  Device free human gesture recognition with Radio Frequency signals has
attained acclaim due to the omnipresence, privacy protection, and broad
coverage nature of RF signals. However, neural network models trained for
recognition with data collected from a specific domain suffer from significant
performance degradation when applied to a new domain. To tackle this challenge,
we propose an unsupervised domain adaptation framework for device free gesture
recognition by making effective use of the unlabeled target domain data.
Specifically, we apply pseudo labeling and consistency regularization with
elaborated design on target domain data to produce pseudo labels and align
instance feature of the target domain. Then, we design two data augmentation
methods by randomly erasing the input data to enhance the robustness of the
model. Furthermore, we apply a confidence control constraint to tackle the
overconfidence problem. We conduct extensive experiments on a public WiFi
dataset and a public millimeter wave radar dataset. The experimental results
demonstrate the superior effectiveness of the proposed framework.
