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This study introduces Mammal, a caregiver-worn computational garment designed to monitor breastfeeding by utilizing inter-body signal transmission through natural mouth-to-breast contact. By employing advanced algorithms, Mammal accurately estimates critical breastfeeding metrics such as latch duration, infant heart rate, suck-swallow-breathe ratio, and milk intake without requiring sensors on the infant. In a user study involving 10 caregiver-infant dyads, Mammal demonstrated impressive accuracy with a mean absolute percentage error of 5.56% for latch duration and high user comfort ratings.
Mammal achieves remarkable accuracy in breastfeeding monitoring without invasive sensors on infants, revolutionizing how we assess feeding competence and health.
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.