International Journal of Technology and Emerging Research
DOI: 10.64823/ijter.2604031
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.
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.
A. System Architecture
The proposed system follows a three-tier MERN architecture:
The workflow is illustrated as:
User → React Frontend → Express API → MongoDB Database
User ↔ 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:
C. AI Chatbot Integration
The chatbot module is implemented using NLP-based conversational logic. It performs:
This module enhances real-time engagement and emotional interaction.
D. Security Framework
Security mechanisms include:
These measures ensure confidentiality and integrity of sensitive user data.
E. Algorithmic Workflow
Algorithm 1: Mood Logging and Analytics
Input: User U, Mood Entry M
Output: Updated Analytics Dashboard
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)
Predicted Positive | Predicted Negative | |
|---|---|---|
Actual Positive | 14 | 1 |
Actual Negative | 1 | 14 |
Where:
TP = 14
TN = 14
FP = 1
FN = 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%
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
System | Accuracy | F1-Score |
|---|---|---|
Traditional Mood App | 85% | 84% |
Chatbot-only System | 88% | 87% |
Gamified Tracker | 90% | 89% |
Proposed RELieF | 93.33% | 93.33% |
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.
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.
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: