Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

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

Prevalence and determinants of academic burnout among undergraduates in a traditional Chinese medicine university: a cross-sectional study.

Frontiers in psychology·2026

Same author

Theoretical prediction of semiconductors by data driven light-element substitution in topological materials.

Science bulletin·2026

Same author

Efficacy of scapular-focused rehabilitation protocol on shoulder function following Proximal Humeral Internal Locking System fixation for proximal humeral fractures: a randomized controlled trial.

International orthopaedics·2026

Same author

Detection of Microbehavior Intervals for Predicting Mental Health: Clinically Relevant and Advanced Multimodal Temporal Analysis.

Journal of medical Internet research·2026

Same author

Dielectric-Confinement-Induced in-Plane Photoelectric Anisotropy in Isotropic Quasi-1D γ-GaS Nanoribbon.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same author

Multi-dimensional analyses unveil strain-specific metabolic perturbations and flavour deterioration traits induced by spoilage bacteria in cold-stored chicken breast.

International journal of food microbiology·2026

Same journal

Ultrasound-Informed State Estimation of Wrist Tremor Dynamics via Koopman Operator for Personalized Sensory Peripheral Nerve Stimulation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026

Same journal

Motion Intention Recognition and DDPG-Based Adaptive Impedance Control for a Robotic Upper-Limb Exoskeleton.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026

Same journal

CNN-Based Modelling Reveals Temporal Brain Dynamics of Auditory Intensity Processing.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026

Same journal

Pathology-Informed Augmentation Improves Cross-Cohort IMU-to-vGRF Estimation Between Healthy Adults and Adults With Osteoarthritis.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026

Same journal

Effects of task-driven head orientations on gait and balance during walking in virtual reality.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026

Same journal

Wearable sensor-based Mild Cognitive Impairment Identification: A Multi-Domain Gait Analysis Approach with Association Rule Mining.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 15, 2025

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Synergy-Based Neural Interface for Human Gait Tracking With Deep Learning.

Dezhen Xiong, Daohui Zhang, Xingang Zhao

IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society

|October 27, 2021

Summary

This summary is machine-generated.

This study introduces a new human-machine interface for gait tracking using muscle synergy, outperforming traditional methods. Deep learning effectively estimates joint angles from electromyography signals, advancing neural interface technology.

More Related Videos

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

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

Published on: April 12, 2016

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

Related Experiment Videos

Last Updated: Oct 15, 2025

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

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

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

Published on: April 12, 2016

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

Area of Science:

Biomedical Engineering
Neuroscience
Robotics

Background:

Electromyography (EMG) signals offer neural information for human-machine interfaces.
Muscle synergy represents high-level neural control for coordinated movements.
Existing EMG-based interfaces often rely on motor unit decomposition.

Purpose of the Study:

To develop a novel neural interface for human gait tracking using muscle synergy.
To evaluate the effectiveness of deep learning in estimating lower limb joint angles from muscle synergy.
To compare the proposed method against traditional machine learning approaches.

Main Methods:

Muscle synergy extraction using Principle Component Analysis (PCA), Factor Analysis (FA), and Nonnegative Matrix Factorization (NMF).
A deep regression neural network (bidirectional gated recurrent unit - BGRU) for temporal information extraction.
Experimentation with eight subjects walking at various speeds (0.5-3.0 km/h).

Main Results:

The synergy-based approach significantly outperformed linear regression (LR) and multilayer perceptron (MLP) methods.
PCA achieved the highest performance with R_var² scores of 0.871±0.029.
Accurate estimation of hip, knee, and ankle joint angles with low Root Mean Square Error (RMSE).

Conclusions:

Muscle synergy is a viable high-level neural control feature for gait analysis.
Deep learning models can effectively decode muscle synergy for precise joint angle estimation.
The proposed method offers a promising new direction for advanced neural interfaces and gait analysis.