Removing wearables from physiological monitoring also removes their supervision: the signal indicating where and when a stress response occurred. Contactless stress sensing therefore becomes a weakly supervised evidence-localization problem, where a clip-level label must be traced to the body regions and moments that produced it.
arXiv:2608. 16087v1 Announce Type: cross Abstract: Removing wearables from physiological monitoring also removes their supervision: the signal indicating where and when a stress response occurred.
arXiv:2608. 14947v1 Announce Type: new Abstract: Detecting opioid craving from wearable physiological signals is critical yet difficult, with the potential to support proactive interventions for individuals with opioid use disorder (OUD).
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.
arXiv:2606. 24985v1 Announce Type: new Abstract: Personalization in wearable-based stress detection remains challenging due to substantial inter-individual variability in physiological and behavioral responses.
arXiv:2606. 00345v1 Announce Type: new Abstract: Wearable and mobile sensing technologies enable continuous monitoring of human behavior and health in real-world settings.
arXiv:2606. 09605v1 Announce Type: new Abstract: Foundation models offer a promising route to compress multi-modal physiological signals into compact representations of human health, with broad applications across sleep medicine, cardiology, neurology and other healthcare domains.
WearableQA is a new benchmark that tests AI systems on health reasoning using real-world wearable data from 200 users, each with up to 500 days of daily measurements. It contains 4,084 ten‑option multiple‑choice questions derived from wearable time series, blood biomarkers, and demographics, and is organized into 16 question types that distinguish data‑driven computation from physiological interpretation and single‑signal from cross‑signal reasoning. Evaluation of 14 large language models shows wide performance gaps, indicating that the benchmark remains challenging and useful for diagnosing model capabilities.
arXiv:2608. 09240v1 Announce Type: cross Abstract: Multimodal federated learning (FL) supports collaborative modeling in privacy-sensitive health-sensing and medical settings, but realistic deployments often exhibit dual-axis modality missingness: clients have different modality sets, and individual samples may contain only subsets of the modalities available locally.
The paper introduces a new evaluation protocol for deep multivariate imputation models on wearable device data, addressing the issue of structured missingness where sensor features drop out together. Using a Garmin smartwatch dataset from an epilepsy patient, the authors generate realistic block-missing patterns from training data and show that matching the training protocol to this distribution reduces BRITS’ mean absolute error by 43%. They also extend BRITS with time‑of‑day encoding and compare it to linear interpolation and SAITS, finding that no single model dominates and that model rankings vary with evaluation design.
The study introduces a clinician‑in‑the‑loop benchmark to assess whether large language models can generate evidence‑grounded Brief Hierarchical Taxonomy of Psychopathology (B‑HiTOP) item profiles from multimodal data, including passive sensing, ecological momentary assessment, and questionnaires. Using the GLOBEM dataset, the authors create 14,592 participant‑day instances aligned to 29 B‑HiTOP items across five spectra, and evaluate evidence compatibility rather than diagnostic accuracy. Two‑stage prediction improves compatibility for EMA and questionnaire evidence but reduces it for passive sensing and combined evidence, yielding more conservative score distributions across models, spectra, and evidence settings.
Falls are a major health concern for older adults, and wearable sensors have been widely explored for detecting falls and enabling timely intervention. However, real-world falls are extremely rare: collecting 100 of them requires an estimated 100,000 days of monitoring, resulting in severely limited labelled data for training machine learning models.
FedCoRe is a federated learning framework that addresses missing modalities in healthcare by learning representation- or logit-space corrections instead of generating synthetic data. In a MIMIC-derived respiratory deterioration task, the method uses paired examples where a modality is present during training but may be absent at deployment, allowing only those clients to update the completion module. Experiments show that FedCoRe can recover roughly half of the performance lost when ECG or CXR data are hidden, but the framework should only be deployed when paired examples and validation evidence confirm the modality’s presence.