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Muscles of the Forearm that Move the Hand and Fingers01:16

Muscles of the Forearm that Move the Hand and Fingers

The muscles of the forearm that move the wrist, hand, and digits are numerous and diverse. They can be classified into two groups based on their location and function — the anterior and posterior compartment muscles.
Anterior Compartment
The anterior compartment muscles originate from the humerus. They primarily function as flexors and are also known as flexor muscles. They typically insert on the carpals, metacarpals, and phalanges. The superficial layer includes the flexor carpi radialis,...

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Related Experiment Video

Updated: May 13, 2026

Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment
05:58

Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment

Published on: March 11, 2021

4.5K

Intelligent Human-Computer Interaction: Combined Wrist and Forearm Myoelectric Signals for Handwriting Recognition.

Andrea Tigrini1, Simone Ranaldi2, Federica Verdini1

  • 1Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy.

Bioengineering (Basel, Switzerland)
|May 25, 2024
PubMed
Summary
This summary is machine-generated.

This study demonstrates that surface electromyographic (EMG) signals can recognize handwritten words. Combining forearm and wrist EMG data with machine learning achieved high accuracy in classifying 30 words.

Keywords:
EMGfeature extractionhandwritinghuman–machine interfacepattern recognitionsignal processing

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Machine Learning

Background:

  • Surface electromyographic (EMG) signals show potential for human-computer interfaces recognizing complex motor tasks like handwriting.
  • Automatic word recognition directly from EMG signals remains an unexplored area.

Purpose of the Study:

  • To investigate the feasibility of using combined forearm and wrist EMG signals for recognizing 30 handwritten words.
  • To evaluate machine learning techniques and feature extraction methods for EMG-based handwriting recognition.

Main Methods:

  • Utilized surface EMG probes on the forearm and wrist of six healthy subjects.
  • Employed pattern recognition tests with machine learning algorithms (SVM, LDA, KNN) and time/frequency domain features.
  • Assessed classification performance using consolidated myoelectric control and specific feature sets like TDAR.

Main Results:

  • K-nearest neighbours (KNN) demonstrated the best performance in classifying 30 words.
  • Achieved a mean accuracy of 95% with all features and 85% with the TDAR feature set.
  • Validated the effectiveness of combined EMG data for intelligent handwriting recognition.

Conclusions:

  • Combined forearm and wrist EMG data are effective for intelligent handwriting recognition using pattern recognition.
  • Machine learning approaches, particularly KNN, show high potential for real-world EMG-based handwriting detection.
  • Feature extraction plays a crucial role in achieving high performance with simplified processing pipelines.