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

Multi-channel auto-encoders for learning domain invariant representations enabling superior classification of histopathology images.

Medical image analysis·2022
Same author

HistoClean: Open-source software for histological image pre-processing and augmentation to improve development of robust convolutional neural networks.

Computational and structural biotechnology journal·2021
Same author

Probabilistic, Recurrent, Fuzzy Neural Network for Processing Noisy Time-Series Data.

IEEE transactions on neural networks and learning systems·2021
Same author

Socioeconomic and psychosocial factors are associated with poor treatment outcomes in Australian adults living with HIV: a case-control study.

Sexual health·2019
Same author

A Computational Model of Thalamocortical Dysrhythmia in People With Tinnitus.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2018
Same author

Socioeconomic factors explain suboptimal adherence to antiretroviral therapy among HIV-infected Australian adults with viral suppression.

PloS one·2017
Same journal

Analysis of End-Tidal CO2 Variability During Plateau Waves Episodes: An Information Theoretic Approach<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

AI and Tomosynthesis for Breast Cancer Molecular Subtyping: A step toward precision medicine<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Towards Sustainable Protein Recovery from Biological Waste: Assessing Polyethersulfone-based Microfiltration.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Analysis of the cardiovascular response to standardized polymicrobial peritonitis experimental model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Automated Wrist Ultrasound Image Bone Enhancement and Segmentation Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
See all related articles

Related Experiment Video

Updated: Aug 29, 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

714

Optimised EMG pipeline for gesture classification.

Jarlath Warner, Richard Gault, John McAllister

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |September 10, 2022
    PubMed
    Summary
    This summary is machine-generated.

    Researchers optimized a pipeline for decoding surface electromyography (sEMG) signals to control robotic prosthetics. This efficient system accurately predicts hand gestures in real-time using an optimized sliding window approach.

    More Related Videos

    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

    12.6K
    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

    Related Experiment Videos

    Last Updated: Aug 29, 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

    714
    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

    12.6K
    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

    Area of Science:

    • Robotics and Biomedical Engineering
    • Neuroscience and Signal Processing

    Background:

    • Surface electromyography (sEMG) signals are crucial for controlling robotic prosthetics.
    • Accurate and real-time decoding of sEMG signals is essential for seamless prosthetic control.
    • Existing pipelines require optimization for efficiency and reliability.

    Purpose of the Study:

    • To propose an optimized pipeline for acquiring, processing, and classifying sEMG signals.
    • To enhance the real-time control of robotic prosthetics through improved hand gesture recognition.
    • To analyze the impact of sliding window parameters on sEMG signal processing.

    Main Methods:

    • Implementation of a sliding window approach for sEMG signal processing.
    • Evaluation of various parameters and modeling techniques within the pipeline.
    • Focus on optimizing the sliding window for efficient and accurate classification.

    Main Results:

    • Development of a robust and optimized sEMG processing and classification pipeline.
    • Extensive analysis of sliding window parameter selection for improved performance.
    • Demonstration of an efficient pipeline with minimal overheads.

    Conclusions:

    • The optimized pipeline enables accurate prediction of hand gestures for robotic prosthetics.
    • The proposed system offers a reliable and efficient solution for real-time sEMG control.
    • This work contributes a practical and optimized approach for prosthetic control applications.