Care delivered when needed, not constantly imposed.

AgeWell is a relationship-strengthening elderly care platform that replaces intrusive supervision with respectful, autonomy-preserving support. Using computer vision and intelligent automation, it provides timely reminders and alerts caregivers only when genuinely needed—transforming care from constant checking into a partnership built on trust.

• Overview
• Core Philosophy
• Key Features
• Technology Stack
• Getting Started
• Architecture
• Target Users
• Success Metrics
• Contributing
• License

AgeWell addresses the emotional and practical challenges of elderly care by:

• Reducing caregiver stress through automated monitoring and intelligent alerts
• Preserving dignity and autonomy for care recipients via passive, respectful monitoring
• Preventing emergencies with fall detection and automated response
• Improving communication between caregivers, care recipients, and healthcare providers

AgeWell is built on four foundational principles:

• Passive monitoring replaces intrusive check-ins
• Caregivers intervene only when genuinely needed
• Care recipients retain independence in daily routines

• Daily checklists reflect real routines
• Voice reminders are supportive, not disciplinary
• Recipients choose their assistance

• Automated alerts replace mental tracking
• Summaries remove need for repeated explanations
• Emergency handling is decisive, not chaotic

• Background systems handle safety quietly
• Care recipients engage only when they choose to
• No need to demonstrate capability constantly

• Real-time analysis of daily activities (eating, medication, drinking water)
• Automatic dashboard updates
• YOLOv8 object detection + MediaPipe pose estimation
• Window alert notifications for caregivers

• Automatic fall detection with >95% accuracy
• Immediate emergency service alerts
• Instant caregiver notifications
• Event logging to dashboard

• GPS navigation for disoriented users
• Large, accessible button interface
• Voice guidance with turn-by-turn directions
• Emergency "Request Help" button with live location sharing

• Trackable medications, meals, hydration, and activities
• Automatic task completion detection via computer vision
• Real-time progress tracking
• Missed task alerts

• Supportive, encouraging tone (not disciplinary)
• Scheduled + missed task triggers
• Medication safety confirmations
• Example: "It is Saturday dinner. Make sure to take the right pill."

• Auto-generated health reports
• Pattern detection (e.g., skipping meals)
• Medication adherence tracking
• Pre-appointment reports for healthcare providers

• Background GPS monitoring
• Caregiver dashboard integration
• Safe zone alerts
• Privacy-conscious implementation

• Node.js 18+ and npm
• Python 3.9+
• MongoDB Atlas account
• Camera device (minimum 720p resolution)
• Stable internet connection (>5 Mbps)

# Clone the repository
git clone https://github.com/Ducksss/hack4good.git
cd hack4good

# Install frontend dependencies
npm install

# Install Python dependencies
pip install -r requirements.txt

# Configure environment variables
cp .env.example .env
# Edit .env with your MongoDB URI, API keys, etc.

# Start the development server
npm run dev

# In another terminal, start the AI service
python ai_service.py

Create a .env file with the following variables:

MONGODB_URI=your_mongodb_atlas_uri
TWILIO_ACCOUNT_SID=your_twilio_sid
TWILIO_AUTH_TOKEN=your_twilio_token
API_PORT=3000
AI_SERVICE_PORT=5000

flowchart TB
    subgraph Frontend
        A[React Web App]
        B[Patient Dashboard]
        C[Caregiver Dashboard]
    end
    
    subgraph Backend
        D[Node.js API Server]
        E[Python Flask - AI Services]
        F[MongoDB Atlas]
    end
    
    subgraph AI_ML["AI/ML Pipeline"]
        G[YOLOv8 Object Detection]
        H[MediaPipe Pose Estimation]
        I[Tesseract OCR]
    end
    
    subgraph External
        J[Window Alerts]
        K[OpenStreetMap]
        L[Emergency Services]
    end
    
    A --> D
    D --> F
    D --> E
    E --> G
    E --> H
    E --> I
    D --> J
    A --> K
    J --> L

1. Camera captures video stream
2. AI Service processes with YOLOv8/MediaPipe
3. Backend receives event detection
4. MongoDB logs the event
5. Dashboard displays window alert notification

• Conditions: Alzheimer's, dementia, memory decline
• Needs: Simple interface, voice guidance, dignity preservation
• Experience: Low to moderate tech literacy

• Role: Family members, professional caregivers, nursing staff
• Needs: Remote monitoring, instant alerts, activity history
• Experience: Moderate to high tech literacy

• Role: Primary care physicians, geriatric specialists
• Needs: Health trend summaries, behavioral reports
• Experience: Periodic report consumption

• ✅ Reduced fall-related hospitalizations
• ✅ Improved medication adherence
• ✅ Decreased caregiver burnout and anxiety
• ✅ Improved caregiver-recipient relationship quality
• ✅ Maintained dignity and autonomy for care recipients
• ✅ Earlier detection of health decline patterns

• Core fall detection
• Basic caregiver alerts
• Simple patient dashboard
• MongoDB data storage

• Daily task checklists
• Voice reminders
• Caregiver dashboard
• Activity logging

• GPS navigation assistance
• Medical summary generation
• Healthcare provider portal
• Advanced analytics

• Performance optimization
• Mobile app development
• Multi-language support
• Integration APIs

• Encryption: AES-256 at rest, TLS 1.3 in transit
• Authentication: JWT tokens, optional biometric
• Authorization: Role-based access control (RBAC)
• Data Retention: Configurable, default 90 days
• Compliance: HIPAA-ready for US deployment
• Privacy: Local processing when possible, minimal data collection

We welcome contributions! Please see our contributing guidelines for more details.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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

• WHO Fall Prevention Guidelines
• Alzheimer's Association Care Standards
• WCAG 2.1 Accessibility Guidelines
• Open-source community for amazing tools and libraries

• Project Lead: AgeWell Development Team
• Repository: github.com/Ducksss/hack4good
• Documentation: See PRD.md for detailed product requirements

Built with ❤️ for dignity, autonomy, and better care relationships.