发表机构
University of Toronto（多伦多大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对Wi-Fi感知的跨用户泛化难题，提出DoRF++模型，将NeRF概念引入Wi-Fi感知，结合球面Transformer实现手势识别，在单多天线AP场景下的困难手势识别中性能优于现有方法。
AI 中文摘要
受IEEE 802.11bf标准化高级无线局域网（WLAN）感知工作的推动，Wi-Fi信道状态信息（CSI）在无源、无设备、隐私保护的活动与手势识别方面的应用兴趣迅速增长。近期研究表明，从CSI中提取的多普勒速度投影可直接反映人体运动速度，能实现更鲁棒的人体活动识别（HAR），并在不同用户及未知条件下具备更强的泛化能力。然而，真实场景变化下的可靠泛化仍是重大挑战，阻碍了Wi-Fi感知在实际应用中的推广。为解决该挑战，本文引入多普勒辐射场（DoRF），将计算机视觉中的神经辐射场（NeRF）概念应用于Wi-Fi感知。DoRF将从Wi-Fi CSI中提取的多普勒速度投影建模为人体运动的稀疏且多样的虚拟相机视图，随后推断潜在的3D运动序列，该序列沿学习到的有效多普勒方向的投影可解释CSI衍生的多普勒观测结果。恢复的运动随后被投影到单位球面上的等角方向网格，生成潜在运动的球面表示。由于DoRF自然地将多普勒表示定义在球面上，本文进一步引入DoRF++，这是一种采用球面Transformer进行活动分类的球面学习设计。在本文收集的手势数据集上的实验表明，DoRF++在跨用户泛化准确率上显著优于当前最先进的基于Wi-Fi的HAR方法，尤其在单多天线接收器接入点（AP）场景下的困难手势识别中表现突出。
英文摘要
Motivated by the IEEE 802.11bf effort to standardize advanced WLAN sensing, interest in Wi-Fi Channel State Information (CSI) for passive, device-free, and privacy-preserving activity and gesture recognition has grown rapidly. Recent studies have shown that Doppler velocity projections extracted from CSI, which directly reflect human-motion velocity, enable more robust human activity recognition (HAR) and stronger generalization across users and unseen conditions. Nevertheless, reliable generalization under real-world variability remains a major challenge that hinders the adoption of Wi-Fi sensing. To address this challenge, we build on Doppler Radiance Fields (DoRF), adapting the concept of neural radiance fields (NeRF) from computer vision to Wi-Fi sensing. DoRF treats the extracted Doppler velocity projections as sparse virtual-camera views of human motion. From these views it recovers a regularized latent 3-D motion descriptor whose projections along learned effective Doppler directions explain the CSI observations. The recovered motion is then re-projected onto an equiangular grid of directions on the unit sphere, yielding a spherical representation of the underlying activity. Because DoRF produces a signal defined on the sphere, this paper introduces DoRF++, a spherical representation-learning model that applies quadrature-aware spherical Transformers directly to the spherical field for activity classification. Experiments on our collected hand-gesture dataset show that DoRF++ noticeably outperforms state-of-the-art Wi-Fi-based HAR methods in cross-user generalization accuracy, especially for difficult gestures when only a single multi-antenna receiver access point (AP) is available.
