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

All-two-dimensional, ion-gating synaptic transistors for high-temperature and ultralow-energy-consumption neuromorphic applications.

Science advances·2026
Same author

A SAUR gene enhances maize drought resilience by promoting silk elongation.

Nature·2026
Same author

All-Solid-State, Ferroelectric-Graded-Doping Reconfigurable Molybdenum Ditelluride Devices.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Algae-based biomaterial synthesis and applications for a sustainable green bioeconomy: A review.

Journal of biotechnology·2026
Same author

NUT1-Exo70A1 Regulates Xylem Vessel Development and Influences Water Use Efficiency in Maize.

Nature communications·2026
Same author

Interface Engineering of van der Waals Devices Based on Metal Disulfide Vertical Heterostructures for Enhanced Photoresponse.

Nano letters·2025

Related Experiment Video

Updated: Jun 26, 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

455

Mapping Method of Human Arm Motion Based on Surface Electromyography Signals.

Yuanyuan Zheng1,2, Gang Zheng1, Hanqi Zhang3

  • 1School of Mechanical and Energy Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.

Sensors (Basel, Switzerland)
|May 11, 2024
PubMed
Summary

This study precisely maps human arm movements using surface electromyography (sEMG) signals and deep learning. The method enables accurate prosthetic arm control, enhancing assistive device development.

Keywords:
deep learninggesture recognitionhuman arm motion mappingsEMG

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

10.9K
A Standardized Method for Measurement of Elbow Kinesthesia
07:56

A Standardized Method for Measurement of Elbow Kinesthesia

Published on: October 10, 2020

7.1K

Related Experiment Videos

Last Updated: Jun 26, 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

455
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

10.9K
A Standardized Method for Measurement of Elbow Kinesthesia
07:56

A Standardized Method for Measurement of Elbow Kinesthesia

Published on: October 10, 2020

7.1K

Area of Science:

  • Biomedical Engineering
  • Robotics
  • Neuroscience

Background:

  • Precise mapping of human arm movements is crucial for advanced prosthetics and assistive devices.
  • Surface electromyography (sEMG) signals offer a promising, non-invasive method for capturing motor intent.
  • Integrating sEMG with inertial measurement units (IMUs) can improve the accuracy of movement reconstruction.

Purpose of the Study:

  • To develop and validate a precise method for mapping human arm movements using sEMG signals.
  • To enhance action recognition and joint angle prediction for robotic arm control.
  • To lay the foundation for more intuitive and responsive prosthetic and assistive devices.

Main Methods:

  • Multi-channel sEMG signal acquisition and processing, including filtering and normalization.
  • Utilizing an Inertial Measurement Unit (IMU) for accurate joint angle calculation.
  • Developing a hybrid deep learning model (CNN-ANN) with multi-feature fusion for gesture recognition.
  • Employing a backpropagation neural network for nonlinear fitting between sEMG and joint angles.

Main Results:

  • Achieved accurate recognition of various human arm movements, including hand gestures and continuous joint actions.
  • Established a highly accurate nonlinear model for predicting joint angles from sEMG signals.
  • Demonstrated successful prosthetic arm control with precise movement prediction and execution.

Conclusions:

  • sEMG signals show significant potential for precise robotic arm control.
  • The developed deep learning approach enhances the accuracy of human movement mapping.
  • This research provides a strong foundation for developing next-generation intuitive prostheses and assistive technologies.