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
arXiv:2607. 26631v1 Announce Type: new Abstract: Human Activity Recognition (HAR) from wearable sensors supports applications in healthcare, rehabilitation, fitness tracking, and smart environments.
By Hansi Karunarathna, Nirhoshan Sivaroopan, Chamara Madarasingha, Anura Jayasumana, Kanchana Thilakarathna
arXiv:2609.36848v1 Announce Type: new
Abstract: Human Activity Recognition (HAR) through wearable sensors greatly improves the quality of human life through its multiple applications. For HAR, multi-...
By Nafees Ahmad, Ho-fung Leung, Muhammad Adil Abid, Sadia Shakil
arXiv:2606. 04798v1 Announce Type: new Abstract: Sensor-based Human Activity Recognition (HAR) models often degrade on unseen users due to domain shifts caused by individual movement patterns and sensor placement.
By Maximilian Burzer, Till Riedel, Michael Beigl, Tobias R\"oddiger
Human Activity Recognition (HAR) from wearable sensors supports applications in healthcare, rehabilitation, fitness tracking, and smart environments. Yet, existing deep learning approaches require dataset-specific training, large labeled corpora, and repeated adaptation to new sensor settings or activity taxonomies.
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:2608. 15621v1 Announce Type: new Abstract: Human Activity Recognition (HAR) with self-administered wearables, such as at-home rehabilitation and exercise monitoring, often requires reattaching inertial measurement units (IMUs) across sessions.
By Seungyeol Baek, Yoonbyung Chai, Yonghyeon Lee, Sungjoon Choi, Sungho Suh
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
arXiv:2608. 13316v1 Announce Type: cross Abstract: Foundation models (FMs) trained on large-scale accelerometer data have been proposed as general-purpose feature extractors for health monitoring, but systematic evidence of their advantages is lacking.
By Alexander Br\"auer, Benjamin Cauchi, Nils Strodthoff
BayaHAR is a lightweight, gradient‑free framework that adapts pretrained sensor‑based Human Activity Recognition (HAR) classifiers to new users by converting them into Prototypical Networks with prior prototypes that maintain zero‑shot performance. It introduces closed‑form Bayesian prototype estimation for labeled calibration data and extends this approach to weakly labeled data, requiring only activity labels. With just three seconds of calibration per activity, supervised adaptation boosts test macro‑F1 on unseen users by 2.76–33.44 percentage points across four datasets, while weakly supervised adaptation improves by 0.56–32.13 points, enabling efficient on‑device personalization.
By Maximilian Burzer, Till Riedel, Michael Beigl, Tobias R\"oddiger
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: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