Abstract
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 (e
3
-skin) with high-performance multimodal physiochemical sensing capabilities. We demonstrate that the e
3
-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 e
3
-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 e
3
-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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Cited By
185
Sensing and Bio-Sensing Research
Metrics
185
Citations
60
References
Details
Published
Sep 15, 2023
Vol/Issue
9(37)
Cite This Article
Yu Song, Roland Yingjie Tay, Jiahong Li, et al. (2023). 3D-printed epifluidic electronic skin for machine learning–powered multimodal health surveillance. Science Advances, 9(37). https://doi.org/10.1126/sciadv.adi6492
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