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

State Space Representation01:27

State Space Representation

643
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
643
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

387
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
387
State Space to Transfer Function01:21

State Space to Transfer Function

639
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
639

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

Data-driven clustering of prefrontal activation identifies functional phenotypes under prioritized dual-task walking conditions in Parkinson's disease.

Neuroscience letters·2026
Same author

Unilateral Ankle Exoskeleton Assistance Reshapes Gait: Temporal Parameters, Load Distribution, and Inter-Limb Symmetry.

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

Site-directed mutagenesis and semi-rational design to enhance chitinolytic activity of bacterial exochitinase.

Microbial cell factories·2026
Same author

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

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

Related Experiment Video

Updated: Mar 8, 2026

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

1.4K

Continuous Estimation of Human Multi-Joint Angles From sEMG Using a State-Space Model.

Qichuan Ding, Jianda Han, Xingang Zhao

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |January 24, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new method to accurately estimate multi-joint movements from surface electromyography (sEMG) signals by segmenting redundant data. The approach significantly improves estimation accuracy compared to traditional neural networks.

    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

    10.3K
    Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
    09:41

    Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

    Published on: April 21, 2023

    2.3K

    Related Experiment Videos

    Last Updated: Mar 8, 2026

    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

    1.4K
    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

    10.3K
    Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
    09:41

    Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

    Published on: April 21, 2023

    2.3K

    Area of Science:

    • Biomedical Engineering
    • Neuroscience
    • Rehabilitation Technology

    Background:

    • Estimating continuous multi-joint movements from surface electromyography (sEMG) is challenging due to muscle coupling.
    • Traditional methods using artificial neural networks struggle with redundant sEMG data and online error correction.

    Purpose of the Study:

    • To develop a novel method for accurate and smooth estimation of multi-joint movements from sEMG signals.
    • To effectively utilize redundant sEMG data for improved prediction and error correction.

    Main Methods:

    • A correlation-based redundancy-segmentation method was proposed to divide sEMG vectors into irredundant and redundant subvectors.
    • A state-space framework was developed to model motion, using the irredundant subvector as input and the redundant subvector as measurement output.
    • An unscented Kalman filter (UKF) was employed within a closed-loop system to estimate multi-joint angles, leveraging redundant data to mitigate model uncertainties.

    Main Results:

    • The proposed method achieved a maximum Root Mean Square Error (RMSE) of 0.16±0.03 for upper limb multi-joint movement estimation.
    • This result was significantly better than the RMSE of 0.25±0.06 and 0.27±0.07 obtained by common neural networks (p < 0.05).

    Conclusions:

    • The developed method effectively segments and utilizes redundant sEMG data for accurate motion estimation.
    • The proposed approach offers superior performance in estimating continuous multi-joint movements compared to existing techniques.