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

A flexible analytic wavelet transform based approach for motor-imagery tasks classification in BCI applications.

Computer methods and programs in biomedicine·2020
Same author

Emotion recognition from single-channel EEG signals using a two-stage correlation and instantaneous frequency-based filtering method.

Computer methods and programs in biomedicine·2019
Same author

Emotion classification using flexible analytic wavelet transform for electroencephalogram signals.

Health information science and systems·2018
Same author

Features based on variational mode decomposition for identification of neuromuscular disorder using EMG signals.

Health information science and systems·2018
Same author

An optimum allocation sampling based feature extraction scheme for distinguishing seizure and seizure-free EEG signals.

Health information science and systems·2017
Same journal

AutoBiGluNet: transformer-based time series modeling for blood glucose prediction in Type 1 diabetes patients.

Health information science and systems·2026
Same journal

Multi-dimensional alignment framework with geometric intraoral constraints for precise occlusal registration.

Health information science and systems·2026
Same journal

SPSGL: uncovering psychiatric network mechanisms via structural-prior guided synaptic graph learning.

Health information science and systems·2026
Same journal

A noval 4D graph temporal brain network model for EEG-based depression detection.

Health information science and systems·2026
Same journal

PLETHSOMNet: automated identification of insomnia using deep neural network technique with photoplethysmography (PPG) signals.

Health information science and systems·2026
Same journal

Self-supervised fusion of clinical expertise and interpersonal skills for enhanced physician recommendation.

Health information science and systems·2026
See all related articles

Related Experiment Video

Updated: Dec 31, 2025

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

An efficient approach for physical actions classification using surface EMG signals.

Sravani Chada1, Sachin Taran1, Varun Bajaj1

  • 1Discipline of Electronics and Communication Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, 452005 India.

Health Information Science and Systems
|January 10, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new method using tunable-Q factor wavelet transform (TQWT) to classify physical actions from surface electromyography (sEMG) signals. The TQWT algorithm achieved a high accuracy of 97.74% for classifying various human movements.

Keywords:
MC-LSSVMPhysical actionsSurface electromyography (sEMG)Tunable-Q factor wavelet transform (TQWT)

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
Extraction of the EPP Component from the Surface EMG
07:16

Extraction of the EPP Component from the Surface EMG

Published on: December 16, 2009

12.9K

Related Experiment Videos

Last Updated: Dec 31, 2025

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
Extraction of the EPP Component from the Surface EMG
07:16

Extraction of the EPP Component from the Surface EMG

Published on: December 16, 2009

12.9K

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Surface electromyography (sEMG) signals are crucial for applications like advanced prosthetics and robotic control.
  • Accurate classification of physical actions from sEMG data is essential for seamless human-machine interaction.
  • Existing methods may face challenges in effectively capturing the complex dynamics of sEMG signals during various movements.

Purpose of the Study:

  • To propose and evaluate a novel algorithm for classifying physical actions using tunable-Q factor wavelet transform (TQWT).
  • To assess the efficacy of TQWT in decomposing sEMG signals and extracting relevant features for action recognition.
  • To compare the performance of a least squares support vector machine (LS-SVM) classifier with different kernel functions for sEMG-based action classification.

Main Methods:

  • The tunable-Q factor wavelet transform (TQWT) was employed to decompose sEMG signals into multiple sub-bands.
  • Statistical features were extracted from each decomposed sub-band.
  • A multi-class least squares support vector machine (LS-SVM) classifier, utilizing Morlet wavelet and radial basis function (RBF) kernels, was used for classification.

Main Results:

  • The proposed TQWT-based method demonstrated high classification accuracy for various physical actions including clapping, hugging, bowing, and walking.
  • Optimal performance was achieved with sub-band eight, yielding a classification accuracy of 97.74% when using the Morlet kernel function.
  • The feature extraction and classification approach proved effective in distinguishing between different physical actions based on sEMG patterns.

Conclusions:

  • The tunable-Q factor wavelet transform (TQWT) is a promising technique for enhancing the accuracy of physical action classification from sEMG signals.
  • The combination of TQWT feature extraction and LS-SVM with a Morlet kernel offers a robust solution for applications requiring precise sEMG-based movement recognition.
  • This approach shows significant potential for improving the functionality of intelligent systems such as prosthetics and human-robot interfaces.