3D-printed epifluidic electronic skin for machine learning–powered multimodal health surveillance
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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Logan Li, Shelby Hughes · 2024
- Published
- Sep 15, 2023
- Vol/Issue
- 9(37)
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