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.
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.
arXiv:2607. 26631v1 Announce Type: new Abstract: Human Activity Recognition (HAR) from wearable sensors supports applications in healthcare, rehabilitation, fitness tracking, and smart environments.
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.
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:2609.25373v1 Announce Type: new Abstract: On-device assistants require function-calling models that map natural language to local system actions, but existing resources emphasize web APIs or na...
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...
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.
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.
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.
The paper proposes an identity‑agnostic approach to multi‑user WiFi sensing that reframes activity recognition as counting how many users perform each activity, rather than assigning actions to specific individuals. Using WiMANS data, the identity‑agnostic counting model outperforms identity‑dependent predictions on integer‑count mean absolute error across all tested environments, and remains robust when users are unseen. Embedding analysis shows that identity‑agnostic representations cluster by activity composition instead of user identity, supporting the claim that counting is a more accurate and robust scene‑level estimation method.
PocketTSLM is a mobile-native deployment of the OpenTSLM SoftPrompt time‑series language model that runs entirely on a consumer smartphone. By injecting encoded time‑series windows as embeddings instead of tokenizing them as text, PocketTSLM preserves temporal structure while using far fewer tokens and staying within the device’s context and memory limits. The model has been benchmarked on 30‑second EEG sleep‑staging clips and 10‑second ECG recordings, achieving on‑device inference with no health data transmitted to the cloud.
The paper investigates the use of Zero Cost Proxies (ZCPs) to identify high‑performing wearable Human Activity Recognition (HAR) models without full training. Eight ZCPs were evaluated across six benchmark HAR datasets, showing that the top‑predicted architectures achieve performance within 7% of fully trained models, and training the top‑10 predictions reaches within 2% of full training. This demonstrates that ZCPs can significantly reduce computational costs while maintaining competitive accuracy in sensor‑based HAR tasks.