A.E.G.I.S.
A.E.G.I.S. - Autonomous Elderly Guardian & Intelligent Sensing.
A device-free fall detection system using Wi-Fi CSI and mmWave sensor fusion for privacy-preserving vital monitoring.
The Problem
Falls are a leading cause of injury and death among the elderly. Current detection methods have significant limitations.
Falls per year among elderly globally
Fatal falls annually worldwide
Falls occur in care facilities
Patients forget wearables
Limitations of Current Solutions
Camera Systems
Privacy violations, cannot be used in bathrooms
Wearable Devices
Often forgotten or removed by dementia patients
Pressure Mats
Limited coverage, can be tripped over
Our Solution
A.E.G.I.S. combines Wi-Fi sensing with mmWave radar to provide comprehensive, privacy-preserving elderly monitoring.
Privacy-Preserving
No cameras or images. Only analyzes Wi-Fi signal distortions to detect movement patterns.
Wi-Fi CSI Analysis
Extracts Channel State Information from standard Wi-Fi signals to detect falls.
mmWave Vital Signs
Uses radar sensing to monitor breathing and detect unconsciousness after falls.
Real-Time Alerts
Immediate notification to caregivers when a fall is detected.
Device-Free
No wearables required. Works for patients with dementia who may remove devices.
Continuous Monitoring
Works 24/7 in all lighting conditions, including complete darkness.
How It Works
The system uses a multi-stage pipeline to detect falls and monitor vital signs.
Signal Capture
ESP32 extracts CSI from Wi-Fi signals at 100Hz
Feature Extraction
Raspberry Pi processes signal patterns in real-time
ML Classification
TensorFlow Lite model classifies activity patterns
Alert System
Immediate notification with vital sign status
Technology Stack
Built with carefully selected hardware and software for optimal performance.
ESP32-WROOM-32U
Hardware
Raspberry Pi 5
Processing
LD2410 mmWave
Sensor
Python
Backend
C/C++
Firmware
TensorFlow Lite
ML
Development Timeline
Track the progress of A.E.G.I.S. throughout the academic year.
Hardware Selection & Setup
CSI Data Collection Pipeline
ML Model Development
mmWave Integration
System Testing & Validation
Documentation & Presentation
Follow the Development Journey
Read weekly updates, technical deep-dives, and supervisor meeting logs documenting the entire development process.
Read the BlogThe A.E.G.I.S. Project
Privacy-preserving fall detection for elderly care using cutting-edge sensing technology.
Autonomous Elderly Guardian & Intelligent Sensing
A device-free fall detection system that uses Wi-Fi Channel State Information and mmWave radar to monitor elderly patients without compromising their privacy. No cameras, no wearables - just intelligent signal analysis.
Explore the ProjectPrivacy-First
No cameras or image capture
Wi-Fi CSI
Analyzes signal distortions
mmWave Radar
Vital signs monitoring
ML Powered
Intelligent fall detection