Content area

Abstract

Wearable devices with skin-like mechanical properties enable continuous monitoring of the human body. However, wearable device design has mainly focused on recording superficial signals from the skin thus far, which can only reveal limited information about health and disease. Deep-tissue signals, for example, electrophysiologic, metabolic, circulatory, thermal and mechanical signals, often have stronger correlation with disease and can predict the onset of symptoms. In this Review, we discuss the engineering of soft wearable devices that can sense signals in deep tissues. We highlight electrical, electromagnetic, thermal and mechanical sensing approaches, investigating sensing mechanisms, device designs, fabrication processes and sensing performance, with a focus on penetration depth and temporal and spatial resolutions in the human body. Finally, we discuss remaining challenges in the field and highlight strategies to further improve penetration depth and specificity, accuracy and system-level integration.

Wearable devices can sense physiological signals on the surface of the human body. This Review discusses the design, sensing mechanisms and fabrication of wearable devices that can probe deep-tissue signals, beyond the skin, to provide information about human health and disease.

Details

Title
Soft wearable devices for deep-tissue sensing
Author
Lin, Muyang 1 ; Hu, Hongjie 2 ; Zhou, Sai 2   VIAFID ORCID Logo  ; Xu, Sheng 3   VIAFID ORCID Logo 

 University of California San Diego, Department of NanoEngineering, La Jolla, USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242) 
 University of California San Diego, Materials Science and Engineering Program, La Jolla, USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242) 
 University of California San Diego, Department of NanoEngineering, La Jolla, USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242); University of California San Diego, Materials Science and Engineering Program, La Jolla, USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242); University of California San Diego, Department of Electrical and Computer Engineering, La Jolla, USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242); University of California San Diego, Department of Bioengineering, La Jolla, USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242); University of California San Diego, Department of Radiology, La Jolla, USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242) 
Pages
850-869
Publication year
2022
Publication date
Nov 2022
Publisher
Nature Publishing Group
e-ISSN
20588437
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2731947116
Copyright
© Springer Nature Limited 2022.