arXiv Computer Vision
DoRF++: Spherical Representation Learning over Doppler Radiance Fields for Robust Wi-Fi Sensing
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arXiv AI
Jun 3
WISE-HAR: A Generalizable Ensemble Deep Learning Framework for WiFi-Based Human Activity Recognition
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.
arXiv AI
Aug 18
ARGUS: Attention-Guided Transformers for Scalable Person Identification Using Wi-Fi Telemetry
arXiv:2608. 14670v1 Announce Type: cross Abstract: Passive, device-free person identification offers an alternative to camera- and wearable-based biometrics, yet existing wireless approaches rely largely on gait or activity cues and are rarely evaluated at scale.
Hugging Face Trending Papers
Sep 8
Beyond Gait: Person Identification from Millimeter-Wave Point Clouds Across Activities of Daily Living
The paper explores person identification using millimeter‑wave point clouds beyond traditional gait analysis, focusing on seven activities of daily living (ADLs). It introduces the mm‑ADL dataset of 11 subjects and proposes an activity‑conditioned framework that routes each clip to an activity‑specific identity expert via a supervised mixture of experts. Experiments show that hard routing improves closed‑set ID accuracy from 62.1% to 68.0% and significantly boosts re‑identification metrics, demonstrating the benefit of activity context under controlled indoor conditions.
arXiv Machine Learning
Jul 7
Seeing Through WiFi: Lightweight Human Pose Estimation with Dynamic Kernel Attention
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.
arXiv Machine Learning
Sep 22
WiNeRF: Measurement Constrained Radiance Fields for Actionable Wireless Channel Modeling
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.
arXiv AI
Jun 2
Physics-Guided Attention in a Lightweight TCN for Efficient WiFi CSI-Based Human Activity Recognition
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.