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

Dual-Modulus Microcone Array for Graded Tactile Sensing and Intelligent Slip Detection.

ACS applied materials & interfaces·2026
Same author

AdaWGAN: Data Augmentation for Few-Shot HD-sEMG Gesture Recognition Using Single-Trial Data.

IEEE journal of biomedical and health informatics·2026
Same author

Advances in printable flexible and stretchable thin-film electrodes: materials, interfaces, technologies and bioelectronic applications.

Nanoscale·2026
Same author

Nondestructive determination of ash content in wheat flour via terahertz time-domain spectroscopy.

Frontiers in plant science·2026
Same author

Resting-state brain network alterations in adolescent idiopathic scoliosis using functional near-infrared spectroscopy.

Biomedical engineering online·2026
Same author

Editorial: Biomechanical and cognitive pattern assessment in human-machine collaborative tasks for industrial robotics.

Frontiers in neurorobotics·2026

Related Experiment Video

Updated: Jan 9, 2026

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

1.1K

Temporal Context Informed Myoelectric Feature Extraction Uncovers Frequency Invariance in EMG-based Gesture

Rami N Khushaba, Rami Mobarak, Oluwarotimi W Samuel

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Context-aware Electromyographic (EMG) feature extraction improves gesture recognition by considering temporal trends. This novel approach achieves high accuracy, demonstrating sampling frequency invariance for human-machine interfaces.

    More Related Videos

    Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality
    08:09

    Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality

    Published on: September 3, 2015

    11.4K
    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

    44.0K

    Related Experiment Videos

    Last Updated: Jan 9, 2026

    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

    1.1K
    Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality
    08:09

    Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality

    Published on: September 3, 2015

    11.4K
    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

    44.0K

    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Human-Computer Interaction

    Background:

    • Electromyographic (EMG) armbands are key for gesture recognition.
    • Current methods often ignore temporal activity trends, limiting context capture and leading to errors.
    • Existing spatial feature extraction methods can be enhanced by incorporating temporal dynamics.

    Purpose of the Study:

    • To develop a context-aware Electromyographic (EMG) feature extraction framework.
    • To improve gesture recognition accuracy by integrating short-term and long-term temporal activity trends.
    • To evaluate the proposed method's performance across different sampling frequencies and in clinical populations.

    Main Methods:

    • Developed a temporal context framework encapsulating Phasor-based Multi-signal Waveform Length (MSWL) features.
    • Concatenated short-term memory (partial correlation) and long-term memory (trend) information streams.
    • Evaluated the method on EMG datasets from healthy subjects with varying sampling frequencies and transradial amputees (NinaPro protocol).

    Main Results:

    • The context-aware EMG feature extraction demonstrated sampling frequency invariance in gesture recognition.
    • Achieved similar average accuracy (91%) across high- and low-frequency armbands, outperforming 58 existing methods.
    • Showcased efficacy in context-sensitive EMG pattern recognition using amputee data.

    Conclusions:

    • Context-aware EMG feature extraction enhances gesture recognition accuracy and robustness.
    • The proposed method challenges the conventional preference for higher sampling frequency devices in EMG applications.
    • This approach holds significant clinical relevance for advanced human-machine interfaces.