Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Resistance Welding Quality Through Artificial Intelligence Techniques.

Sensors (Basel, Switzerland)·2025
Same author

Pose Estimation of a Cobot Implemented on a Small AI-Powered Computing System and a Stereo Camera for Precision Evaluation.

Biomimetics (Basel, Switzerland)·2024
Same author

Non-Invasive Prediction of Choledocholithiasis Using 1D Convolutional Neural Networks and Clinical Data.

Diagnostics (Basel, Switzerland)·2024
Same author

Modeling of Particulate Pollutants Using a Memory-Based Recurrent Neural Network Implemented on an FPGA.

Micromachines·2023
Same author

Performance Evaluation of Different Object Detection Models for the Segmentation of Optical Cups and Discs.

Diagnostics (Basel, Switzerland)·2022
Same author

Performance evaluation of a recurrent deep neural network optimized by swarm intelligent techniques to model particulate matter.

Journal of the Air & Waste Management Association (1995)·2022

Related Experiment Video

Updated: Jan 1, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

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

Published on: March 28, 2025

1.1K

Methodology Proposal of EMG Hand Movement Classification Based on Cross Recurrence Plots.

M A Aceves-Fernandez1, J M Ramos-Arreguin1, E Gorrostieta-Hurtado1

  • 1Universidad Autónoma de Querétaro, Faculty of Engineering, Cerro de las Campanas S/N, Querétaro 76010, Mexico.

Computational and Mathematical Methods in Medicine
|December 31, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method using cross recurrence quantification analysis (CRQA) to accurately classify electromyography (EMG) signals from hand movements. The CRQA approach demonstrates superior performance over traditional machine learning techniques for EMG signal analysis.

More Related Videos

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

44.0K
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.5K

Related Experiment Videos

Last Updated: Jan 1, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

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

Published on: March 28, 2025

1.1K
Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

44.0K
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.5K

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Nonlinear Dynamics

Background:

  • Electromyography (EMG) signals present challenges due to their nonstationary, noisy, and high-dimensional nature, complicating accurate prediction and classification.
  • Cross recurrence plots (CRPs) are effective in identifying subtle patterns within noisy signal environments.
  • Traditional machine learning methods may struggle with the inherent complexities of EMG data.

Purpose of the Study:

  • To propose and validate a novel methodology for detecting and classifying surface EMG (sEMG) signals from hand movements.
  • To assess the accuracy, sensitivity, and specificity of the proposed method in avoiding false classifications.
  • To compare the performance of the novel method against traditional machine learning approaches.

Main Methods:

  • Fifty subjects performed ten distinct hand movements, with EMG data collected from electrodes placed on each arm.
  • Nonlinear features of the sEMG signals were analyzed using cross recurrence quantification analysis (CRQA).
  • A new methodology was developed, centering on CRQA for movement detection and classification, augmented with tools to ensure classification robustness.

Main Results:

  • The proposed CRQA-based methodology achieved good accuracy, sensitivity, and specificity in classifying sEMG signals.
  • The method effectively detected and classified ten different hand movements across fifty subjects.
  • CRQA demonstrated advantages over traditional machine learning methods in classifying complex EMG patterns.

Conclusions:

  • The developed CRQA methodology is a feasible and effective technique for classifying sEMG signals.
  • This approach offers improved performance and robustness compared to conventional machine learning methods for EMG analysis.
  • The findings highlight the potential of nonlinear dynamics, specifically CRQA, in advancing EMG-based human-computer interfaces and movement analysis.