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. 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: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
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: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 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