FemWear: A Parameter-Efficient Wearable Foundation Model for Women's Health

Abstract

General-purpose wearable foundation models are pretrained on broad sensor streams and populations, but their representations are not organized around women's health. FemWear is a women's wearable foundation model, obtained by parameter-efficiently repurposing a pretrained general multimodal wearable backbone into a specialized representation for women's health. It keeps the pretrained patch projection and Transformer encoder frozen and trains 239,236 encoder parameters - 1.11% of a 21.54M-parameter encoder - through low-rank residual adapters and causal task-family heads, producing one shared longitudinal representation for menstrual, symptom, affective, sleep/recovery, autonomic, activity, and pregnancy outcomes. We evaluate six cohorts with 63 comparable primary metrics, 33 from women's-health cohorts, while retaining the 32-task OpenMHC ability-retention benchmark. On a fixed participant split over three seeds, FemWear improved cycle-phase macro-F1 by 8.15% and reduced mean absolute error for cramps, mood symptoms, and sleep problems by 9.32%, 5.80%, and 9.43%; 24-hour onset AUPRC decreased by 3.40%. A stricter 42-participant nested leave-one-participant-out audit retained positive changes for 24-hour onset (+2.87%), 72-hour onset (+6.35%), and cramps (+2.19%), while phase, mood, and sleep changes were neutral or negative and no endpoint had a strictly positive corrected confidence interval. Capacity-matched experiments beat a latest-day multilayer perceptron but not shared-GRU or multi-gate mixture-of-experts baselines. Train-only calibration reduced onset expected calibration error by 84.2-88.2% with zero temporal-nesting violations. FemWear is therefore a women's wearable foundation model: a reproducible, parameter-efficient specialization delivering targeted transfer across women's-health tasks and coherent probability outputs.