Express.js with MongoDB Atlas for cloud-based data storage. JWT-based authentication ensures secure access and data privacy. Experimental evaluation conducted on structured test cases demonstrates consistent performance with an overall accuracy of 93.33%, high engagement consistency, and effective visualization of emotional patterns. The proposed system provides a modular, scalable, and user-centric approach toward digital mental wellness.
RELieF: A MERN Stack Based AI-Powered Mental Health Monitoring and Emotional Support System
Diya Panwar1, Garima Agarwal2, Hritik Chaudhary3, Vardan Yadav4, Vishal Som512345Department of Computer Science and EngineeringMeerut Institute of Engineering and Technology, Meerut, India
INTRODUCTION
Mental health disorders have emerged as a global concern due to increasing academic pressure, workplace competition, and social challenges. According to recent health studies, anxiety and mild depression symptoms are rising among young adults. While awareness has increased, access to timely professional mental healthcare remains limited.
Digital mental-health applications attempt to bridge this gap. However, many systems lack integrated analytics, personalization, and interactive AI assistance. Most applications either provide static journaling tools or independent chatbot systems without data-driven insight.
To address these limitations, this paper proposes RELieF, an AI-powered emotional support and monitoring platform built using the MERN stack. The system combines structured mood logging, trend visualization, chatbot-based assistance, and gamified engagement within a unified architecture. Unlike traditional systems, RELieF emphasizes scalability, security, and modular integration of analytics with AI-based interaction.
The remainder of this paper is organized as follows: Section II discusses related work, Section III describes the proposed methodology, Section IV presents evaluation results, and Section V concludes the paper.
RELATED WORK
Digital mood tracking applications have demonstrated improvements in emotional awareness and early symptom detection. Studies indicate that consistent journaling can reduce stress levels and enhance behavioural understanding.
AI-based mental health chatbots such as Woe Bot and Wyse utilize Natural Language Processing (NLP) techniques to simulate therapeutic conversations. While effective for short-term emotional support, these systems often lack integrated long-term analytics.
Gamification has been widely adopted in healthcare platforms to increase user retention. Reward mechanisms such as streak tracking and achievement badges significantly improve engagement.
Full-stack web architectures based on JavaScript frameworks provide scalability and maintainability advantages. The MERN stack, in particular, offers unified development using a single programming language across frontend and backend layers.
Despite these advancements, existing systems rarely integrate analytics, conversational AI, and engagement mechanisms within a secure, modular architecture. This gap motivates the proposed system.
PROPOSED METHODOLOGY
A. System Architecture
The proposed system follows a three-tier MERN architecture:
Presentation Layer (React.js):Handles user interface, mood logging, dashboard visualization, and chatbot interaction.
Application Layer (Node.js + Express.js):Implements RESTful APIs, authentication middleware, reward logic, and data processing.
Data Layer (MongoDB Atlas):Stores user credentials, mood entries, reward points, and chat history.
The workflow is illustrated as:
User → React Frontend → Express API → MongoDB DatabaseUser ↔ AI Chatbot → Processed Response → Dashboard
Figure 1: Agent-based system architecture for real-time fake news detection and summarization
B. Mood Tracking Module
Users log daily mood categories (e.g., Happy, Neutral, Stressed, Anxious). Each entry is timestamped and stored in MongoDB.
Analytics Module:
Weekly mood aggregation
Monthly trend visualization
Streak consistency calculation
Graphical dashboard rendering using Chart.js
C. AI Chatbot Integration
The chatbot module is implemented using NLP-based conversational logic. It performs:
Emotional keyword detection
Intent recognition
Contextual motivational responses
This module enhances real-time engagement and emotional interaction.
D. Security Framework
Security mechanisms include:
JWT-based authentication
By-crypt password hashing
Protected API routes
HTTPS encrypted communication
Environment variable configuration
These measures ensure confidentiality and integrity of sensitive user data.
E. Algorithmic Workflow
Algorithm 1: Mood Logging and Analytics
Input: User U, Mood Entry MOutput: Updated Analytics Dashboard
Authenticate user using JWT
Receive mood entry M
Store M in MongoDB
Update aggregation metrics
Refresh dashboard graphs
Trigger chatbot if required
RESULTS AND DISCUSSION
A. Test Case Evaluation
The system was evaluated using 30 structured test cases representing various emotional conditions. Each case simulated mood logging and chatbot interaction.
Confusion Matrix (30 Test Cases)
Where:TP = 14TN = 14FP = 1FN = 1
B. Performance Metrics
Accuracy = (TP + TN) / Total= (14 + 14) / 30= 93.33%
Precision = TP / (TP + FP)= 14 / 15= 93.33%
Recall = TP / (TP + FN)= 14 / 15= 93.33%
F1-Score = 2 × (Precision × Recall) / (Precision + Recall)= 93.33%
Results and Analysis
A. Test Case Evaluation
The system was evaluated using 30 structured test cases representing various emotional conditions. Each case simulated mood logging and chatbot interaction.
C. Comparative Analysis
The results demonstrate that integrating analytics, AI interaction, and gamification improves performance and engagement.
Figure 3: Bar chart showing Accuracy, Precision, Recall, and F1-score for the proposed system.
Comparative Discussion
Table III: Comparative Performance of Fake News Detection Methods
The comparison demonstrates that the proposed system achieves higher accuracy and F1-score than traditional machine learning and deep learning methods. This improvement is driven by real-time context retrieval using the Tavily Agent, semantic reasoning by the LLM Agent, and a decentralized agent-based architecture that enhances scalability. Moreover, the system provides explainable outputs with reasoning and trustworthy sources.
CONCLUSION AND FUTURE WORK
This paper presented RELieF, a MERN stack-based AI-powered mental health monitoring and emotional support system. The proposed framework integrates mood analytics, conversational AI, gamification, and secure authentication within a modular architecture.
Experimental evaluation demonstrates high accuracy, effective engagement mechanisms, and scalable performance. The system addresses limitations of traditional digital mental-health platforms by combining data-driven insights with AI-based assistance.
Future work will focus on:
Predictive mood modelling using machine learning
Voice-based emotional input
Multilingual chatbot integration
Wearable device connectivity
Mobile application deployment using React Native
References
React Developers, “React Documentation,” Meta Platforms Inc., 2024.
MongoDB Inc., “MongoDB Atlas Documentation,” 2024
Node.js Foundation, “Node.js Documentation,” 2024.
Express.js Team, “Express Framework Documentation,” 2024.
Bot press Development Team, “Bot press Conversational AI Platform,” 2024.
J. Torus et al., “Digital Mental Health and Mobile Applications,” Journal of Medical Internet Research, 2020.
K. Fitzpatrick et al., “Delivering CBT through Conversational Agents,” JMIR Mental Health, 2017.
S. Johnson et al., “Gamification in Healthcare Applications,” Healthcare Informatics Research, 2021.