Self-attention CNN based indoor human events detection with UWB radar

Abstract

In the era of smart homes and healthcare automation, the ability to accurately monitor and detect indoor human activities is paramount. Ultra-wideband (UWB) radar has emerged as a promising means for event detection, given its non-invasive nature and easy deployment in diverse environments. However, despite the advances in radar-based event detection, challenges remain, such as distinguishing between similar events like falls and rapid sitting. To address these challenges, for the first time, we propose an impulse radio-ultrawideband (IR-UWB) radar system to collect over ten thousand radar echo signals of eight similar actions from different angles and design a self-attention-based low-complexity convolutional neural network (CNN) model for event classification. The model leverages global correlations in radar signal spectrograms to efficiently extract features. A comparative simulation study is conducted to evaluate the detection accuracy of the proposed model and some of the existing methods based on different dataset sizes and CNN configurations. Moreover, the influence of different self-attention structures on precision and model parameter count is analyzed. Our findings reveal that the proposed self-attention-based CNN model significantly outperforms other traditional machine learning techniques while maintaining a low level computational complexity.