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

Motor Unit Stimulation01:20

Motor Unit Stimulation

2.5K
When the neuron of a motor unit fires an action potential, it triggers a series of events, leading to a twitch contraction in the muscle fibers. The process of excitation-contraction coupling is crucial in relaying the action potential to the muscle fibers.
The latent period of contraction marks the onset of excitation-contraction coupling, when the action potential propagates across the sarcolemma, preparing the muscle fibers for contraction. As the fibers enter the contraction phase, the...
2.5K
Muscle Stimulation Frequency01:22

Muscle Stimulation Frequency

3.3K
The contraction strength of muscles is regulated by motor neurons, which modulate the frequency of action potentials dispatched to the motor units based on the body's requirements. This process of varying the muscle stimulation frequency allows muscles to contract with a force that is precisely tailored to the needs of the moment, whether lifting a feather or a heavy box.
Wave summation
At low firing rates, motor neurons induce individual twitch contractions in muscle fibers. These twitches...
3.3K

You might also read

Related Articles

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

Sort by
Same author

Direct photodegradation of aromatic carbamate pesticides: Kinetics and mechanisms in aqueous vs. non-aqueous media.

Journal of hazardous materials·2025
Same author

Alterations of Motor Unit Characteristics Associated With Muscle Fatigue.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2023
Same author

An integrated approach for quantifying trace metal sources in surface soils of a typical farmland in the three rivers plain, China.

Environmental pollution (Barking, Essex : 1987)·2023
Same author

Enhanced Dynamic Surface EMG Decomposition Using the Non-Negative Matrix Factorization and Three-Dimensional Motor Unit Localization.

IEEE transactions on bio-medical engineering·2023
Same author

Organic ligands activate the dark formation of hydroxyl radicals (HO<sup>•</sup>) in surface soil/sediment: Yields, mechanisms, and applications.

Journal of hazardous materials·2023
Same author

Actional Mechanisms of Active Ingredients in Functional Food Adlay for Human Health.

Molecules (Basel, Switzerland)·2022

Related Experiment Video

Updated: Oct 8, 2025

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
09:42

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Published on: January 24, 2025

757

[A new method for high-density surface electromyography decomposition in dynamic muscle contraction].

Jinbao He1, Binglei Guan1, Kai Huang2

  • 1The School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo, Zhejiang 315211, P.R.China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|December 31, 2021
PubMed
Summary

This study introduces a novel method for decomposing surface electromyography (sEMG) signals using spatial location, improving the accuracy of motor unit (MU) firing train identification during dynamic muscle contractions.

Keywords:
dynamic signal decompositionmotor unitspatial locationsurface electromyography

More Related Videos

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

821
Muscle Function Obtained with Motion Mode Ultrasound and Surface Electromyography during Core Endurance Exercise
09:21

Muscle Function Obtained with Motion Mode Ultrasound and Surface Electromyography during Core Endurance Exercise

Published on: August 25, 2022

3.4K

Related Experiment Videos

Last Updated: Oct 8, 2025

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
09:42

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Published on: January 24, 2025

757
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

821
Muscle Function Obtained with Motion Mode Ultrasound and Surface Electromyography during Core Endurance Exercise
09:21

Muscle Function Obtained with Motion Mode Ultrasound and Surface Electromyography during Core Endurance Exercise

Published on: August 25, 2022

3.4K

Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Signal Processing

Background:

  • Surface electromyography (sEMG) is crucial for understanding muscle activity.
  • Decomposing sEMG signals into individual motor unit (MU) action potentials is challenging, especially during dynamic contractions.
  • Accurate MU decomposition is vital for clinical diagnostics and biomechanical analysis.

Purpose of the Study:

  • To propose a novel sEMG signal decomposition method utilizing spatial location information.
  • To enhance the accuracy of identifying MU firing times and trains in high-density sEMG data.
  • To provide a new approach for analyzing dynamic muscle contractions.

Main Methods:

  • Extraction of MU firing times based on waveform correlation across channels.
  • Classification of extracted firing times using the spatial location of MUs.
  • Generation of MU firing trains through spatial classification.

Main Results:

  • Achieved over 91.67% accuracy for single MU firing train classification in simulations.
  • Demonstrated over (88.3 ± 2.1)% accuracy in identifying the same MU using the "two source" method with real sEMG signals.
  • Validated the effectiveness of the spatial location-based decomposition method.

Conclusions:

  • The proposed spatial location-based method offers a significant advancement in sEMG signal decomposition.
  • This technique improves the precision of MU activity analysis during dynamic muscle contractions.
  • The findings provide a valuable new tool for researchers and clinicians in neuromuscular studies.