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.