구자훈 (세종대학교)

한빛사논문

조회197

세종대학교

CV File 147 kb

Annu. Rev. Biomed. Eng., Jul 2024, 26 (1) | https://doi.org/10.1146/annurev-bioeng-103122-032652 Electronic Skin: Opportunities and Challenges in Convergence with Machine Learning

Ja Hoon Koo^1*, Young Joong Lee^2,3*, Hye Jin Kim^4,5*, Wojciech Matusik^2,3, Dae-Hyeong Kim^4,5,6,7, and Hyoyoung Jeong⁸

¹Department of Semiconductor Systems Engineering and Institute of Semiconductor and System IC, Sejong University, Seoul, Republic of Korea

²Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

³Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

⁴Center for Nanoparticle Research, Institute for Basic Science, Seoul, Republic of Korea

⁵School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul, Republic of Korea

⁶Department of Materials Science and Engineering, Seoul National University, Seoul, Republic of Korea

⁷Interdisciplinary Program for Bioengineering, Seoul National University, Seoul, Republic of Korea

⁸Department of Electrical and Computer Engineering, University of California, Davis, California, USA

*These authors contributed equally to this article.

Corresponding authors : Dae-Hyeong Kim, Hyoyoung Jeong

Abstract

Recent advancements in soft electronic skin (e-skin) have led to the development of human-like devices that reproduce the skin's functions and physical attributes. These devices are being explored for applications in robotic prostheses as well as for collecting biopotentials for disease diagnosis and treatment, as exemplified by biomedical e-skins. More recently, machine learning (ML) has been utilized to enhance device control accuracy and data processing efficiency. The convergence of e-skin technologies with ML is promoting their translation into clinical practice, especially in healthcare. This review highlights the latest developments in ML-reinforced e-skin devices for robotic prostheses and biomedical instrumentations. We first describe technological breakthroughs in state-of-the-art e-skin devices, emphasizing technologies that achieve skin-like properties. We then introduce ML methods adopted for control optimization and pattern recognition, followed by practical applications that converge the two technologies. Lastly, we briefly discuss the challenges this interdisciplinary research encounters in its clinical and industrial transition.

논문정보

형식Review Paper
게재일2024년 07월 (BRIC 등록일 2024-07-16)
연구진국내(교신)+국외 연구진
분야 바이오·의료융합 > 바이오센싱 및 나노바이오물질

댓글 작성 로그인

CV 열람하기

연락처 보기