Min Seo Kim

Papers

Total Citations

250

H-Index

About

Min Seo Kim is a pioneering researcher whose work bridges the precision of robotic surgery with the innovation of bioinspired wearable technology. His primary research areas include surgical learning curves, robotic gastrectomy, and stretchable strain sensors for motion monitoring. Kim’s major contribution lies in systematically defining the complication-based learning curve for robotic surgery, a framework that helps surgeons identify and mitigate risks during the critical early phase of adopting robotic techniques. His landmark 2019 study on the comprehensive learning curve of robotic surgery, with 131 citations, provides essential evidence for training protocols and patient safety. In parallel, Kim developed the omni-purpose stretchable strain sensor (OPSS), inspired by the spider sensory system, which uses a highly dense nanocracking structure to monitor whole-body motions from joint-level to skin-level movements. This 2017 paper, with 116 citations, demonstrates his ability to translate biological principles into practical engineering solutions. Kim’s work has significant implications for surgical education, rehabilitation, and human-machine interfaces, establishing him as a versatile contributor to both clinical and materials science fields.