ElderEase – caring for them, one step at a time.

🧠 Inspiration

With the growing elderly population, many individuals live alone without continuous medical supervision. Delayed detection of health issues such as low oxygen levels or high heart rate can lead to serious consequences. We wanted to build a system that enables real-time monitoring and early intervention to improve elderly safety and independence.

💡 What it does

ElderEase is a real-time elderly health monitoring system that:

• Simulates IoT-based vital data (heart rate, SpO₂, temperature)
• Processes and analyzes health data in real-time
• Detects abnormal conditions using rule-based logic
• Generates alerts for caregivers
• Provides actionable health insights using AI (MedGamma)

The system helps caregivers monitor multiple patients efficiently and respond quickly to potential risks.

⚙️ How we built it

• Node-RED: Simulates IoT devices generating real-time vitals
  
• Backend (Node.js + Express): Processes incoming data, applies validation, and determines health status
  
• MongoDB: Stores patient data, vitals, and alerts
  
• Frontend (React): Displays dashboards for caregivers and patients with real-time updates
  
• AI Integration (MedGamma): Generates intelligent, context-aware medical insights based on vitals
  

🚨 Key Features

• Real-time vitals monitoring
  
• Emergency alert system
  
• Risk scoring and status classification
  
• Notification system
  
• Health insights panel
  
• Scalable architecture for real IoT integration
  

🧩 Challenges we ran into

• Transitioning from Node-RED-based logic to a scalable MERN architecture
  
• Designing real-time data flow between simulation, backend, and frontend
  
• Structuring medical insights in a meaningful and actionable way
  
• Balancing between rule-based logic and AI-based reasoning
  

📚 What we learned

• Designing scalable system architecture for IoT-based applications
  
• Real-time data handling and API integration
  
• Importance of UI/UX in healthcare systems
  
• How AI models can enhance decision-making without full ML pipelines
  

🚀 What's next

• Integration with real IoT wearable devices
  
• Advanced AI-based predictive analytics
  
• Deployment on cloud infrastructure
  
• Expansion for hospital and assisted living use cases
  

Built With

• elevenlabs
• elevenlabs-voice-api
• javascript
• node.js
• python
• raindrop-ai-platform
• raindrop-mcp-server
• raindrop-smartbuckets
• raindrop-smartinference
• react
• smartmemory
• vultr-backend-services
• vultr-cloud
• vultr-object-storage