A fully integrated wearable ultrasound system to monitor deep tissues in moving subjects

Recent advances in wearable ultrasound technologies have demonstrated the potential for hands-free data acquisition, but technical barriers remain as these probes require wire connections, can lose track of moving targets and create data-interpretation challenges. Here we report a fully integrated a...

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Published inNature biotechnology Vol. 42; no. 3; pp. 448 - 457
Main Authors Lin, Muyang, Zhang, Ziyang, Gao, Xiaoxiang, Bian, Yizhou, Wu, Ray S., Park, Geonho, Lou, Zhiyuan, Zhang, Zhuorui, Xu, Xiangchen, Chen, Xiangjun, Kang, Andrea, Yang, Xinyi, Yue, Wentong, Yin, Lu, Wang, Chonghe, Qi, Baiyan, Zhou, Sai, Hu, Hongjie, Huang, Hao, Li, Mohan, Gu, Yue, Mu, Jing, Yang, Albert, Yaghi, Amer, Chen, Yimu, Lei, Yusheng, Lu, Chengchangfeng, Wang, Ruotao, Wang, Joseph, Xiang, Shu, Kistler, Erik B., Vasconcelos, Nuno, Xu, Sheng
Format Journal Article
LanguageEnglish
Published New York Nature Publishing Group US 01.03.2024
Nature Publishing Group
Subjects
631/443
692/700
Agriculture
Algorithms
Bandwidths
Bioinformatics
Biomedical and Life Sciences
Biomedical Engineering/Biotechnology
Biomedicine
Biotechnology
Blood pressure
Cardiac output
Circuit design
Circuits
Computer engineering
Data acquisition
Data communication
Data interpretation
Design
Heart rate
Humans
Learning algorithms
Life Sciences
Machine learning
Moving targets
Physiology
Preconditioning
Receivers & amplifiers
Skin
Surveillance
Tracking
Ultrasonic imaging
Ultrasound
Veins & arteries
Vital Signs
Wearable Electronic Devices
Wearable technology
Wireless communications
Online AccessGet full text

