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

Factors Associated with Chemsex Practice Among People Living with HIV in Serbia.

AIDS and behavior·2026
Same author

Concurrent control of natural and robotic limbs through a tactile-encoded brain-computer interface.

Nature communications·2026
Same author

Spiking neural network decoders of finger forces from high-density intramuscular microelectrode arrays.

Nature communications·2026
Same author

A Roadmap to Navigate the Future of Neural Engineering.

Journal of neural engineering·2026
Same author

Author Correction: Implanted microelectrode arrays in reinnervated muscles allow separation of neural drives from transferred polyfunctional nerves.

Nature biomedical engineering·2026
Same author

The increase in time to task failure following endurance training is associated with adjustments in motor unit firing properties.

Journal of applied physiology (Bethesda, Md. : 1985)·2026

Related Experiment Video

Updated: Feb 27, 2026

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses
05:21

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

Published on: January 7, 2019

8.4K

User adaptation in Myoelectric Man-Machine Interfaces.

Janne M Hahne1, Marko Markovic2, Dario Farina2,3

  • 1Neurorehabilitaiton Systems Research Group, Department of Trauma Surgery, Orthopedic Surgery and Hand Surgery, Universiy Medical Center Göttingen, Göttingen, Germany. janne.hahne@bccn.uni-goetttingen.de.

Scientific Reports
|July 2, 2017
PubMed
Summary
This summary is machine-generated.

This study compared machine learning for prosthetic control. Regression-based methods showed superior online performance and user adaptability compared to classification, highlighting the importance of user-in-the-loop testing for myocontrol reliability.

More Related Videos

Therapy Interventions for Upper Limb Amputees Undergoing Selective Nerve Transfers
07:59

Therapy Interventions for Upper Limb Amputees Undergoing Selective Nerve Transfers

Published on: October 29, 2021

4.2K
Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
11:16

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

Published on: July 22, 2014

16.7K

Related Experiment Videos

Last Updated: Feb 27, 2026

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses
05:21

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

Published on: January 7, 2019

8.4K
Therapy Interventions for Upper Limb Amputees Undergoing Selective Nerve Transfers
07:59

Therapy Interventions for Upper Limb Amputees Undergoing Selective Nerve Transfers

Published on: October 29, 2021

4.2K
Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
11:16

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

Published on: July 22, 2014

16.7K

Area of Science:

  • Biomedical Engineering
  • Rehabilitation Robotics
  • Machine Learning in Healthcare

Background:

  • Current clinical hand prostheses offer limited, single-function control.
  • Advanced machine learning approaches enhance functionality but lack clinical reliability due to signal non-stationarities.
  • Robust myocontrol is crucial for improving prosthetic device performance and user experience.

Purpose of the Study:

  • To analyze the robustness and performance of two distinct machine learning approaches for electromyography (EMG) signal-based prosthetic control.
  • To compare classification (finite classes) and regression (continuous mapping) methods under non-stationary EMG conditions in a closed-loop system.
  • To evaluate the impact of user adaptation in online testing scenarios.

Main Methods:

  • Implemented both classification and regression models for EMG-to-command projection.
  • Artificially introduced non-stationarities into EMG signals to simulate real-world variability.
  • Conducted offline and online closed-loop tests with ten able-bodied individuals and one transradial amputee.

Main Results:

  • Both classification and regression methods were similarly affected by non-stationarities in offline testing.
  • In online tests, the regression-based approach demonstrated significantly greater robustness to signal changes than the classification approach.
  • Users could better adapt and correct control commands using the regression-based method.

Conclusions:

  • Online, user-in-the-loop testing is essential for accurately assessing myocontrol system performance.
  • Regression-based EMG control offers superior adaptability and robustness for prosthetic applications compared to classification.
  • Findings suggest regression is more effective for user correction of prosthetic control commands.