WixUp: A General Data Augmentation Framework for Wireless Perception in Tracking of Humans

Abstract

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.

SenSys pre-published version

References in corpus (9)

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• A Simple Framework for Contrastive Learning of Visual Representations
• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
• Bootstrap your own latent: A new approach to self-supervised Learning
• Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
• NeRF2: Neural Radio-Frequency Radiance Fields
• Training Data Augmentation for Deep Learning Radio Frequency Systems
• Modeling the Noticeability of User-Avatar Movement Inconsistency for Sense of Body Ownership Intervention
• Beyond-Voice: Towards Continuous 3D Hand Pose Tracking on Commercial Home Assistant Devices