The paper proposes an identity‑agnostic approach to multi‑user WiFi sensing that reframes activity recognition as counting how many users perform each activity, rather than assigning actions to specific individuals. Using WiMANS data, the identity‑agnostic counting model outperforms identity‑dependent predictions on integer‑count mean absolute error across all tested environments, and remains robust when users are unseen. Embedding analysis shows that identity‑agnostic representations cluster by activity composition instead of user identity, supporting the claim that counting is a more accurate and robust scene‑level estimation method.
By Kemal Bayik, Olayinka Ajayi, Daniel Roggen, Philip Birch
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
arXiv:2610.19411v1 Announce Type: new
Abstract: A major challenge in wearable device activity recognition is the heterogeneity across datasets in sensor placement, channel count, sampling rate and su...
By Yogesh Prabhu, Loki Natarajan, Jingjing Zou
arXiv:2608.08381v2 Announce Type: replace
Abstract: Motivated by the IEEE 802.11bf effort to standardize advanced WLAN sensing, interest in Wi-Fi Channel State Information (CSI) for passive, device-f...
By Navid Hasanzadeh, Shahrokh Valaee
AmbientFM is a foundation model designed for ambient sensing using ubiquitous WiFi signals. It is pre‑trained on 9.2 million unlabeled Channel State Information samples collected over 439 days from 20 commercial devices in real homes, making it the largest real‑world WiFi CSI pre‑training corpus reported to date. The model learns transferable wireless representations through contrastive learning, masked reconstruction, and physics‑informed objectives, and with lightweight adaptation it can transfer to nine downstream tasks, achieving above 0.90 AUROC on all classification benchmarks and supporting dense spatial reconstruction.
By Guozhen Zhu, Yuqian Hu, Sakila Jayaweera, Weihang Gao, Wei-Hsiang Wang, Jiaxuan Zhang, Beibei Wang, Chenshu Wu, K. J. Ray Liu
arXiv:2609.01036v1 Announce Type: cross
Abstract: A lack of suitable datasets has limited the research into the privacy risks of novel smart city sensors, such as thermal cameras, depth cameras, and...
By Julian Todt, Felix Morsbach, Philip Dissert, Thorsten Strufe