FemWear: A Parameter-Efficient Wearable Foundation Model for Women's Health
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, activit...
| Authors | Yifan Wang, Chenzhong Li |
|---|---|
| Primary category | cs.AI |
| Published | 8 August 2026 |
| Last updated at source | 2 September 2026 |
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