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

3D 打印表流体电子皮肤,用于机器学习驱动的多模式健康监测

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作者:Yu Song, Roland Yingjie Tay, Jiahong Li, Changhao Xu, Jihong Min, Ehsan Shirzaei Sani, Gwangmook Kim, Wenzheng Heng, Inho Kim, Wei Gao

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 (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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