arXiv:2606. 01834v1 Announce Type: cross Abstract: Human Action Recognition (HAR) using WiFi Channel State Information (CSI) has gained increasing attention due to its non-contact, low-cost, and privacy-preserving nature.
By Chinthaka Ranasingha, Tharindu Fernando, Sridha Sridharan, Clinton Fookes, Harshala Gammulle
arXiv:2606. 02974v1 Announce Type: new Abstract: Human Activity Recognition (HAR) using WiFi signals has emerged as a transformative technology for smart homes, healthcare monitoring, security systems, and ambient assisted living.
By Maheen Arshad, Qindeel E Zahra, Muhammad Khuram Shahzad
The paper introduces a physics‑informed autoencoder that separates reflector speed from sensing geometry in WiFi spectrograms, enabling joint recovery of speed, geometry factor, relative amplitude, and ridge width for each Doppler ridge. It builds a compact parametric representation of spectrograms validated on a large human‑activity dataset and employs a synthetic‑to‑real training framework to avoid real‑data collection. Experiments on synthetic and 31 real WiFi scenarios show the method outperforms existing baselines in accurately extracting speed and geometry information.
By Mert Torun, Darius Cuenca, Yasamin Mostofi
arXiv:2607. 03196v1 Announce Type: cross Abstract: WiFi-based human pose estimation (HPE) enables the detection and interpretation of human body positions and movements without the need for wearable devices while preserving individual privacy concerns.
By Toan D. Gian, Van-Dinh Nguyen, Vo Phi Son, Nhan Thanh Nguyen, Dinh Thai Hoang, Diep N. Nguyen, Nguyen Cong Luong, Symeon Chatzinotas
The paper introduces Inter-Instance Generative Adversarial Networks (IIns‑GAN), a deep learning approach for synthesizing realistic labeled wireless signals. Unlike traditional environmental‑model based methods, IIns‑GAN adapts to various scenarios and produces signals that closely match the physical characteristics of real measurements. Experiments on public Ultra‑Wideband datasets show that the generated signals improve model training for tasks such as distance estimation and environment identification.
By Yuxiao Li, Keke Hu, Santiago Mazuelas, Yuan Shen
WiNeRF is a neural field framework that learns a spatially continuous, complex-valued wireless channel representation from sparse channel state information collected by commodity WiFi devices. It incorporates system constraints such as antenna geometry, limited spatial resolution, and phase uncertainty through a 3D conical wave sampling model, a multi-resolution implicit scene representation, and a differentiable optimization framework. In diverse indoor environments with non‑line‑of‑sight regions, WiNeRF achieves a median prediction SNR of 5.3 dB, outperforming prior neural baselines by 4.9 dB on average, and produces a task‑agnostic channel representation that can be reused in standard signal‑processing pipelines without hardware or protocol changes.
By Saif Ur Rahman, Rafid Umayer Murshed, Anton Dmitriev, Cagri Tanriover, Rahul C. Shah, Elah\'e Soltanaghai