Abstract
Human activity recognition (HAR) requires extracting accurate spatial-temporal features with human movements. A mmWave radar point cloud-based HAR system suffers from sparsity and variable-size problems due to the physical features of the mmWave signal. Existing works usually borrow the preprocessing algorithms for the vision-based systems with dense point clouds, which may not be optimal for mmWave radar systems. In this work, we proposed a graph representation with a discrete dynamic graph neural network (DDGNN) to explore the spatial-temporal representation of human movement-related features. Specifically, we designed a star graph to describe the high-dimensional relative relationship between a manually added static center point and the dynamic mmWave radar points in the same and consecutive frames. We then adopted DDGNN to learn the features residing in the star graph with variable sizes. Experimental results demonstrated that our approach outperformed other baseline methods using real-world HAR datasets. Our system achieved an overall classification accuracy of 94.27\%, which gets the near-optimal performance with a vision-based skeleton data accuracy of 97.25\%. We also conducted an inference test on Raspberry Pi~4 to demonstrate its effectiveness on resource-constraint platforms. \sh{ We provided a comprehensive ablation study for variable DDGNN structures to validate our model design. Our system also outperformed three recent radar-specific methods without requiring resampling or frame aggregators.
Abstract (translated)
人体活动识别（HAR）需要从人类运动中提取准确的时空特征。基于毫米波雷达点云的HAR系统由于毫米波信号的物理特性而面临稀疏性和大小可变的问题。现有研究通常借用为密集点云设计的视觉系统的预处理算法，这可能并不适用于毫米波雷达系统。在这项工作中，我们提出了一种使用离散动态图神经网络（DDGNN）表示的图表示方法来探索与人类运动相关的时空特征的表示。具体而言，我们设计了一个星形图来描述手动添加的静态中心点和同一帧及连续帧中的动态毫米波雷达点之间的高维相对关系。随后，我们采用DDGNN来学习存在于不同大小星形图中的特征。实验结果表明，在真实世界HAR数据集上，我们的方法优于其他基线方法。我们的系统实现了94.27%的整体分类准确率，接近使用基于视觉的骨架数据实现的97.25%的最佳性能。我们还在Raspberry Pi 4上进行了推理测试，以展示其在资源受限平台上的有效性。此外，我们进行了一系列消融实验来验证不同结构下的DDGNN模型设计的有效性。我们的系统还优于三种最近的雷达专用方法，并且不需要重采样或帧聚合器。
URL
https://arxiv.org/abs/2512.12013
