Recent advancements in wireless perception technologies, including mmWave, WiFi, and acoustics, have expanded their application in human motion tracking and health monitoring. They are promising alternatives to traditional camera-based perception systems, thanks to their efficacy under diverse conditions or occlusions, and enhanced privacy. However, the integration of deep learning within this field introduces new challenges such as the need for extensive training data and poor model generalization, especially with sparse and noisy wireless point clouds. As a remedy, data augmentation is one solution well-explored in other deep learning fields, but they are not directly applicable to the unique characteristics of wireless signals. This motivates us to propose a custom data augmentation framework, WixUp, tailored for wireless perception. Moreover, we aim to make it a general framework supporting various datasets, model architectures, sensing modalities, and tasks; while previous wireless data augmentation or generative simulations do not exhibit this generalizability, only limited to certain use cases. More specifically, WixUp can reverse-transform lossy coordinates into dense range profiles using Gaussian mixture and probability tricks, making it capable of in-depth data diversity enhancement; and its mixing-based method enables unsupervised domain adaptation via self-training, allowing training of the model with no labels from new users or environments in practice. In summary, our extensive evaluation experiments show that WixUp provides consistent performance improvement across various scenarios and outperforms the baselines.

翻译：近期，包括毫米波、WiFi与声学在内的无线感知技术的最新进展，已将其应用拓展至人体运动追踪与健康监测领域。由于其在多样化环境或遮挡条件下仍具有效性，并增强了隐私保护，这些技术成为传统基于摄像头的感知系统的理想替代方案。然而，深度学习在该领域的整合带来了新的挑战，例如对大量训练数据的依赖以及模型泛化能力不足，尤其是在处理稀疏且含噪的无线点云时。作为应对措施，数据增强是深度学习其他领域已充分研究的一种解决方案，但这些方法并不直接适用于无线信号的独特特性。这促使我们提出一种专为无线感知定制的数据增强框架WixUp。此外，我们旨在使其成为支持多种数据集、模型架构、感知模态与任务的通用框架；而先前的无线数据增强或生成式模拟方法并不具备这种通用性，仅限于特定用例。具体而言，WixUp能够利用高斯混合与概率技巧将有损坐标逆变换为密集距离剖面，从而深入增强数据多样性；其基于混合的方法可通过自训练实现无监督域自适应，使得在实际应用中，模型能够无须新用户或新环境标签即可完成训练。总之，我们广泛评估实验表明，WixUp在多种场景下均能提供一致的性能提升，并优于基线方法。