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This content will become publicly available on September 15, 2024

Title: 3D-printed epifluidic electronic skin for machine learning–powered multimodal health surveillance

The amalgamation of wearable technologies with physiochemical sensing capabilities promises to create powerful interpretive and predictive platforms for real-time health surveillance. However, the construction of such multimodal devices is difficult to be implemented wholly by traditional manufacturing techniques for at-home personalized applications. Here, we present a universal semisolid extrusion–based three-dimensional printing technology to fabricate an epifluidic elastic electronic skin (e3-skin) with high-performance multimodal physiochemical sensing capabilities. We demonstrate that the e3-skin can serve as a sustainable surveillance platform to capture the real-time physiological state of individuals during regular daily activities. We also show that by coupling the information collected from the e3-skin with machine learning, we were able to predict an individual’s degree of behavior impairments (i.e., reaction time and inhibitory control) after alcohol consumption. The e3-skin paves the path for future autonomous manufacturing of customizable wearable systems that will enable widespread utility for regular health monitoring and clinical applications.

 
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Award ID(s):
2145802
NSF-PAR ID:
10496699
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ;
Publisher / Repository:
Science Advances
Date Published:
Journal Name:
Science Advances
Volume:
9
Issue:
37
ISSN:
2375-2548
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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