3D-printed epifluidic electronic skin for machine learning–powered multimodal health surveillance
Yu Song, Roland Yingjie Tay, Jiahong Li, Changhao Xu, Jihong Min, Ehsan Shirzaei Sani, Gwangmook Kim, Wenzheng Heng, In Ho 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 (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.