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

1.3K
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...
1.3K
Muscle Contraction01:10

Muscle Contraction

6.1K
In skeletal muscles, acetylcholine is released by nerve terminals at the motor endplate—the point of synaptic communication between motor neurons and muscle fibers. The binding of acetylcholine to its receptors on the sarcolemma allows entry of sodium ions into the cell and triggers an action potential in the muscle cell. Thus, electrical signals from the brain are transmitted to the muscle. Subsequently, the enzyme acetylcholinesterase breaks down acetylcholine to prevent excessive...
6.1K
Muscle Stimulation Frequency01:22

Muscle Stimulation Frequency

1.9K
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...
1.9K
Excitation-Contraction Coupling in Skeletal Muscles01:20

Excitation-Contraction Coupling in Skeletal Muscles

7.4K
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...
7.4K
Actin and Myosin in Muscle Contraction01:16

Actin and Myosin in Muscle Contraction

7.9K
Actin and myosin are contractile proteins that form the sarcomere found in skeletal muscle tissues for regulating muscle contraction. Actin, a globular contractile protein, interacts with myosin for muscle contraction. The skeletal tissue appears striped or striated under a microscope due to the repeated arrangement of contractile proteins actin and myosin along the length of myofibrils. Dark A bands and light I bands repeat along myofibrils, and the alignment of myofibrils in the cell causes...
7.9K
Smooth Muscle Contraction01:25

Smooth Muscle Contraction

2.1K
Smooth muscle contraction is a complex process vital for various bodily functions, from maintaining blood vessel tension to facilitating the movement of food through the digestive tract. Unlike striated muscles, smooth muscle contraction begins more slowly and lasts longer.
The onset of contraction is triggered by an increase in calcium ions within the sarcoplasm, similar to the process in striated muscle. However, smooth muscles have a relatively smaller reservoir of the sarcoplasmic...
2.1K

You might also read

Related Articles

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

Sort by
Same author

Self-derived Motion Features from sEMG for Inferring 3D Forearm Trajectories.

IEEE transactions on bio-medical engineering·2026
Same author

Geometric Neural Ordinary Differential Equations: From Manifolds to Lie Groups.

Entropy (Basel, Switzerland)·2025
Same author

Endoscopic sleeve gastroplasty video assessment: do technical features influence ESG integrity and weight loss at 6 and 12 months follow-up?

Surgical endoscopy·2025
Same author

Hierarchical Transformer Fusion of Gaze Attention and Muscle Activity for Forearm Movement Estimation.

IEEE transactions on bio-medical engineering·2025
Same author

Sex-based differences in musculoskeletal pain among surgeons: an international survey.

Surgical endoscopy·2025
Same author

The Influence of Mass and Friction in Teleoperated Tasks.

IEEE transactions on haptics·2025
Same journal

An EEG-Based Framework for Sleep Quality Assessment and Modulation with Conditional Convolutional Diffusion Modeling.

IEEE journal of biomedical and health informatics·2026
Same journal

Substantia Nigra Imaging Biomarker Segmentation for Parkinson's Disease Diagnosis via Transformer-Enhanced U-Net Architecture.

IEEE journal of biomedical and health informatics·2026
Same journal

E-TIME: Emotion Trend Inspired Multi-task Sparse Mask Neural Network for Multimodal Emotion Recognition.

IEEE journal of biomedical and health informatics·2026
Same journal

Cross-Modal Feature Adapter for Few-Shot Human Activity Recognition.

IEEE journal of biomedical and health informatics·2026
Same journal

Cross Domain Self-Prompting SAM2 for Intraoperative OCT Video Segmentation.

IEEE journal of biomedical and health informatics·2026
Same journal

Multi-Property Optimization of Antimicrobial Peptides Using Reinforcement Learning and Conditional Independence Regularization.

IEEE journal of biomedical and health informatics·2026
See all related articles

Related Experiment Video

Updated: May 11, 2025

Corticospinal Excitability Modulation During Action Observation
12:33

Corticospinal Excitability Modulation During Action Observation

Published on: December 31, 2013

8.8K

Deciphering Muscular Dynamics: A Dual-Attention Framework for Predicting Muscle Contraction From Activation Patterns.

Bangyu Lan, Gijs Krijnen, Stefano Stramigioli

    IEEE Journal of Biomedical and Health Informatics
    |April 17, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study uses deep learning on surface electromyography (sEMG) signals to predict muscle deformation, bypassing ultrasound. This enables portable, real-time muscle health monitoring using only sEMG data.

    More Related Videos

    Combining Multiple Data Acquisition Systems to Study Corticospinal Output and Multi-segment Biomechanics
    08:48

    Combining Multiple Data Acquisition Systems to Study Corticospinal Output and Multi-segment Biomechanics

    Published on: January 9, 2016

    6.8K
    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
    06:37

    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

    Published on: December 15, 2023

    2.5K

    Related Experiment Videos

    Last Updated: May 11, 2025

    Corticospinal Excitability Modulation During Action Observation
    12:33

    Corticospinal Excitability Modulation During Action Observation

    Published on: December 31, 2013

    8.8K
    Combining Multiple Data Acquisition Systems to Study Corticospinal Output and Multi-segment Biomechanics
    08:48

    Combining Multiple Data Acquisition Systems to Study Corticospinal Output and Multi-segment Biomechanics

    Published on: January 9, 2016

    6.8K
    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
    06:37

    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

    Published on: December 15, 2023

    2.5K

    Area of Science:

    • Biomedical Engineering
    • Neuroscience
    • Rehabilitation Science

    Background:

    • Accurate muscle deformation assessment is vital for diagnosing muscle diseases like Facioscapulohumeral Dystrophy.
    • Ultrasound imaging is effective but limited by device portability and wiring.
    • Surface electromyography (sEMG) measures muscle activation, correlating with thickness changes.

    Purpose of the Study:

    • To develop a deep-learning method for inferring muscle deformation directly from sEMG signals.
    • To eliminate the need for ultrasound imaging in muscle deformation monitoring.
    • To enable portable, real-time muscle health assessment.

    Main Methods:

    • A deep learning model utilizing hierarchical self-attention and cross-attention mechanisms was employed.
    • The model processed sEMG data to predict muscle deformation.
    • Experiments were conducted on six healthy subjects.

    Main Results:

    • The developed approach accurately predicted muscle excursion from sEMG signals.
    • An average precision of 0.923 ± 0.900 mm was achieved.
    • The method demonstrated the feasibility of muscle deformation measurement using only sEMG.

    Conclusions:

    • This deep learning technique offers a portable and non-invasive method for muscle deformation monitoring.
    • It integrates bioelectrical and biomechanical information for comprehensive muscle health insights.
    • Potential applications include clinical diagnostics, sports science, and rehabilitation.