arXiv:2606. 24781v1 Announce Type: new Abstract: While the field of Human Activity Recognition (HAR) continues to draw interest from researchers and advance in important ways, some key challenges remain.
By Rebecca Adaimi, Edison Thomaz
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:2606. 14604v1 Announce Type: cross Abstract: Wearable devices and smartphones generate rich behavioural time series that can support proactive health interventions, yet systematic comparisons of modern forecasting architectures for these data are lacking.
By Pavlos Nicolaou, Kleanthis Malialis, Artemis Kontou, Panayiotis Kolios
HALO is a heterogeneity‑aware, language‑aligned foundation model for inertial measurement unit (IMU) based human activity recognition. It uses a two‑stage training process: first, a self‑supervised encoder learns to handle diverse sensor configurations and natural‑language sensor descriptions; second, the encoder is aligned with text embeddings through synonym‑aware contrastive learning, enabling open‑set recognition via cosine similarity. Trained on ten public HAR datasets, HALO outperforms five state‑of‑the‑art baselines across eight metrics while using only ~35 M parameters, and improves zero‑shot open‑set accuracy by 13.7 percentage points over 87 training labels.
By Zihan Ding, Liyu Zhang, Xiaomin Ouyang