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Related Experiment Video

Updated: Aug 3, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

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High-Fidelity sEMG Signals Recorded by an on-Skin Electrode Based on AgNWs for Hand Gesture Classification Using

Xiaoyang Zou1, Jiaqi Xue1, Xiaoting Li1

  • 1Department of Biomedical Engineering, Centre for Robotics and Automation, City University of Hong Kong, Hong Kong 999077, China.

ACS Applied Materials & Interfaces
|April 10, 2023
PubMed
Summary
This summary is machine-generated.

Researchers developed a new stretchable silver nanowire (AgNW) on-skin electrode for high-fidelity surface electromyography (sEMG) signal recording. This advancement is crucial for improving human-machine interface (HMI) applications like gesture recognition.

Keywords:
gesture classificationon-skin electrodesEMG

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Area of Science:

  • Biomedical Engineering
  • Materials Science
  • Neuroscience

Background:

  • Forearm muscles are crucial for hand motion, requiring high-fidelity surface electromyography (sEMG) signals for human-machine interface (HMI) applications.
  • Existing commercial electrodes are rigid, lack stretchability, and can cause skin irritation due to hydrogel shrinkage, hindering long-term HMI use.
  • Development of elastic, biocompatible electrodes is essential for reliable sEMG recording and advanced HMI systems.

Purpose of the Study:

  • To fabricate a novel nanocomposite hybrid on-skin electrode for improved sEMG signal acquisition.
  • To develop a stretchable and biocompatible electrode suitable for dynamic skin contact and long-term wear.
  • To evaluate the electrode's performance in recording sEMG signals and recognizing hand gestures for HMI applications.

Main Methods:

  • Fabrication of a nanocomposite electrode by infiltrating silver nanowires (AgNWs) into polydimethylsiloxane (PDMS).
  • Characterization of electrode properties including thickness, sheet resistance, and mechanical strain tolerance (up to 54%).
  • Testing of electrode performance in recording sEMG signals from forearm muscles and recognizing hand gestures using machine learning.

Main Results:

  • The AgNW-PDMS electrode demonstrated low sheet resistance (0.481 ± 0.014 Ω/sq) and maintained performance after 1000 strain cycles.
  • High signal-to-noise ratio (SNR) sEMG signals were recorded, reflecting varying muscle force levels.
  • Four hand gestures were recognized with 92.3% accuracy using multichannel electrodes and machine learning.

Conclusions:

  • The developed AgNW on-skin electrode offers excellent stretchability, conductivity, and biocompatibility for sEMG recording.
  • This electrode technology shows significant potential for enhancing the performance and reliability of sEMG-based HMI systems.
  • The findings pave the way for more sophisticated and comfortable wearable interfaces for gesture control and other applications.