A31 – Chan/CMU

Grant Funding: Making Obstetric Care Smart

Justin Chan's lab received ARPA-H funding to design the AI/ML algorithms for a wearable monitoring system to better identify fetal distress and its cause, enabling a safer labor and delivery experience for mothers and babies. The system, called OMEGA, or Optical, Mechanical, and Electrical Global Assessment of fetal hypoxia, aims to replace 50-year-old, indirect, unreliable fetal heart rate monitoring technology with a unified, real-time assessment of fetal oxygen delivery and adaptive capacity. Source: https://engineering.cmu.edu/news-events/news/2026/06/23-transform-childbirth-care.html

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Publication: Measuring multi-site pulse transit time with an AI-enabled mmWave radar

Authors: Jiangyifei Zhu, Kuang Yuan, Akrash Prabhakara, Yunzhi Li, Gongwei Wang, Kelly Michaelsen, Justin Chan & Swarun Kumar Abstract Pulse Transit Time (PTT) is a measure of arterial stiffness and a physiological marker associated with cardiovascular function, with an inverse relationship to diastolic blood pressure (DBP). We present an AI-enabled mmWave system for contactless multi-site PTT measurement using a single radar. By leveraging radar beamforming and deep learning algorithms our system simultaneously measures PTT and estimates diastolic blood pressure at multiple sites. The system was evaluated across three physiological pathways – heart-to-radial artery, heart-to-carotid artery, and mastoid area-to-radial artery – achieving correlation coefficients of 0.75–0.86 compared to contact-based reference sensors for measuring PTT. Furthermore, the system demonstrated correlation coefficients of 0.90–0.91 for estimating DBP, and achieved a mean error of -0.62–0.06 mmHg and standard deviation…

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