Lightweight Deep Learning Model for Radar-Based Fall Detection With Metric Learning

The radar-based fall detection system has grown in popularity because of its stability and privacy protection. Deep neural networks have been used in previous radar-based fall detection systems to improve detection accuracy. However, most of them need a lot of memory and have high computational complexity, making them impractical for Internet of Things (IoT) devices. In this article, we propose an extremely lightweight network named Tiny-RadarNet for extracting characteristics from raw data. Unlike traditional neural networks, we use a unique parallel 1-D depthwise convolutions structure as the core module to eliminate the need for standard convolutions, and achieve significant parameter reduction. Furthermore, instead of regarding fall detection as a classification problem, we use our metric learning technique to treat it as a matching problem for better distinction of embeddings. Finally, we introduce a novel dual loss function to improve the proposed network's robustness against unobserved human motions without the need for additional anchors or expensive computation. The experimental results reveal that the proposed method can achieve comparable fall detection accuracy compared with state-of-art methods but with much fewer weight parameters and lower computational complexity. These findings suggest that a low-power and low-latency fall detection solution for IoT applications is achievable.

Automated summary

In this article, we propose a lightweight network called Tiny-RadarNet for extracting features from raw data. Unlike traditional neural networks, we use a unique parallel 1-D depthwise convolutions structure to eliminate the need for standard convolutions and achieve significant parameter reduction. Furthermore, we treat fall detection as a matching problem using metric learning technique and introduce a dual loss function to improve the network's robustness against unobserved human motions.