arXiv:2602. 01910v2 Announce Type: replace Abstract: Smart-home sensor-based behavioral monitoring holds significant potential for healthcare, independent living, and early detection of functional or cognitive changes.
By Michele Fiori, Gabriele Civitarese, Flora D. Salim, Claudio Bettini
arXiv:2505. 20894v2 Announce Type: replace Abstract: Despite recognized limitations in modeling long-range temporal dependencies, Human Activity Recognition (HAR) has traditionally relied on a sliding window approach to segment labeled datasets.
By Marius Bock, Juergen Gall, Michael Moeller, Kristof Van Laerhoven
arXiv:2606. 28377v1 Announce Type: cross Abstract: HAR using Inertial Measurement Unit (IMU) sensors is vital for healthcare monitoring and rehabilitation.
By Saeid Arabzadeh, Farshad Almasganj, Mohammad Mahdi Ahmadi
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:2607. 16350v1 Announce Type: cross Abstract: Sensor-based human activity recognition (HAR) has achieved significant progressed in fully supervised learning settings.
By Mohd Halim Mohd Noor, Abdulrahman M. A. Baraka
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.