MaterCare Homes

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

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

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

Made with ❤️ for our grandparents