Project Description:

80% of new mothers want to exclusively breastfeed their infant; however, only 41% meet this goal. The most common reason why new mothers cease exclusive breastfeeding is because of concerns over adequate milk intake. At-home timing of feedings is often used to quantify this, but this method can be mentally exhausting and prone to error3. A more accurate assessment can be made using baby scales, but small changes in weight during feedings make it difficult to use a scale accurately without training. There is a need for an accessible solution that allows easy assessment of a newborn’s milk intake at home. We developed a mobile application that uses a deep learning model to estimate milk transfer efficiency. Through the detection of audible rhythmic swallowing, a sign of consistent milk intake, this app can provide parents with reassurance that their newborn is receiving enough milk when breastfeeding.

Project Photo:

The image shows a cartoon mother using the LactaLearn app to monitor her breastfeeding session. The phone is positioned near the baby’s throat to capture swallowing sounds.

Student Team Members

• Veronica Kidwell
• Eric Song
• Shalika Subramanian
• Rida Danish
• Christina Heal-Kowal
• Iris Zheng
• Mackenzie Petersen
• David Lu

Course Faculty

• Michelle Zwernemann

Project Mentors, Sponsors, and Partners

• Azadeh Farzin, MD
• Monique Solieau-Burke, MD
• Elizabeth Logsdon, PhD
• Unnathi Annapurna Shashikumar