arXiv:2608. 08244v1 Announce Type: new Abstract: General wearable foundation models are pretrained across broad sensor streams and populations, but are not designed around women's-health tasks.
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FemWear is a parameter‑efficient wearable foundation model specifically tailored for women's health. It repurposes a pretrained multimodal wearable backbone by training only 239,236 encoder parameters—just 1.11% of the original 21.54M—using low‑rank residual adapters and causal task‑family heads to create a shared longitudinal representation for menstrual, symptom, affective, sleep/recovery, autonomic, activity, and pregnancy outcomes. Evaluations across six cohorts and 63 metrics show improvements in cycle‑phase macro‑F1 and reductions in mean absolute error for cramps, mood symptoms, and sleep problems, while maintaining the OpenMHC ability‑retention benchmark.
arXiv:2411. 15240v5 Announce Type: replace-cross Abstract: Wearable movement data is collected by nearly all commercially available smartwatches and is a valuable resource for mental health research, reflecting fine-grained temporal behavioral trends.
SleepFM-2 is a foundation model trained on 282,511 polysomnography recordings, covering over two million hours of multimodal sleep physiology. It outperforms its predecessor in disease prediction, sleep scoring, and event detection, and its representation improves performance across diverse tasks—including wearable sensing, subjective sleep reports, and transfer to other EEG modalities. When combined with age, sex, and BMI, the model meets stringent discrimination criteria for 215 EHR phenotypes, adding reproducible information beyond demographics for 155 of them.
arXiv:2606. 07692v1 Announce Type: cross Abstract: Foundation models for wearable biosignals have matched or exceeded supervised specialists across a range of clinical tasks, yet all rely on modalities that require deliberate user action--wearing a device or visiting a sleep lab.
arXiv:2607. 15721v1 Announce Type: new Abstract: Cardiometabolic diseases remain among the most persistent drivers of preventable morbidity because diabetes, hypertension, and cardiovascular disease frequently co-occur and share metabolic, vascular, demographic, and behavioral determinants.
arXiv:2606. 18506v1 Announce Type: new Abstract: Objective sleep assessment relies on polysomnography (PSG), yet clinical impact is often better reflected in patient-reported outcomes (PROs) such as sleepiness and fatigue.