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

2026-07-22

AI Summary

This study addresses the challenge of unobtrusively monitoring breastfeeding, a private process traditionally requiring sensors on the infant. The authors propose a caregiver-worn smart garment that, for the first time, leverages body-coupled signals generated by contact between the infant’s mouth and the breast to non-invasively capture both infant electrocardiogram (ECG) and suckling-related acoustic signals from the caregiver’s side. By integrating signal separation, latch-on detection, suck-swallow-breathe (S-S-B) event recognition, and milk intake estimation algorithms, the system enables objective assessment of key feeding metrics. In tests with 10 mother-infant pairs, the system achieved a mean absolute percentage error (MAPE) of 5.56% for latch duration, a mean absolute error (MAE) of 3.61 bpm for heart rate, an S-S-B ratio MAE of 0.12, and a relative milk intake error of 15.76%, while receiving high comfort ratings from users—demonstrating a favorable balance between measurement accuracy and wearability.

Abstract

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.

Problem

• breastfeeding monitoring
• inter-body sensing
• infant physiological signals
• computational garments
• non-invasive sensing

Innovation

• inter-body sensing
• computational garments
• breastfeeding monitoring
• non-invasive infant sensing
• suck-swallow-breathe ratio