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

Systematic analysis of success of lower limb muscle combinations in the prediction of ankle biomechanics during stair descent: Guiding input selection in sEMG-based prosthetic control.

Gait & posture·2026
Same author

Prediction of ankle kinematics and kinetics in stair ascent motion using surface EMG feature inputs of lower extremity muscle combinations.

Journal of biomechanics·2026
Same author

Synergistic muscle activation impacts muscle spindles projecting to the mouse trigeminal mesencephalic nucleus.

Journal of neurophysiology·2025
Same author

Neurochallenges in smart cities: state-of-the-art, perspectives, and research directions.

Frontiers in neuroscience·2025
Same author

Biochemical, biomechanical and imaging biomarkers of ischemic stroke: Time for integrative thinking.

The European journal of neuroscience·2024
Same author

Effects of elastic therapeutic taping on along-muscle fascicle local length changes: Magnetic resonance and diffusion tensor imaging based assessment.

Journal of biomechanics·2023

Related Experiment Video

Updated: Dec 1, 2025

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

16.5K

Development of a neural network based control algorithm for powered ankle prosthesis.

A Doğukan Keleş1, Can A Yucesoy1

  • 1Biomedical Engineering Institute, Boğaziçi University, Istanbul, Turkey.

Journal of Biomechanics
|November 6, 2020
PubMed
Summary

This study developed a neural network control algorithm for lower limb prostheses using surface electromyograms (sEMG). The algorithm successfully predicted ankle movement, identifying optimal muscle combinations for economic and flexible prosthesis control.

Keywords:
Ankle disarticulationNeural networksPowered ankle prosthesisTranstibial amputationsEMG

More Related Videos

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
06:58

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

10.0K
A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation
11:06

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

Published on: April 12, 2016

10.7K

Related Experiment Videos

Last Updated: Dec 1, 2025

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

16.5K
A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
06:58

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

10.0K
A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation
11:06

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

Published on: April 12, 2016

10.7K

Area of Science:

  • Biomedical Engineering
  • Neuroprosthetics
  • Rehabilitation Technology

Background:

  • Lower limb amputation necessitates advanced prosthetic solutions, with surface electromyograms (sEMG) being explored for powered prosthesis control.
  • Current commercial prostheses lack sEMG control, and transtibial amputations present challenges due to reduced lower leg muscle mass.
  • Optimizing sEMG usage is crucial for developing economical and flexible powered prosthesis controllers.

Purpose of the Study:

  • To assess the feasibility of a neural network (NN) approach for powered ankle prosthesis control using sEMG.
  • To develop a control algorithm capable of predicting sagittal ankle angle and moment during walking.
  • To identify the most economic and flexible muscle combinations for NN-based prosthesis control.

Main Methods:

  • Utilized sEMG data from five lower extremity muscles (tibialis anterior, medial gastrocnemius, rectus femoris, biceps femoris, gluteus maximus) in healthy individuals.
  • Developed a time-delay feed-forward-multilayer-architecture NN algorithm to predict ankle angle and moment.
  • Ranked muscle combination variations using Pearson's correlation coefficient and root-mean-square error.

Main Results:

  • The NN algorithm successfully predicted sagittal ankle angle and moment using sEMG.
  • The tibialis anterior (TA) + medial gastrocnemius (MG) combination achieved high accuracy (rposition=0.952, rmoment=0.997).
  • TA + MG + biceps femoris (BF) and MG + BF + gluteus maximus (GM) were identified as economic and flexible variations, respectively.

Conclusions:

  • A neural network approach is feasible for developing sEMG-based powered ankle prosthesis control.
  • Specific muscle combinations (TA+MG, TA+MG+BF, MG+BF+GM) offer promising solutions for economic and flexible prosthesis design.
  • Further research involving amputee data is recommended for algorithm training and testing.