Jove
Visualize
Contact Us

Related Concept Videos

Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

56.8K
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...
56.8K

You might also read

Related Articles

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

Sort by
Same author

Sensing Muscle Deformation for Upper-Limb Prosthetic Control: a Narrative Review.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

Coordinated hand movement sensation revealed through an implanted magnetic prosthetic kinesthetic interface.

Science advances·2026
Same author

Recent Advances in Supplementary Haptic Feedback for Human-Machine Interfaces in Upper Limb Assistance and Rehabilitation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

Toward Simultaneous Neurostimulation and Prosthetic Control: Real-Time Filtering of Amplitude-Modulated Stimulation Artifacts From Implanted Electrode Signals.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

Myoelectric and Progressive Motor Training for Phantom Limb Pain in People with Amputations During War: A Feasibility Report.

Journal of pain research·2026
Same author

Patient's healthcare needs in the traditional and technological neuro-rehabilitation field: a survey methodological approach.

Frontiers in digital health·2026
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 Experiment Video

Updated: Aug 23, 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

705

Decoding of Multiple Wrist and Hand Movements Using a Transient EMG Classifier.

Daniele D'Accolti, Katarina Dejanovic, Leonardo Cappello

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |November 3, 2022
    PubMed
    Summary

    This study introduces a novel algorithm decoding transient electromyographic (EMG) signals for prosthetic control, achieving high accuracy in non-amputees and promising results in amputees. This approach offers a viable alternative to traditional steady-state EMG pattern recognition strategies.

    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

    43.5K
    Assessment and Communication for People with Disorders of Consciousness
    07:37

    Assessment and Communication for People with Disorders of Consciousness

    Published on: August 1, 2017

    9.2K

    Related Experiment Videos

    Last Updated: Aug 23, 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

    705
    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

    43.5K
    Assessment and Communication for People with Disorders of Consciousness
    07:37

    Assessment and Communication for People with Disorders of Consciousness

    Published on: August 1, 2017

    9.2K

    Area of Science:

    • Biomedical Engineering
    • Neuroprosthetics
    • Rehabilitation Technology

    Background:

    • Designing effective prosthetic controllers using neurophysiological signals remains a significant bioengineering challenge.
    • Current electromyographic (EMG) controllers often assume repeatable muscle contraction patterns, which may not always hold true.
    • Processing transient EMG signals, occurring immediately after muscle contraction onset, presents a novel approach to prosthetic control.

    Purpose of the Study:

    • To develop and evaluate an algorithm for decoding wrist and hand movements using transient EMG signals.
    • To assess the intra-subject and cross-subject classification performance of the proposed algorithm.
    • To explore the potential of transient EMG decoding as a pattern recognition strategy for prosthetic control.

    Main Methods:

    • Collected EMG data from non-amputee and transradial amputee participants performing various wrist and hand movements.
    • Developed a classification algorithm processing transient EMG signals following muscle contraction onset.
    • Evaluated controller performance through intra-subject (within-participant) and cross-subject (between-participant) classification analyses.

    Main Results:

    • The controller achieved a median intra-subject accuracy of approximately 96% for non-amputees and 89% for amputees.
    • For amputees, at least one movement combination consistently exceeded 85% accuracy.
    • Cross-subject classification showed promising results in non-amputees (up to 80%) but lower accuracy in amputees (up to 35%).

    Conclusions:

    • Transient EMG decoding is a viable pattern recognition strategy for prosthetic control, offering high accuracy in non-amputees and acceptable performance in amputees.
    • Further research, potentially incorporating domain-adaptation strategies, is needed to improve cross-subject classification for amputee populations.
    • Preliminary online assessments support the potential of this approach for real-world prosthetic applications.