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 Experiment Video

Updated: May 25, 2026

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

Subject-specific lower limb waveforms planning via artificial neural network.

Trieu Phat Luu1, H B Lim, Xingda Qu

  • 1School of Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), Singapore 639798.

IEEE ... International Conference on Rehabilitation Robotics : [Proceedings]
|January 26, 2012
PubMed
Summary
This summary is machine-generated.

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

Reduced Gaze-Stimulus Synchrony to a Rhythmic Children's Song in Young Children With Autism: A Recurrence Quantification Analysis Approach.

Journal of autism and developmental disorders·2026
Same author

Enhancing performance feedback in computer-based aviation training simulations.

Ergonomics·2025
Same author

Characteristics of Visual Attention in Young Children with Autism During A Hide-And-Seek Video Viewing Task.

Journal of autism and developmental disorders·2025
Same author

Lower-limb inter-joint coordination during balance recovery after trips.

Gait & posture·2024
Same author

Balance recovery after trips is affected by the type of tripping obstacles.

Ergonomics·2024
Same author

Enhancing squat movement classification performance with a gated long-short term memory with transformer network model.

Sports biomechanics·2024
Same journal

Exploring Synergy Between Tactile Perception and Arm Usage.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2025
Same journal

Multi-Modal Muscle Activation Modeling Using Koopman Operator Linearization for an Ankle Exoskeleton.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2025
Same journal

Unsupervised Robot-Assisted Therapy at Home After Stroke: a Pilot Feasibility Study.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2025
Same journal

Optimizing Senior Living with Robots: A User Study on Social and Architectural Integration.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2025
Same journal

Effects of Exoskeletons on Error Between Marker and Markerless Motion Capture in Children With Crouch Gait: A Pilot Study.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2025
Same journal

Recovr Glove: Accessible Hand Exoskeleton for Stroke Rehabilitation and Everyday Aid.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2025
See all related articles

This study enhances robotic gait rehabilitation by introducing a new model for generating natural lower limb joint angle waveforms. The model accurately predicts gait patterns using multi-layer perceptron neural networks and Fourier coefficients.

Area of Science:

  • Biomechanics
  • Robotics
  • Rehabilitation Engineering

Background:

  • Robotic systems are increasingly used in gait rehabilitation to assist patients.
  • Effective gait rehabilitation requires natural and smooth gait patterns, influenced by parameters like stride length and cadence.
  • A systematic methodology for gait pattern planning is currently lacking.

Purpose of the Study:

  • To introduce an enhanced model for generating lower limb joint angle waveforms during walking.
  • To improve gait pattern planning in robotic rehabilitation systems.
  • To utilize gait parameters and anthropometric data for precise waveform generation.

Main Methods:

  • A motion capture system recorded walking data.
  • Lower limb joint angle waveforms were decomposed into Fourier coefficients.

More Related Videos

Standing Neurophysiological Assessment of Lower Extremity Muscles Post-Stroke
08:23

Standing Neurophysiological Assessment of Lower Extremity Muscles Post-Stroke

Published on: July 26, 2021

Related Experiment Videos

Last Updated: May 25, 2026

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

Standing Neurophysiological Assessment of Lower Extremity Muscles Post-Stroke
08:23

Standing Neurophysiological Assessment of Lower Extremity Muscles Post-Stroke

Published on: July 26, 2021

  • Multi-layer perceptron neural networks (MLPNNs) predicted Fourier coefficient vectors based on gait parameters and anthropometrics.
  • Predicted waveforms were compared to experimental data using correlation coefficient, MAD, and TAD.

    • Main Results:

    • The MLPNNs successfully predicted Fourier coefficient vectors for specific subjects and desired gait parameters.
    • Constructed joint angle waveforms closely matched experimental waveforms.
    • Assessment parameters confirmed the high quality of the MLPNNs' predictions.

    Conclusions:

    • The enhanced model effectively generates natural lower limb joint angle waveforms for robotic gait rehabilitation.
    • This approach provides a systematic methodology for gait pattern planning.
    • The findings contribute to more effective and personalized robotic-assisted gait recovery.