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

5.2K
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...
5.2K
Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

60.4K
Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...
60.4K
Generation of Action Potential in Skeletal Muscles01:24

Generation of Action Potential in Skeletal Muscles

11.0K
Every cell in the body maintains a membrane potential due to an uneven distribution of positive and negative charges across its plasma membrane. The membrane potential is measured in millivolts and quantifies the difference in charge across the membrane.
Like neurons, muscle cells are also regarded as excitable due to their capacity to change in response to stimuli, primarily due to voltage-gated ion channels embedded in their plasma membranes, which get activated by alterations in the...
11.0K
Excitation-Contraction Coupling in Skeletal Muscles01:20

Excitation-Contraction Coupling in Skeletal Muscles

22.2K
Excitation-contraction coupling is a series of events that occur between generating an action potential and initiating a muscle contraction. It occurs at the triad, a structure found in skeletal muscle fibers that comprise a T-tubule and terminal cisternae of the sarcoplasmic reticulum on each side. These triads are visible in longitudinally sectioned muscle fibers. They are typically located at the A-I junction — the junction between the A and I bands of the sarcomere.
When an action...
22.2K
Muscle Stimulation Frequency01:22

Muscle Stimulation Frequency

5.2K
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...
5.2K

You might also read

Related Articles

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

Sort by
Same author

[Heart sound denoising by dynamic noise estimation].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi·2020
Same author

EMG burst presence probability: a joint time-frequency representation of muscle activity and its application to onset detection.

Journal of biomechanics·2015
Same author

Investigation of objective measures for intelligibility prediction of noise-reduced speech for Chinese, Japanese, and English.

The Journal of the Acoustical Society of America·2014
Same author

Wiener filtering of surface EMG with a priori SNR estimation toward myoelectric control for neurological injury patients.

Medical engineering & physics·2014
Same author

Subspace based adaptive denoising of surface EMG from neurological injury patients.

Journal of neural engineering·2014
Same author

In vitro and in vivo investigations on the antiviral activity of a series of mixed-valence rare earth borotungstate heteropoly blues.

European journal of medicinal chemistry·2008
Same journal

Modeling and analysis of forward and inverse kinematics for a flexible Stewart platform.

PloS one·2026
Same journal

Barriers and facilitators to healthcare utilization amongst people living with sickle cell disease in the United States: A scoping review.

PloS one·2026
Same journal

Enhancing data completeness in time series: Imputation strategies for missing data using significant periodically correlated components.

PloS one·2026
Same journal

Key targets and mechanisms by which gut microbiota-derived metabolites regulate Alzheimer's disease through the immune - inflammatory pathway: Based on network pharmacology and molecular docking.

PloS one·2026
Same journal

Grid-tied Transformer-less Boost Switched Capacitor Topology (TLBSCT) for PV applications.

PloS one·2026
Same journal

The load-velocity profiles and exercise-specific velocity zones for seven commonly used weightlifting exercises.

PloS one·2026
See all related articles

Related Experiment Video

Updated: Apr 11, 2026

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

1.5K

Robust muscle activity onset detection using an unsupervised electromyogram learning framework.

Jie Liu1, Dongwen Ying2, William Z Rymer3

  • 1Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, United States of America.

Plos One
|June 4, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces an unsupervised learning framework using a sequential Gaussian mixture model (GMM) for detecting muscle activity onsets in surface electromyography (EMG) signals. The method robustly identifies muscle activation, even with low or changing signal-to-noise ratios, facilitating practical applications.

More Related Videos

The Muscle Cuff Regenerative Peripheral Nerve Interface for the Amplification of Intact Peripheral Nerve Signals
07:30

The Muscle Cuff Regenerative Peripheral Nerve Interface for the Amplification of Intact Peripheral Nerve Signals

Published on: January 13, 2022

2.6K
Repeated Measurement of Respiratory Muscle Activity and Ventilation in Mouse Models of Neuromuscular Disease
09:24

Repeated Measurement of Respiratory Muscle Activity and Ventilation in Mouse Models of Neuromuscular Disease

Published on: April 17, 2017

14.0K

Related Experiment Videos

Last Updated: Apr 11, 2026

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

1.5K
The Muscle Cuff Regenerative Peripheral Nerve Interface for the Amplification of Intact Peripheral Nerve Signals
07:30

The Muscle Cuff Regenerative Peripheral Nerve Interface for the Amplification of Intact Peripheral Nerve Signals

Published on: January 13, 2022

2.6K
Repeated Measurement of Respiratory Muscle Activity and Ventilation in Mouse Models of Neuromuscular Disease
09:24

Repeated Measurement of Respiratory Muscle Activity and Ventilation in Mouse Models of Neuromuscular Disease

Published on: April 17, 2017

14.0K

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Accurate detection of muscle activity onset is crucial for surface electromyogram (EMG) applications.
  • Existing methods often struggle with dynamic environments and varying signal quality.

Purpose of the Study:

  • To develop an unsupervised learning framework for robust muscle activity onset detection in EMG signals.
  • To improve the reliability of EMG analysis in challenging signal conditions.

Main Methods:

  • Utilized a sequential Gaussian mixture model (GMM) to characterize EMG signal power distributions.
  • Estimated GMM parameters using maximum likelihood with constraints for sequential analysis.
  • Implemented a voting procedure across frequency bands for final onset detection.

Main Results:

  • The GMM framework accurately detected muscle activity onsets in simulated and experimental EMG data.
  • Demonstrated robust performance across varying and low signal-to-noise ratios.
  • The approach does not require assumptions about non-EMG burst periods initially.

Conclusions:

  • The proposed unsupervised GMM framework offers a reliable method for surface EMG muscle activity onset detection.
  • Its real-time applicability and robustness make it suitable for diverse practical applications.
  • This method advances EMG signal analysis, particularly in dynamic and noisy conditions.