Mammal：通过具有身体间传感功能的计算服装支持母乳喂养监测
Mammal: Supporting Breastfeeding Monitoring Through Computational Garments with Inter-Body Sensing
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中文总结 AI 辅助
研究旨在解决母乳喂养客观监测难的问题，核心方法是通过护理人员穿戴的计算服装，利用身体间信号传输及算法监测相关信号，实现 latch 持续时间等指标的估计，且在用户研究中取得较好效果，舒适度高。
中文摘要 AI 辅助
母乳喂养能为婴儿喂养能力和生理健康提供关键见解，但因其亲密和内在性质，客观监测困难。我们提出了Mammal，这是一种供护理人员穿戴的计算服装，无需在婴儿身上附着传感器就能不显眼地监测母乳喂养。Mammal利用通过自然的口到乳房接触进行的身体间信号传输，在护理人员身体上捕捉婴儿心脏和喂养相关的声学信号。使用新颖算法检测 latch 开始、推断婴儿心电图（ECG）并从身体间信号识别吸吮和吞咽事件，Mammal估计 latch 持续时间、喂养时心率、吸吮 - 吞咽 - 呼吸（SSB）比率和奶量摄入。在一项针对10对护理人员 - 婴儿二元组的用户研究中，Mammal在 latch 持续时间上实现了5.56%的平均绝对百分比误差（MAPE），在婴儿心率估计上实现了3.61次/分钟的平均绝对误差（MAE），在SSB比率估计上实现了0.12的平均绝对误差，在奶量摄入上实现了15.76%的平均相对误差，参与者报告了高舒适度和可穿戴性。
英文摘要
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.
