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

Hierarchy of Motor Control01:18

Hierarchy of Motor Control

4.1K
The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.
4.1K

You might also read

Related Articles

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

Sort by
Same author

Nanosecond-level time-domain coding metasurface for radar signal generation.

Nature communications·2025
Same author

Advancing sleep health equity through deep learning on large-scale nocturnal respiratory signals.

Nature communications·2025
Same author

Association of immune-inflammatory biomarkers during pregnancy and the postpartum period with postpartum depression symptoms: A cross-sectional and longitudinal retrospective analysis.

Brain, behavior, and immunity·2025
Same author

Multifunctional Mesoporous Titanium Dioxide Nanodrug for Corneal Haze Treatment and Its Mechanism.

Biomaterials research·2025
Same author

Construction of safer hirudin-derived peptides with enhanced anticoagulant properties based on C-terminal active residue adaptation and modification.

Journal of advanced research·2025
Same author

Trust, Anxious Attachment, and Conversational AI Adoption Intentions in Digital Counseling: A Preliminary Cross-Sectional Questionnaire Study.

JMIR AI·2025

Related Experiment Video

Updated: Oct 21, 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

854

A Hierarchical View Pooling Network for Multichannel Surface Electromyography-Based Gesture Recognition.

Wentao Wei1, Hong Hong2, Xiaoli Wu1

  • 1School of Design Arts and Media, Nanjing University of Science and Technology, Nanjing, Jiangsu, China.

Computational Intelligence and Neuroscience
|September 6, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel hierarchical view pooling network (HVPN) for improved surface electromyography (sEMG) based hand gesture recognition. The HVPN framework enhances accuracy in both intrasubject and intersubject recognition tasks.

More Related Videos

An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles
09:27

An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles

Published on: August 25, 2020

4.4K
Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

1.8K

Related Experiment Videos

Last Updated: Oct 21, 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

854
An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles
09:27

An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles

Published on: August 25, 2020

4.4K
Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

1.8K

Area of Science:

  • Biomedical Engineering
  • Rehabilitation Engineering
  • Machine Learning

Background:

  • Surface electromyography (sEMG) is crucial for hand gesture recognition in biomedical and rehabilitation engineering.
  • High-density sEMG (HD-sEMG) has advanced gesture recognition, but robust recognition with sparse multichannel sEMG remains challenging.

Purpose of the Study:

  • To present a hierarchical view pooling network (HVPN) framework for enhanced multichannel sEMG-based gesture recognition.
  • To improve gesture recognition by learning both view-specific and view-shared deep features from multiview feature spaces.

Main Methods:

  • Developed a hierarchical view pooling network (HVPN) within a multiview deep learning context.
  • Evaluated the HVPN framework on the NinaPro database using intrasubject and intersubject evaluations with 200 ms sliding windows.

Main Results:

  • The proposed HVPN framework achieved high intrasubject accuracies (up to 90.3%) and intersubject accuracies (up to 88.9%) across five NinaPro subdatabases.
  • HVPN significantly outperformed state-of-the-art methods in multichannel sEMG-based gesture recognition.

Conclusions:

  • The HVPN framework offers a robust and effective solution for multichannel sEMG-based hand gesture recognition.
  • This approach advances the capabilities of non-invasive prosthetics and human-computer interaction systems.