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

Summary

CUHK-Shenzhen team takes Stanford's open OpenMHC wearable backbone (frozen, 21.5M params) and adds a tiny (1.11%, 239K param) low-rank adapter plus task-family heads to specialize it for women's-health outcomes — menstrual phase/onset, symptom severity (cramps, mood, sleep), pregnancy, and general ability retention. It is explicitly not a new pretraining effort: it's a parameter-efficient fine-tuning (PEFT) recipe layered on someone else's foundation model, evaluated on six cohorts including the hormone-verified mcPHASES dataset (42 participants, Fitbit + urinary LH/estrogen/progesterone).