Yun Soung Kim, Ph.D. is an assistant professor of Radiology in the BioMedical Engineering and Imaging Institute at the Icahn School of Medicine at Mount Sinai. His research focuses on developing soft, stretchable, and wireless electronic systems that can gently integrate with the skin for various applications in healthcare research. Dr. Kim received BS and MS in Materials Science and Engineering from the University of Illinois Urbana-Champaign in 2009 and 2012, respectively, and PhD in Bioengineering from the University of California San Diego in 2017. In 2021, Dr. Kim completed his postdoctoral training at Georgia Institute of Technology, where he served as Research Faculty until 2022. Leveraging a wide range of emerging nanomanufacturing technologies, such as MEMS, printing, laser micromachining, and electronic chip integration, Dr. Kim strives to translate the concepts of smart medicine and human-machine interfaces into practical devices deployable in both clinical and home settings.
Title of Talk: Soft, Stretchable, and Smart Bioelectronics for Advancing Healthcare and Wellness
Thin skin-wearable devices, sometimes referred to as epidermal electronics, found in many research articles fall short as concept‐only representation due to the fundamental discrepancy in the mechanics of thin-film materials and rigid essential components. This talk introduces a set of engineering solutions to overcoming the challenges in manufacture and assembly of epidermal electronics and the soft wearable bioelectronics platform in general. Strategic integration of thin‐film electronics with soft elastomers allows the stretchable biopotential electrodes to maintain the conformal contact with the skin while the integrated circuit deforms naturally with the body. The stretchable electrodes with optimized design and structure for intimate skin integration are capable to perform high-fidelity electrophysiology and accurate analysis of the skin’s electrical properties, such as the galvanic skin responses. Moreover, direct integration of small, off-the-shelf chip sensors (e.g., accelerometer, pulse oximeter, and microphone) with a stretchable electronic platform opens the possibility for concurrent monitoring of multiple physiological parameters, while providing researchers with freedom of device placement on the body. Implementation of convolutional neural networks for real‐time physiological classifications demonstrates the feasibility of multifaceted analysis with a high clinical relevance. In vivo demonstrations with animals and human subjects in various scenarios reveal the true potential of the soft bioelectronics as both a powerful research tool and a game-changer for health monitoring at home and care units.
Time: Oct. 20, 2022 12:00 PM Eastern Time
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Meeting ID: 869 5009 3418
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Meeting ID: 869 5009 3418
