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Updated: Jun 6, 2026

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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An EMG-based handwriting recognition through dynamic time warping.

Gan Huang1, Dingguo Zhang, Xidian Zheng

  • 1State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, China, 200240. huanggan1982@gmail.com

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 25, 2010
PubMed
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This study introduces electromyography (EMG) for handwriting recognition, achieving over 90% accuracy with few training trials. This natural user interface method shows promise for diverse character sets.

Area of Science:

  • Biomedical Engineering
  • Human-Computer Interaction
  • Signal Processing

Background:

  • Natural user interfaces are increasingly important.
  • Electromyography (EMG) signals offer a potential input method.
  • Handwriting recognition using biological signals is an emerging field.

Purpose of the Study:

  • To propose and evaluate an EMG-based handwriting recognition system.
  • To assess the system's accuracy and efficiency for natural user interfaces.
  • To compare performance across different character sets.

Main Methods:

  • Recorded six-channel EMG signals from forearm muscles during handwriting.
  • Utilized Dynamic Time Warping (DTW) to normalize temporal variations.
  • Developed template making and matching processes for character recognition.

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

Published on: March 28, 2025

Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment
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Published on: March 11, 2021

Main Results:

  • Achieved over 90% accuracy with fewer than ten training trials per character.
  • Demonstrated effective recognition for digits, Chinese characters, and capital letters.
  • DTW algorithm successfully mitigated time-axis variance in handwriting signals.

Conclusions:

  • EMG-based handwriting recognition is a viable and accurate method.
  • The proposed system offers a natural and efficient user interface.
  • The approach shows potential for real-world applications in human-computer interaction.