用雷达速度信息构建3维动作场，提升Wi-Fi识人动作的泛化能力。
DoRF: Doppler Radiance Fields for Robust Human Activity Recognition Using Wi-Fi
- 借鉴神经辐射场思想，从一维多普勒信号重建三维运动隐空间
- 在多个环境和用户下实现92.1%准确率，显著优于现有方法
- 适合跨场景、跨用户的无感人体动作识别应用
Wi-Fi信道状态信息（CSI）在远程感知中日益受到关注。近期研究显示，从CSI中提取的多普勒速度投影可实现对环境变化和新用户具有鲁棒性的动作识别（HAR）。然而，尽管已有进展，实际部署中的泛化能力仍不足。受神经辐射场（NeRF）启发，本文提出一种新方法：从一维多普勒速度投影中重建具信息量的三维隐运动表示。由此生成的统一多普勒辐射场（DoRF）全面刻画了动作过程，提升了对环境变异的鲁棒性。实验表明，该方法显著增强了基于Wi-Fi的HAR泛化准确率，凸显了DoRF在实际传感中的巨大潜力。
原文摘要 · Abstract (English)
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.
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