WixUp: A General Data Augmentation Framework for Wireless Perception in Tracking of Humans
Yin Li, Rajalakshmi Nandakumar

TL;DR
WixUp is a versatile data augmentation framework designed for wireless perception, enhancing human tracking accuracy by improving data diversity and enabling unsupervised domain adaptation across various sensing modalities and environments.
Contribution
We introduce WixUp, a novel general data augmentation framework tailored for wireless perception, supporting multiple datasets, models, and tasks, with techniques for data densification and unsupervised domain adaptation.
Findings
WixUp consistently improves performance across different scenarios.
It outperforms baseline methods in wireless human tracking tasks.
The framework effectively enhances data diversity and domain generalization.
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsVideo Surveillance and Tracking Methods · Energy Efficient Wireless Sensor Networks
