MaterCare Homes Server

The "Grandma Test" passed - No smartphone required. Passive monitoring for elderly that informs caregivers.

What is MaterCare Homes?

MaterCare Homes is an AI-powered eldercare platform designed for the 80% of seniors who don't use smartphones. It combines:

• 🤖 Agentic AI - Autonomous decision-making for eldercare
• 📄 OCR - Scan prescriptions, medical documents
• 📚 RAG - Healthcare knowledge retrieval
• 📡 IoT Sensors - Passive monitoring (mmWave, PIR, door sensors)
• 🔔 Alerts - SMS/call to caregivers

The Problem We Solve

Traditional Eldercare Tech	MaterCare
Senior needs smartphone	Senior does NOTHING
Wearable required	Passive sensors
App complexity	Caregiver uses app
Reactive alerts	Proactive detection
Cloud-only	Edge processing

Features

1. AI Assistant

• Fine-tuned Llama for eldercare
• Answers: dementia, fall prevention, medications, nutrition
• Available via: API, MCP, Voice (Alexa/Google Home)

2. Care Plan Generator

• Personalized plans based on conditions
• Daily routines, medications, safety
• Emergency protocols

3. Passive Monitoring

• mmWave Radar - Fall detection, vital signs
• PIR Motion - Activity levels
• Door Sensors - Wandering detection
• Pressure Mats - Bed/chair occupancy

4. Alert System

• Real-time SMS/call to caregivers
• Severity-based routing
• Escalation protocols

5. Knowledge Base

• CDC, NIH guidelines
• Drug interactions
• Emergency protocols
• Custom source addition

Quick Start

Installation

pip install matercare-homes

Python Usage

from matercare import MaterCareLLM, SensorGateway, KnowledgeBase

# Chat with eldercare AI
llm = MaterCareLLM()
response = llm.chat("What are signs of dehydration in elderly?")
print(response)

# Set up sensors
gateway = SensorGateway("senior_01")
gateway.register_sensor("mmwave_01", "mmwave")
gateway.register_sensor("door_01", "door")

# Query knowledge base
kb = KnowledgeBase()
results = kb.retrieve("fall prevention")

API Server

# Run API
matercare-api

# Or programmatically
from matercare.src.api import app
import uvicorn
uvicorn.run(app, port=8000)

MCP Server (For AI Agents)

# Run MCP server
matercare-mcp

# Now connect Claude Code, Cursor, etc.

Architecture

┌─────────────────────────────────────────────────────────────────┐
│                    MATERCARE HOMES                               │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│   ┌──────────────┐     ┌──────────────┐     ┌──────────────┐  │
│   │   PASSIVE    │     │   AGENTIC    │     │   ALERT      │  │
│   │   SENSORS    │────▶│   AI CORE    │────▶│   SYSTEM      │  │
│   │              │     │              │     │              │  │
│   │  • mmWave    │     │  • OCR       │     │  • SMS       │  │
│   │  • Motion    │     │  • RAG       │     │  • Call      │  │
│   │  • Door      │     │  • LLM       │     │  • Push      │  │
│   └──────────────┘     └──────────────┘     └──────────────┘  │
│                                                                 │
│   ┌─────────────────────────────────────────────────────┐    │
│   │              MCP CONNECTOR (Plug & Play)            │    │
│   │  • Claude Code  • Cursor  • Copilot  • CrewAI    │    │
│   └─────────────────────────────────────────────────────┘    │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

Integration

Connect to Any AI Agent

from matercare.src.mcp import MaterCareMCP, MCPRequest

mcp = MaterCareMCP()

# Works with Claude Code, Cursor, Copilot, etc.
response = mcp.handle(MCPRequest(
    method="chat",
    params={"message": "Elder care advice"}
))

REST API

# Chat
curl -X POST http://localhost:8000/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Fall prevention tips"}'

# Care plan
curl -X POST http://localhost:8000/care-plan \
  -H "Content-Type: application/json" \
  -d '{"patient_name": "John", "conditions": ["diabetes"], "mobility": "ambulatory", "cognitive_status": "alert"}'

# Sensors
curl http://localhost:8000/sensors/status

Add Custom Knowledge

from matercare import KnowledgeBase, KnowledgeSource

kb = KnowledgeBase()
kb.add_source(KnowledgeSource(
    name="Custom Hospital Protocol",
    content="Our emergency protocol for...",
    source_type="manual"
))

6-Phase Care Loop Orchestrator

MaterCare features a novel 6-phase orchestration that no competitor has:

Phase 1: SENSE - Collect all data sources

• IoT sensor data (mmWave, PIR, door)
• Voice input
• Documents/prescriptions
• Historical care data

Phase 2: THINK - Multi-agent analysis

• TriageAgent: Overall condition assessment
• MedicationAgent: Drug interactions & adherence
• VitalAgent: Heart rate, breathing, temperature
• CognitiveAgent: Mental status evaluation
• ActivityAgent: Daily patterns
• SocialAgent: Engagement monitoring
• EmergencyAgent: Critical condition detection
• NutritionAgent: Dietary needs

Phase 3: PLAN - Generate care recommendations

Synthesize all agent analyses into actionable recommendations.

