Human gesture recognition with Radio Frequency (RF) signals has attained acclaim due to the omnipresence, privacy protection, and broad coverage nature of RF signals. These gesture recognition systems rely on neural networks trained with a large number of labeled data. However, the recognition model trained with data under certain conditions would suffer from significant performance degradation when applied in practical deployment, which limits the application of gesture recognition systems. In this paper, we propose an unsupervised domain adaptation framework for RF-based gesture recognition aiming to enhance the performance of the recognition model in new conditions by making effective use of the unlabeled data from new conditions. We first propose pseudo-labeling and consistency regularization to utilize unlabeled data for model training and eliminate the feature discrepancies in different domains. Then we propose a confidence constraint loss to enhance the effectiveness of pseudo-labeling, and design two corresponding data augmentation methods based on the characteristic of the RF signals to strengthen the performance of the consistency regularization, which can make the framework more effective and robust. Furthermore, we propose a cross-match loss to integrate the pseudo-labeling and consistency regularization, which makes the whole framework simple yet effective. Extensive experiments demonstrate that the proposed framework could achieve 4.35% and 2.25% accuracy improvement comparing with the state-of-the-art methods on public WiFi dataset and millimeter wave (mmWave) radar dataset, respectively.

翻译：基于射频信号的手势识别因其普遍性、隐私保护性和广覆盖特性而备受关注。这些手势识别系统依赖于使用大量标注数据训练的神经网络。然而，在特定条件下训练得到的识别模型在实际部署时性能会显著下降，这限制了手势识别系统的应用。本文提出一种用于射频手势识别的无监督域适应框架，旨在通过有效利用新条件下的未标注数据来提升识别模型在新环境中的性能。我们首先提出伪标签生成和一致性正则化方法，以利用未标注数据进行模型训练并消除不同域间的特征差异。随后，我们提出置信度约束损失以增强伪标签的有效性，并基于射频信号特性设计了两种相应的数据增强方法以强化一致性正则化的性能，从而使框架更加有效和鲁棒。此外，我们提出交叉匹配损失来整合伪标签生成与一致性正则化，使得整个框架简洁而高效。大量实验表明，在公开的WiFi数据集和毫米波雷达数据集上，所提框架相较于现有最优方法分别实现了4.35%和2.25%的准确率提升。