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: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.
By Nayan Sanjay Bhatia, Pranay Kocheta, Yuhan Li, Katia Obraczka
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: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
arXiv:2607. 26381v1 Announce Type: cross Abstract: Wi-Fi-based human activity recognition has advanced substantially, but most existing methods assume a closed set of activities and require labeled Wi-Fi samples for every target class, limiting their ability to recognize unseen activities.
By Yitong Shen, Cheng Guo, Peiliang Wang, Jingzhe Zhang, Yi Sheng, Haopeng Zhang, Hongfei Xue, Yili Ren
The paper presents a comprehensive benchmark for Domain Generalization (DG) in smartphone-based Human Activity Recognition (HAR), running over 410,000 experiments across multiple architectures, training objectives, initialization strategies, and architectural tweaks. It finds that individual DG components offer limited, highly conditional improvements, while combined configurations often yield stronger, sometimes super‑additive gains that depend on the model and shift scenario. The study also highlights that current source‑validation selection captures only a fraction of the potential oracle performance, underscoring the need for joint DG design and robust model‑selection methods.
By Ot\'avio Oliveira Napoli, Edson Borin
Wi-Fi-based human activity recognition has advanced substantially, but most existing methods assume a closed set of activities and require labeled Wi-Fi samples for every target class, limiting their ability to recognize unseen activities. We present Zero-Fi, a contrastive signal-language alignment framework for zero-shot Wi-Fi-based human activity recognition.
arXiv:2609.36154v1 Announce Type: new
Abstract: General-purpose models promise sensor-based decisions without training a task-specific classifier, which could reduce the dependence of Human Activity...
By Orhan Konak
arXiv:2607. 03089v1 Announce Type: cross Abstract: HAR is increasingly expected to run continuously on edge devices, yet recent LLM-based methods remain hard to deploy: raw sensor prompts are long, cloud inference adds latency and privacy risk, and fine-tuned LLM pipelines turn general-purpose models into task-specific classifiers.
By Nirhoshan Sivaroopan, Albert Zomaya, Kanchana Thilakarathna
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
The paper introduces a coverage-aware virtual IMU augmentation framework for human activity recognition. It selects diverse and scarce data points in a learned sensor embedding space, generates virtual IMU samples as prompts, ranks them by proximity and label consistency, and incorporates them into training with reliability-based weights. Experiments on public benchmarks demonstrate consistent performance gains over existing baselines, with ablation studies confirming the framework’s effectiveness.
By Jiayuan Gao, Yingwei Zhang, Ziyao Tang, Yuejia Ma, Yuanzhe Chen, Shuchao Song, Boshi Tang
arXiv:2609.17204v1 Announce Type: cross
Abstract: Wi-Fi signal data can be used to compromise the privacy of individuals. While many existing approaches rely on Channel State Information (CSI), colle...
By Ariel Duschanek-Myers, Thomas Welsh, Helmut Neukirchen