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Abstract Recent advances in wearable ultrasound technologies have demonstrated the potential for hands-free data acquisition, but technical barriers remain as these probes require wire connections, can lose track of moving targets and create data-interpretation challenges. Here we report a fully integrated autonomous wearable ultrasonic-system-on-patch (USoP). A miniaturized flexible control circuit is designed to interface with an ultrasound transducer array for signal pre-conditioning and wireless data communication. Machine learning is used to track moving tissue targets and assist the data interpretation. We demonstrate that the USoP allows continuous tracking of physiological signals from tissues as deep as 164 mm. On mobile subjects, the USoP can continuously monitor physiological signals, including central blood pressure, heart rate and cardiac output, for as long as 12 h. This result enables continuous autonomous surveillance of deep tissue signals toward the internet-of-medical-things. A wearable ultrasound patch monitors subjects in motion using machine learning and wireless electronics.
AbstractList Recent advances in wearable ultrasound technologies have demonstrated the potential for hands-free data acquisition, but technical barriers remain as these probes require wire connections, can lose track of moving targets and create data-interpretation challenges. Here we report a fully integrated autonomous wearable ultrasonic-system-on-patch (USoP). A miniaturized flexible control circuit is designed to interface with an ultrasound transducer array for signal pre-conditioning and wireless data communication. Machine learning is used to track moving tissue targets and assist the data interpretation. We demonstrate that the USoP allows continuous tracking of physiological signals from tissues as deep as 164 mm. On mobile subjects, the USoP can continuously monitor physiological signals, including central blood pressure, heart rate and cardiac output, for as long as 12 h. This result enables continuous autonomous surveillance of deep tissue signals toward the internet-of-medical-things.
Recent advances in wearable ultrasound technologies have demonstrated the potential for hands-free data acquisition, but technical barriers remain as these probes require wire connections, can lose track of moving targets and create data-interpretation challenges. Here we report a fully integrated autonomous wearable ultrasonic-system-on-patch (USoP). A miniaturized flexible control circuit is designed to interface with an ultrasound transducer array for signal pre-conditioning and wireless data communication. Machine learning is used to track moving tissue targets and assist the data interpretation. We demonstrate that the USoP allows continuous tracking of physiological signals from tissues as deep as 164 mm. On mobile subjects, the USoP can continuously monitor physiological signals, including central blood pressure, heart rate and cardiac output, for as long as 12 h. This result enables continuous autonomous surveillance of deep tissue signals toward the internet-of-medical-things. A wearable ultrasound patch monitors subjects in motion using machine learning and wireless electronics.
Recent advances in wearable ultrasound technologies have demonstrated the potential for hands-free data acquisition, but technical barriers remain as these probes require wire connections, can lose track of moving targets and create data-interpretation challenges. Here we report a fully integrated autonomous wearable ultrasonic-system-on-patch (USoP). A miniaturized flexible control circuit is designed to interface with an ultrasound transducer array for signal pre-conditioning and wireless data communication. Machine learning is used to track moving tissue targets and assist the data interpretation. We demonstrate that the USoP allows continuous tracking of physiological signals from tissues as deep as 164 mm. On mobile subjects, the USoP can continuously monitor physiological signals, including central blood pressure, heart rate and cardiac output, for as long as 12 h. This result enables continuous autonomous surveillance of deep tissue signals toward the internet-of-medical-things.A wearable ultrasound patch monitors subjects in motion using machine learning and wireless electronics.
Recent advances in wearable ultrasound technologies have demonstrated the potential for hands-free data acquisition, but technical barriers remain as these probes require wire connections, can lose track of moving targets and create data-interpretation challenges. Here we report a fully integrated autonomous wearable ultrasonic-system-on-patch (USoP). A miniaturized flexible control circuit is designed to interface with an ultrasound transducer array for signal pre-conditioning and wireless data communication. Machine learning is used to track moving tissue targets and assist the data interpretation. We demonstrate that the USoP allows continuous tracking of physiological signals from tissues as deep as 164 mm. On mobile subjects, the USoP can continuously monitor physiological signals, including central blood pressure, heart rate and cardiac output, for as long as 12 h. This result enables continuous autonomous surveillance of deep tissue signals toward the internet-of-medical-things.Recent advances in wearable ultrasound technologies have demonstrated the potential for hands-free data acquisition, but technical barriers remain as these probes require wire connections, can lose track of moving targets and create data-interpretation challenges. Here we report a fully integrated autonomous wearable ultrasonic-system-on-patch (USoP). A miniaturized flexible control circuit is designed to interface with an ultrasound transducer array for signal pre-conditioning and wireless data communication. Machine learning is used to track moving tissue targets and assist the data interpretation. We demonstrate that the USoP allows continuous tracking of physiological signals from tissues as deep as 164 mm. On mobile subjects, the USoP can continuously monitor physiological signals, including central blood pressure, heart rate and cardiac output, for as long as 12 h. This result enables continuous autonomous surveillance of deep tissue signals toward the internet-of-medical-things.
Author Yin, Lu
Park, Geonho
Xu, Sheng
Lu, Chengchangfeng
Hu, Hongjie
Wang, Chonghe
Yaghi, Amer
Zhang, Zhuorui
Yang, Xinyi
Zhang, Ziyang
Qi, Baiyan
Gu, Yue
Lin, Muyang
Gao, Xiaoxiang
Xu, Xiangchen
Yang, Albert
Wang, Ruotao
Li, Mohan
Zhou, Sai
Xiang, Shu
Wu, Ray S.
Yue, Wentong
Chen, Xiangjun
Chen, Yimu
Vasconcelos, Nuno
Lou, Zhiyuan
Kang, Andrea
Wang, Joseph
Huang, Hao
Mu, Jing
Bian, Yizhou
Kistler, Erik B.
Lei, Yusheng
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  organization: Materials Science and Engineering Program, University of California San Diego, Department of Neurosurgery, Yale University
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/37217752$$D View this record in MEDLINE/PubMed
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2023. The Author(s), under exclusive licence to Springer Nature America, Inc.
Copyright_xml – notice: The Author(s), under exclusive licence to Springer Nature America, Inc. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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Snippet Recent advances in wearable ultrasound technologies have demonstrated the potential for hands-free data acquisition, but technical barriers remain as these...
SourceID proquest
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crossref
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StartPage 448
SubjectTerms 631/443
692/700
Agriculture
Algorithms
Bandwidths
Bioinformatics
Biomedical and Life Sciences
Biomedical Engineering/Biotechnology
Biomedicine
Biotechnology
Blood pressure
Cardiac output
Circuit design
Circuits
Computer engineering
Data acquisition
Data communication
Data interpretation
Design
Heart rate
Humans
Learning algorithms
Life Sciences
Machine learning
Moving targets
Physiology
Preconditioning
Receivers & amplifiers
Skin
Surveillance
Tracking
Ultrasonic imaging
Ultrasound
Veins & arteries
Vital Signs
Wearable Electronic Devices
Wearable technology
Wireless communications
Title A fully integrated wearable ultrasound system to monitor deep tissues in moving subjects
URI https://link.springer.com/article/10.1038/s41587-023-01800-0
https://www.ncbi.nlm.nih.gov/pubmed/37217752
https://www.proquest.com/docview/2956984643
https://www.proquest.com/docview/2818053628
Volume 42
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