Burnout Sentinel

An Early Warning System for Student Burnout Using Workload and Recovery Indicators

Overview - Live Demo & Presentation - Features - Quick Start - Project Structure - Roadmap

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Table of Contents

• Overview
• Live Demo & Presentation
• Project Status
• Features
• How It Works
• Quick Start
• Project Structure
• Tech Stack
• Research Question
• Roadmap
• Contributing
• License
• Privacy Policy

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Live Demo & Presentation

• 🚀 Live Web Application: https://burnout-sentinel-omega.vercel.app/
• 🎥 Video Demo & Presentation: YouTube Link
• ⚙️ API Documentation (Swagger UI): https://burnout-sentinel-omega.vercel.app/docs
• 🩺 API Health Check: https://burnout-sentinel-omega.vercel.app/health

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Overview

Burnout Sentinel is a student wellness project focused on helping students detect overload early and rebalance their week before stress becomes burnout.

Instead of acting like a basic to-do list, the app combines planning inputs with explainable risk scoring, personalized recommendations, and trend tracking.

The current implementation is an MVP prototype with a polished UI, backend analysis API, and competition-ready demo flow.

Project Status

• Project title: Burnout Sentinel
• Research subtitle: An Early Warning System for Student Burnout Using Workload and Recovery Indicators
• Version: 0.4.0
• Last updated: April 19, 2026
• Scope: MVP prototype
• Current focus: frontend experience, research feed, and explainable burnout analysis

Features

Core Planner

• Weekly workload and recovery input form
• Preset weeks (Balanced, Heavy, Overloaded)
• Live workload summary while editing
• Collapse/expand controls for planner sections and analysis panels

Analysis and Guidance

• Burnout risk score (0-100) with Low/Moderate/High labels
• Explainable score breakdown and contributing factors
• What-if simulation for schedule adjustments
• Personalized recommendation generation

Visualization and Interaction

• Workload snapshot metrics
• Risk trend chart with saved snapshots
• Drag-and-drop panel reordering
• State-aware UI feedback for preset and risk interactions

Research and Product Experience

• Research Signal page with external links and infinite scrolling
• Help popup for formula and usage instructions
• Lightweight cookie-session login/signup flow

How It Works

1. Student enters weekly workload and recovery indicators.
2. Frontend validates payload and posts to POST /api/v1/analyze.
3. Backend computes explainable risk and recommendations.
4. Frontend renders risk summary, breakdown, what-if panel, and trend insights.
5. If backend is unavailable, frontend can fall back to local analyzer logic.

Quick Start

Backend

For full setup, tests, and troubleshooting, see backend/README.md.

cd /Users/dominhduy/Documents/Playground/burnout-sentinel/backend
source .venv/bin/activate
python3 -m uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

Backend endpoints:

• Health: http://localhost:8000/health
• Docs: http://localhost:8000/docs

Frontend

cd /Users/dominhduy/Documents/Playground/burnout-sentinel/frontend
cp .env.example .env.local
npm install
npm run dev

Frontend app:

• http://localhost:3000

Vercel Deployment

See docs/vercel-deployment.md for monorepo/Vercel setup.

Project Structure

• frontend/: Next.js application (UI, client logic, API route bridge)
• backend/: FastAPI burnout scoring and recommendation API
• backend/README.md: backend run/test/troubleshooting guide
• docs/: proposal, pitch notes, build plan, deployment notes
• ml/: future machine-learning workspace
• shared/: future shared schemas/constants/prompts

Tech Stack

• Frontend: Next.js, React, TypeScript, Tailwind CSS
• Forms/Validation: React Hook Form, Zod
• Charts: Recharts
• Backend: FastAPI, Pydantic
• Modeling approach: explainable rules-based risk scoring (ML-ready architecture)
• Deployment target: Vercel (frontend) + Render/Railway/Fly/Azure (backend)

Research Question

Can a machine learning-supported planning tool help students identify overload and reduce burnout risk by providing personalized weekly planning recommendations?

Roadmap

• Replace explainable rules with trained model once dataset is available
• Add persistence for long-term schedule and trend history
• Expand recommendation quality and personalization
• Add optional account system for multi-device continuity

Contributing

Contributions are welcome! Please read our CONTRIBUTING.md guide for details on how to set up the development environment, coding standards, and submit pull requests.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Privacy Policy

We respect your data privacy. Please read our Privacy.md file to understand how we process planner inputs and cookies.