In Progress

Fall Detection System

Privacy-preserving mmWave radar and deep learning to detect falls in real time

Overview

In response to the growing elderly population and associated fall risks, we developed a radar-based fall detection system using mmWave radar and machine learning to detect falls in real time. Our system employs deep learning models like CNNs and RNNs to accurately monitor falls by learning complex features from radar data. Our survey emphasizes the effectiveness of Micro-Doppler, Range-Doppler, and Range-Doppler-Angle techniques in ensuring privacy and detecting falls through obstructions, while also addressing challenges such as unpredictability and the lack of realistic fall data.

Key Highlights

Elderly Care
Patient Monitoring
Privacy Preservation
Obstruction Detection
Real-time Processing

Methodology

Supervised learning on radar micro-Doppler and range-Doppler features with temporal modeling using RNN variants for fall sequence detection. Hybrid Variational RNN AutoEncoder for improved accuracy.