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FLOW Measures of breastfeeding and infant sucking behavior

Sufficient milk intake by newborns is important. Not all infants are able to self-regulate the amount of breastfeeding that is needed, while sufficient nutrient intake is crucial, especially in the first 6 months. Current breastfeeding assessment methods are invasive, or inaccurate and may affect normal feeding practices.
The aim of this proposal is to take the first steps in developing a breastfeeding assessment device that is able to measure direct milk intake and sucking behavior of infants in a non-invasive manner. It aims to transform sensing principles into wearable, non-intrusive assessment devices (by integration in attributes such as bibs, hairbands, or skin stickers). Different assessment methods will be explored, such as motion sensors to detect swallow-movement, strain sensors to detect breast volume changes/swallow movements, or imaging techniques (such as laser-doppler) to assess milk-flow. A literature study combined with a stakeholder analysis will be done on how to best capture breastfeeding and sucking behavior. Based on this we will define a clear set of requirements. The most promising method will be further developed into a first wearable (rapid) prototype. Using the prototype, suitable analysis techniques will be explored to account for proper event detection using time series threshold analysis vs machine learning. Additionally, IoT integration is critical for ensuring non-obtrusive data collection.  The development of such breastfeeding sensor will aid in monitoring milk intake of infants thereby contributing to infant health.