Radar-Based Non-intrusive Fall Motion Recognition using Deformable Convolutional Neural Network

Abstract

Radar is an attractive sensing technology for remote and non-intrusive human health monitoring and elderly fall detection due to its ability to work in low lighting conditions, its invariance to the environment, and its ability to operate through obstacles. Radar reflections from humans produce unique micro-Doppler signatures that can be used for classifying human activities and fall motion. However, radar-based elderly fall detection need to handle the indistinctive inter-class differences and large intra-class variations of human fall-motion in a real-world situation. Further, the radar placement in the room and varying aspect angle of the falling subject could result in differing radar micro-Doppler signature of human fall-motion. In this paper, we use a compact short-range 60-GHz frequency modulated continuous wave radar for detecting human fall motion using a novel deformable deep convolutional neural network with novel 1-class contrastive loss function in conjunction to focus loss to recognize elderly fall and address several of these signal processing system challenges. We demonstrate the performance of our proposed system in laboratory conditions under staged fall motion.