The paper introduces PAFIR, a Personalized and Adaptive Feature selection framework that treats feature selection as a reinforcement learning problem over longitudinal multimodal health data. PAFIR jointly models structural dependencies among assessment variables and temporal dynamics in wearable-derived physical activity data, learning adaptive selection policies across repeated study visits using reward signals from sparse fall incidence outcomes. Applied to the Physio Feedback Exercise Program (PEER) trial, PAFIR outperforms state‑of‑the‑art baselines by capturing longitudinal and structural patterns of feature relevance, enabling dynamic, subject‑specific feature selection for more timely fall prevention strategies.
arXiv:2606. 00345v1 Announce Type: new Abstract: Wearable and mobile sensing technologies enable continuous monitoring of human behavior and health in real-world settings.
DeMMO is an interpretable framework that models longitudinal digital mobility outcomes (DMOs) across multiple diseases and outcomes using multi-task learning. It introduces a cross-disease, cross-outcome relation-learning mechanism that learns signed relationships from longitudinal DMO coefficient matrices, allowing selective information sharing even when disease cohorts lack shared participants. Evaluated on the Mobilise‑D dataset, DeMMO outperforms nine strong baselines and identifies reliable longitudinal DMO patterns for clinical validation.
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.
arXiv:2605. 19208v2 Announce Type: replace-cross Abstract: Physical activity (PA) plays an important role in maintaining and improving health.
arXiv:2608. 15309v1 Announce Type: new Abstract: Continuous multimodal sensing now allows human physiology to be observed throughout daily life rather than only during occasional clinical visits.
arXiv:2605. 22759v2 Announce Type: replace Abstract: While ubiquitous wearable sensors capture a wealth of behavioral and physiological information, effectively transforming these signals into personalized health insights is challenging.
BALMS is a benchmark for evaluating large language model (LLM) agents that analyze longitudinal wearable data to predict mental‑health wellbeing scores and generate evidence‑grounded rationales. It covers three real‑world datasets, two task families (score prediction and rationale generation), and tests five LLM backbones across open‑ and closed‑source paradigms. The study finds that zero‑shot agents rarely beat a simple mean baseline, and while chain‑of‑thought prompting helps reasoning, it does not ensure temporal grounding or numerical accuracy.
arXiv:2607. 14190v1 Announce Type: new Abstract: Amyotrophic lateral sclerosis (ALS) is a progressive and heterogeneous neurodegenerative disease in which predicting clinically meaningful milestones, such as assistive device use, remains challenging.
arXiv:2607. 06954v1 Announce Type: new Abstract: Wearable and mobile sensing technologies have demonstrated strong potential for health inference; however, most sensor models are designed for specific disease types, limiting their transferability across different health risks.
arXiv:2606. 00141v1 Announce Type: cross Abstract: Adaptive sensing strategies that selectively sample data are increasingly used in wearable health systems to improve prediction performance under limited data budgets, yet their benefits across individuals remain poorly understood.
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.
arXiv:2606. 19092v1 Announce Type: cross Abstract: Chronic disease management relies on regular patient-provider interactions to follow-up on disease progression and control.