Allows CrewAI agents to programmatically access eldercare monitoring data and healthcare knowledge bases for autonomous decision-making.
Integrates with Google Home for voice-activated eldercare assistance and passive monitoring alerts.
Supports the storage and retrieval of patient information, sensor activity logs, and emergency protocols in a PostgreSQL database.
Facilitates the deployment of sensor gateways on Raspberry Pi to collect and process data from passive monitoring hardware like mmWave radar and PIR motion detectors.
Powers the platform's alert system by sending real-time SMS and phone call notifications to caregivers via Twilio's communication API.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MaterCare Homes MCP ServerGenerate a personalized care plan for a senior with mild dementia"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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:
π€ Agentic AI - Autonomous decision-making for eldercare
π OCR - Scan prescriptions, medical documents
π RAG - Healthcare knowledge retrieval
π‘ IoT Sensors - Passive monitoring (mmWave, PIR, door sensors)
π 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-homesPython 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
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β MATERCARE HOMES β
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ€
β β
β ββββββββββββββββ ββββββββββββββββ ββββββββββββββββ β
β β PASSIVE β β AGENTIC β β ALERT β β
β β SENSORS ββββββΆβ AI CORE ββββββΆβ SYSTEM β β
β β β β β β β β
β β β’ mmWave β β β’ OCR β β β’ SMS β β
β β β’ Motion β β β’ RAG β β β’ Call β β
β β β’ Door β β β’ LLM β β β’ Push β β
β ββββββββββββββββ ββββββββββββββββ ββββββββββββββββ β
β β
β βββββββββββββββββββββββββββββββββββββββββββββββββββββββ β
β β MCP CONNECTOR (Plug & Play) β β
β β β’ Claude Code β’ Cursor β’ Copilot β’ CrewAI β β
β βββββββββββββββββββββββββββββββββββββββββββββββββββββββ β
β β
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββIntegration
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/statusAdd 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.pyAvailable tools:
care_loop- Execute full 6-phase care loopassess_senior- Get comprehensive assessmentcheck_emergency- Check for emergenciesreview_medications- Review drugs for interactionsregister_senior- Register new seniornotify_family- Send family notificationsget_knowledge- Query knowledge baseget_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
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
HuggingFace: https://huggingface.co/Taurus-AI-Corp