RadarAttn: Efficient Radar-Based Human Activity Recognition by Integrating Visual Attention and Self-Attention

Radar-based human activity recognition (HAR) has emerged as a critical component in various applications, ranging from smart homes to healthcare monitoring. Radar has several advantages: a wide detection range, a certain penetration ability, non-contact and non-perception detection ability, not being affected by light, and privacy-preserving. However, achieving high accuracy with efficiency remains a significant challenge due to the complexity of radar signals and the variability in human activities. Currently, the majority of research efforts are centered on enhancing performance, often at the expense of computational efficiency. In this paper, we propose RadarAttn, a novel approach that integrates visual attention mechanisms with self-attention to enhance the performance and efficiency of HAR systems. The architecture of RadarAttn can reduce floating-point operations (FLOPs) and parameter counts while improving accuracy. Our method leverages the visual attention mechanism to focus on the most relevant regions of radar spectrograms. Simultaneously, the self-attention mechanism is used to capture long-range dependencies within the radar signal, enabling the model to learn complex patterns associated with different activities. Experimental results on benchmark radar-based HAR datasets demonstrate that RadarAttn significantly outperforms state-of-the-art methods in both accuracy and computational efficiency. Our approach offers a promising direction for developing robust and scalable radar-based HAR systems for real-world applications.