DoRF: Doppler Radiance Fields for Robust Human Activity Recognition Using Wi-Fi

TL;DR

This paper introduces DoRF, a novel 3D motion representation derived from Wi-Fi Doppler velocity data, which significantly improves the robustness and generalization of human activity recognition in varying environments.

Contribution

It proposes a new Doppler radiance field (DoRF) method inspired by NeRF to enhance Wi-Fi-based HAR robustness and generalization across different settings and users.

Findings

Enhanced generalization accuracy in Wi-Fi HAR

Robustness to environmental variability demonstrated

Effective 3D motion representation from Doppler data

Abstract

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.