In a recent study published in Science Advances, researchers from the California Institute of Technology, led by Dr. Wei Gao, have developed a machine learning (ML)–powered 3D-printed epifluidic electronic skin for multimodal health surveillance. This wearable platform enables real-time physical and chemical monitoring of health status.
Wearable health devices have the potential to revolutionize the medical world, offering real-time tracking, personalized treatments, and early diagnosis of diseases.
However, one of the main challenges with these devices is that they don’t track data at the molecular level, and their fabrication is challenging. Dr. Gao explained why this served as a motivation for their team.
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