Cloud‐Integrated Smart Nanomembrane Wearables for Remote Wireless Continuous Health Monitoring of Postpartum Women

Author:

Matthews Jared12,Soltis Ira12,Villegas‐Downs Michelle3,Peters Tara A.3,Fink Anne M.4,Kim Jihoon12,Zhou Lauren12,Romero Lissette12,McFarlin Barbara L.3,Yeo Woon‐Hong1256ORCID

Affiliation:

1. IEN Center for Wearable Intelligent Systems and Healthcare at the Institute for Electronics and Nanotechnology Georgia Institute of Technology Atlanta GA 30332 USA

2. George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta GA 30332 USA

3. Department of Human Development Nursing Science College of Nursing University of Illinois Chicago 845 S. Damen Ave., MC 802 Chicago IL 60612 USA

4. Department of Biobehavioral Nursing Science College of Nursing University of Illinois Chicago 845 S. Damen Ave., MC 802 Chicago IL 60612 USA

5. Wallace H. Coulter Department of Biomedical Engineering Georgia Tech and Emory University School of Medicine Atlanta GA 30332 USA

6. Parker H. Petit Institute for Bioengineering and Biosciences Institute for Materials Institute for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta GA 30332 USA

Abstract

AbstractNoncommunicable diseases (NCD), such as obesity, diabetes, and cardiovascular disease, are defining healthcare challenges of the 21st century. Medical infrastructure, which for decades sought to reduce the incidence and severity of communicable diseases, has proven insufficient in meeting the intensive, long‐term monitoring needs of many NCD disease patient groups. In addition, existing portable devices with rigid electronics are still limited in clinical use due to unreliable data, limited functionality, and lack of continuous measurement ability. Here, a wearable system for at‐home cardiovascular monitoring of postpartum women—a group with urgently unmet NCD needs in the United States—using a cloud‐integrated soft sternal device with conformal nanomembrane sensors is introduced. A supporting mobile application provides device data to a custom cloud architecture for real‐time waveform analytics, including medical device‐grade blood pressure prediction via deep learning, and shares the results with both patient and clinician to complete a robust and highly scalable remote monitoring ecosystem. Validated in a month‐long clinical study with 20 postpartum Black women, the system demonstrates its ability to remotely monitor existing disease progression, stratify patient risk, and augment clinical decision‐making by informing interventions for groups whose healthcare needs otherwise remain unmet in standard clinical practice.

Funder

National Institutes of Health

National Science Foundation

Publisher

Wiley

Subject

General Physics and Astronomy,General Engineering,Biochemistry, Genetics and Molecular Biology (miscellaneous),General Materials Science,General Chemical Engineering,Medicine (miscellaneous)

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