  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.
