Wearable imaging system could provide insight into preterm birth

Chuan Wang, Shantanu Chakrabartty, Yong Wang developing AI-integrated device

Beth Miller 
 A team of WashU researchers in McKelvey Engineering and WashU Medicine is developing an at-home wearable device that would monitor electrical and mechanical signals in the uterus during pregnancy and labor and provide information about preterm birth. (Credit: Chuan Wang)
A team of WashU researchers in McKelvey Engineering and WashU Medicine is developing an at-home wearable device that would monitor electrical and mechanical signals in the uterus during pregnancy and labor and provide information about preterm birth. (Credit: Chuan Wang)

Preterm birth, or delivery before 37 weeks of gestation, affects about 10% of pregnancies worldwide and is the leading cause of infant death, yet the causes behind it are poorly understood.

 A team of researchers at Washington University in St. Louis is developing an at-home wearable device that would monitor electrical and mechanical signals in the uterus during pregnancy and labor, with a four-year, $920,769 grant from the National Institutes of Health. Chuan Wang, associate professor, and Shantanu Chakrabartty, the Clifford W. Murphy Professor and vice dean for research and graduate education, both in the Preston M. Green Department of Electrical & Systems Engineering in the McKelvey School of Engineering; and Yong Wong, professor of obstetrics & gynecology and of radiology at WashU Medicine and of biomedical engineering and of electrical & systems engineering in McKelvey Engineering, are leading the research.

The device, made up of soft, stretchable, wireless sensors, would provide insight into uterine activity and provide that data into a specially designed platform that will use machine learning algorithms for better signal quality and real-time monitoring. The electrodes on the abdomen would simultaneously record high-quality electrical and mechanical signals from the mother and the fetus, generating a 3D map of uterine contraction activities. 

The team plans to design its own instruments to use in a portable device that is connected to its proposed imaging system to record data, synchronize it, and transfer the data wirelessly. It will also validate its design on human subjects to develop machine learning algorithms to classify and predict term and preterm birth.

“This proposed system has the potential to improve early detection and management of preterm labor, reducing fetal mortality and improving maternal-fetal health outcomes,” Wang said. “It could also advance our understanding of uterine physiology, facilitate personalized obstetric care and provide new tools for remote prenatal monitoring.

Previously, the team created a battery-powered portable instrumentation that connects with a stretchy patch that places electrodes around the wearer’s abdomen. The electrodes detect both maternal heartbeat and EMG signals, which correspond to uterine contractions. Wang created flexible electrodes that Chakrabartty’s group used to build the device. 

The team is working with WashU’s Office of Technology Management.


The McKelvey School of Engineering at Washington University in St. Louis promotes independent inquiry and education with an emphasis on scientific excellence, innovation and collaboration without boundaries. McKelvey Engineering has top-ranked research and graduate programs across departments, particularly in biomedical engineering, environmental engineering and computing, and has one of the most selective undergraduate programs in the country. With 165 full-time faculty, 1,524 undergraduate students, 1,554 graduate students and 22,000 living alumni, we are working to solve some of society’s greatest challenges; to prepare students to become leaders and innovate throughout their careers; and to be a catalyst of economic development for the St. Louis region and beyond.

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