From muscle to motion: GaIn nanoparticle-TPU core/shell mesh electrodes for intelligent prosthesis.

Journal: Materials horizons
Published Date:

Abstract

Electromyography (EMG) electrodes are critical for detecting and interpreting muscle activity, which is essential for operating prosthetic devices and wearable robots. Traditional EMG electrodes, however, often face limitations such as being uncomfortable, lacking stretchability, and wearing out quickly. To overcome these challenges, we developed an innovative EMG wristband with mesh electrodes created using charge-reverse electro writing (CREW). The wristband is tailored to fit the unique muscle distribution of the user, featuring a special fiber with a core/shell structure. The core, enriched with liquid metal nanoparticles (LM-NPs), ensures excellent electrical conductivity, while the thermoplastic polyurethane (TPU) shell enhances flexibility, durability, and washability. The wristband is also designed for long-term comfort, with a breathable 3D scaffold structure that allows natural skin ventilation. Even under maximum strain, it maintains high signal clarity, achieving a signal-to-noise ratio (SNR) of over 30 decibels. The signals are processed through a machine learning algorithm, the multilayer perceptron (MLP), with minimal delay time, enabling smooth and human-like motor movements in prosthetic devices. This breakthrough addresses key challenges in traditional electrodes, providing a reliable, high-performance solution for wearable robotics and assistive technologies, with a focus on comfort, durability, and seamless integration into everyday life.

Authors

  • Yeonjee Jeon
    School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. [email protected].
  • Seongjun Moon
    Machining Technology Research Group 2, Hwacheon Machine Tool Co., Ltd., Gwangju 62227, Korea.
  • Chanho Jung
    Department of Electrical Engineering, Hanbat National University, Republic of Korea.
  • Jonghyeon Noh
    School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. [email protected].
  • Jaeyu Lee
    Department of Chemical Engineering and Applied Chemistry College of Engineering, Chungnam National University, Daejeon, Republic of Korea. [email protected].
  • Safina Abdusamievna Saidova
    Department of Aerospace Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
  • Jaehun Lee
    Intelligence and Interaction Research Center, Korea Institute of Science and Technology, Seoul, Republic of Korea; Department of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, Republic of Korea. Electronic address: [email protected].
  • Seungseok Han
    School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. [email protected].
  • Seonju Jeong
    School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. [email protected].
  • Kee-Eung Kim
    School of Computing, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea.
  • Wu Bin Ying
    Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang, 315201, P. R. China.
  • Kyung Jin Lee
    Department of Chemical Engineering and Applied Chemistry, College of Engineering, Chungnam National University, Daejeon, 34134, Republic of Korea.
  • Jung-Yong Lee
    School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. [email protected].

Keywords

No keywords available for this article.