Phase 4: ACT - Execute actions

• Send alerts
• Update care plans
• Trigger interventions

Phase 5: LEARN - Feedback loop

Learn from outcomes to improve future recommendations.

Phase 6: REPORT - Notify stakeholders

• Family members
• Caregivers
• Healthcare providers

Using the Orchestrator

from matercare.src.orchestration import MaterCareOrchestrator
from matercare.src.orchestration.agents import get_care_agent

# Create orchestrator
orchestrator = MaterCareOrchestrator()

# Register care agents
orchestrator.register_agent("triage_agent", get_care_agent("triage"))
orchestrator.register_agent("medication_agent", get_care_agent("medication"))
orchestrator.register_agent("emergency_agent", get_care_agent("emergency"))
orchestrator.register_agent("vital_agent", get_care_agent("vital"))
orchestrator.register_agent("cognitive_agent", get_care_agent("cognitive"))

# Execute care loop
result = await orchestrator.care_loop("senior_123", {
    "sensors": {
        "motion": True,
        "fall": False,
        "heart_rate": 72,
        "temperature": 36.5
    },
    "voice": "I'm feeling tired today"
})

print(f"Priority: {result.priority}")
print(f"Recommendation: {result.recommendation}")
print(f"Actions: {result.actions}")

MCP Server for External Agents

The MCP server exposes MaterCare to external AI agents:

# Run MCP server
python -m matercare.src.orchestration.mcp_server

# Or run directly
python matercare/src/orchestration/mcp_server.py

Available tools:

• care_loop - Execute full 6-phase care loop
• assess_senior - Get comprehensive assessment
• check_emergency - Check for emergencies
• review_medications - Review drugs for interactions
• register_senior - Register new senior
• notify_family - Send family notifications
• get_knowledge - Query knowledge base
• get_care_history - Get historical data

Connect to TAURUS Platform MCPs

from matercare.src.orchestration.integrations import create_connector

# Create connector to TAURUS MCPs
connector = await create_connector()

# Use MCP bridge for eldercare-specific operations
bridge = MaterCareMCPBridge(connector)

# Notify family via email, SMS, WhatsApp, Slack
await bridge.notify_family(
    senior_name="John Smith",
    message="Fall detected - please check in",
    priority="urgent",
    channels=["email", "sms", "whatsapp"]
)

# Schedule caregiver visit
from datetime import datetime
await bridge.schedule_caregiver_visit(
    senior_name="John Smith",
    caregiver_name="Mary",
    scheduled_time=datetime(2026, 2, 28, 10, 0),
    notes="Regular wellness check"
)

Hardware Setup

Recommended Sensors

Sensor	Purpose	Cost
HLK-LD2410 mmWave	Fall detection, vitals	$30
HC-SR501 PIR	Motion detection	$5
RC-51 Door	Wandering detection	$5
Pressure Mat	Bed/chair occupancy	$25

Raspberry Pi Setup

# Install
pip install matercare-homes

# Run sensor gateway
python -m matercare.sensors.gateway --senior-id "dad"

Environment Variables

# .env
MATERCARE_MODEL=Taurus-AI-Corp/matercare-llama-3.2-3b
HUGGINGFACE_API_TOKEN=your_token
TWILIO_ACCOUNT_SID=your_sid
TWILIO_AUTH_TOKEN=your_token
TWILIO_PHONE_NUMBER=+1234567890
ALERT_PHONE_NUMBER=+0987654321
DATABASE_URL=postgresql://...

Documentation

• Implementation Plan
• Tech Stack Research
• API Reference
• Sensor Integration

Roadmap

• V1.0 - Core AI + RAG + Sensors
• V1.1 - Voice integration (Alexa/Google)
• V1.2 - Mobile caregiver app
• V2.0 - Enterprise multi-tenant
• V2.1 - Hardware companion device

License

MIT License - see LICENSE

Author

TAURUS AI Corp - Quantum-Resistant Fintech & Eldercare Platform

• Website: https://q-grid.taurusai.io
• GitHub: https://github.com/Taurus-AI-Corp
• HuggingFace: https://huggingface.co/Taurus-AI-Corp

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Made with ❤️ for our grandparents