Radar-based Human Activity Recognition (HAR) offers privacy and robustness over camera-based methods, yet remains computationally demanding for edge deployment. We present the first use of Spiking Neural Networks (SNNs) for radar-based HAR on aircraft marshalling signal classification. Our novel hybrid architecture combines convolutional modules for spatial feature extraction with Leaky Integrate-and-Fire (LIF) neurons for temporal processing, inherently capturing gesture dynamics. The model reduces trainable parameters by 88\% with under 1\% accuracy loss compared to baselines, and generalizes well to the Soli gesture dataset. Through systematic comparisons with Artificial Neural Networks, we demonstrate the trade-offs of spiking computation in terms of accuracy, latency, memory, and energy, establishing SNNs as an efficient and competitive solution for radar-based HAR.

翻译：基于雷达的人体活动识别相较于基于摄像头的方法具有隐私保护和鲁棒性优势，但其在边缘设备部署时仍面临计算需求高的挑战。本文首次将脉冲神经网络应用于基于雷达的人体活动识别，具体针对飞机引导信号分类任务。我们提出了一种新颖的混合架构，该架构结合了用于空间特征提取的卷积模块与用于时序处理的Leaky Integrate-and-Fire神经元，从而内在地捕捉手势动态。与基线模型相比，该模型在精度损失低于1%的情况下，将可训练参数量减少了88%，并在Soli手势数据集上展现出良好的泛化能力。通过与人工神经网络进行系统比较，我们阐明了脉冲计算在精度、延迟、内存和能耗方面的权衡关系，从而确立了脉冲神经网络作为基于雷达的人体活动识别的一种高效且具有竞争力的解决方案。