DoRF: Doppler Radiance Fields for Robust Human Activity Recognition Using Wi-Fi
This work addresses the challenge of making Wi-Fi sensing robust for practical human activity recognition applications, representing an incremental advance over prior methods.
The paper tackles the problem of insufficient generalizability in Wi-Fi-based human activity recognition by proposing a novel approach to reconstruct a 3D latent motion representation from Doppler velocity projections, resulting in improved robustness to environmental variability and enhanced generalization accuracy.
Wi-Fi Channel State Information (CSI) has gained increasing interest for remote sensing applications. Recent studies show that Doppler velocity projections extracted from CSI can enable human activity recognition (HAR) that is robust to environmental changes and generalizes to new users. However, despite these advances, generalizability still remains insufficient for practical deployment. Inspired by neural radiance fields (NeRF), which learn a volumetric representation of a 3D scene from 2D images, this work proposes a novel approach to reconstruct an informative 3D latent motion representation from one-dimensional Doppler velocity projections extracted from Wi-Fi CSI. The resulting latent representation is then used to construct a uniform Doppler radiance field (DoRF) of the motion, providing a comprehensive view of the performed activity and improving the robustness to environmental variability. The results show that the proposed approach noticeably enhances the generalization accuracy of Wi-Fi-based HAR, highlighting the strong potential of DoRFs for practical sensing applications.