Epidermal mechano-acoustic sensing electronics for cardiovascular diagnostics and human-machine interfaces

By Yuhao Liu, James J. S. Norton, Raza Qazi, Zhanan Zou, Kaitlyn R. Ammann, Hank Liu, Lingqing Yan, P. Tran, Kyung-In Jang, Jung Woo Lee and 9 more. Science Advances, 2016

357 citations recorded by the source corpus.

Researchers report advances in materials and designs for skin-integrated devices capable of measuring acoustic signatures. Physiological mechano-acoustic signals, often with frequencies and intensities that are beyond those associated with the audible range, provide information of great clinical utility. Stethoscopes and digital accelerometers in conventional packages can capture some relevant data, but neither is suitable for use in a continuous, wearable mode, and both have shortcomings associated with mechanical transduction of signals through the skin. We report a soft, conformal class of device configured specifically for mechano-acoustic recording from the skin, capable of being used on nearly any part of the body, in forms that maximize detectable signals and allow for multimodal operation, such as electrophysiological recording. Experimental and computational studies highlight the key roles of low effective modulus and low areal mass density for effective operation in this type of measurement mode on the skin. Demonstrations involving seismocardiography and heart murmur detection in a series of cardiac patients illustrate utility in advanced clinical diagnostics. Monitoring of pump thrombosis in ventricular assist devices provides an example in characterization of mechanical implants. Speech recognition and human-machine interfaces represent additional demonstrated applications. These and other possibilities suggest broad-ranging uses for soft, skin-integrated digital technologies that can capture human body acoustics.

Subfields: Biomedical Engineering, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/5a54a64e8531fc24d9a8aadfba66a62c9189e965

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Chengkuo Lee
2. Tianyiyi He
3. Feng Wen
4. Qiongfeng Shi
5. Jianyong Ouyang
6. Tao Chen
7. Hao Wang
8. Xinge Guo

In Cognitive Neuroscience

1. Chengkuo Lee
2. Tianyiyi He
3. Feng Wen
4. Qiongfeng Shi
5. Xinge Guo
6. Long Liu
7. Zixuan Zhang

Citations in the corpus

• Beyond energy harvesting - multi-functional triboelectric nanosensors on a textile (2019, background): “This work was supported by HiFES Seed Funding-2017-01 grant (R-263-501-012-133) “Hybrid Integration of Flexible Power Source and Pressure Sensors” at the National University of Singapore; Faculty Research Committee (FRC) grant (R-263-000-B56-112) “Thermoelectric power generator (TEG) based self-powe”