The recognition of human activities based on WiFi Channel State Information (CSI) enables contactless and visual privacy-preserving sensing in indoor environments. However, poor model generalization, due to varying environmental conditions and sensing hardware, is a well-known problem in this space. To address this issue, in this work, data augmentation techniques commonly used in image-based learning are applied to WiFi CSI to investigate their effects on model generalization performance in cross-scenario and cross-system settings. In particular, we focus on the generalization between line-of-sight (LOS) and non-line-of-sight (NLOS) through-wall scenarios, as well as on the generalization between different antenna systems, which remains under-explored. We collect and make publicly available a dataset of CSI amplitude spectrograms of human activities. Utilizing this data, an ablation study is conducted in which activity recognition models based on the EfficientNetV2 architecture are trained, allowing us to assess the effects of each augmentation on model generalization performance. The gathered results show that specific combinations of simple data augmentation techniques applied to CSI amplitude data can significantly improve cross-scenario and cross-system generalization.

翻译：基于WiFi信道状态信息（CSI）的人体活动识别技术能够在室内环境中实现非接触式且保护视觉隐私的感知。然而，由于环境条件和传感硬件的差异，模型泛化能力差是该领域公认的难题。针对这一问题，本研究将常用于基于图像学习的数据增强技术应用于WiFi CSI，探究其对跨场景与跨系统设置下模型泛化性能的影响。具体而言，本文重点研究了视距（LOS）与非视距（NLOS）穿墙场景之间的泛化问题，以及不同天线系统之间的泛化问题——这一方向目前仍鲜有探索。我们采集并公开了一个人体活动CSI幅度频谱图数据集。基于该数据，我们开展了消融实验：训练基于EfficientNetV2架构的活动识别模型，从而评估每种增强方法对模型泛化性能的影响。实验结果表明，将简单数据增强技术进行特定组合应用于CSI幅度数据，能够显著提升跨场景与跨系统的泛化能力。