Mammal: Supporting Breastfeeding Monitoring Through Computational Garments with Inter-Body Sensing

Yanfeng Zhao, Morgan Geck, Kate Fernandez, Madison Nicole Jones, Xia Zhou, Jessica L. Ridgway, Te-Yen Wu

Breastfeeding provides critical insight into infant feeding competence and physiological health, yet objective monitoring remains difficult due to the intimate and internal nature of feeding. We present Mammal, a caregiver-worn computational garment that unobtrusively monitors breastfeeding without attaching sensors to the infant. Mammal leverages inter-body signal transmission through natural mouth-to-breast contact to capture infant cardiac and feeding-related acoustic signals on the caregiver's body. Using novel algorithms to detect latch onset, infer infant electrocardiogram (ECG), and identify suck and swallow events from inter-body signals, Mammal estimates latch duration, in-feeding heart rate, suck-swallow-breathe (SSB) ratio, and milk intake. In a user study with 10 caregiver-infant dyads, Mammal achieves a mean absolute percentage error (MAPE) of 5.56% for latch duration, a mean absolute error (MAE) of 3.61 bpm for infant heart rate estimation, a mean absolute error of 0.12 for SSB ratio estimation, and a mean relative error of 15.76% for milk intake, with participants reporting high comfort and wearability.