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

Hierarchy of Motor Control

Hierarchy of Motor Control

The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Association between clopidogrel preloading time and post-procedural troponin elevation in patients with stable angina undergoing elective percutaneous coronary intervention: a retrospective cohort study.

Journal of Yeungnam medical science·2026

Same author

The performance of ASpirin-FREE therapy after successful percutaneous coronary intervention for acute coronary syndrome: the ASFREE prospective pilot study.

Journal of Yeungnam medical science·2026

Same author

Reflection-Enhanced Raman Identification of Single Bacterial Cells Patterned Using Capillary Assembly.

ACS sensors·2025

Same author

Prognostic Role of Pan-Immune-Inflammatory Value in Patients with Non-ST-Segment Elevation Acute Coronary Syndrome.

Journal of cardiovascular development and disease·2025

Same author

Laparoscopic removal of a broken acupuncture needle in pancreatic head: a case report.

Journal of surgical case reports·2024

Same author

TopicFM+: Boosting Accuracy and Efficiency of Topic-Assisted Feature Matching.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2024

Same journal

Multimodal Contrastive Spatiotemporal Self-Organizing Neural Networks for In-Home Activity Learning of Mild Cognitive Impairment.

IEEE journal of biomedical and health informatics·2026

Same journal

Integrating Multi-View Residue Graph and Protein Language Model for Cell-Penetrating Peptide Prediction via Global-Local Graph Aggregation and Cross-Attentive Fusion.

IEEE journal of biomedical and health informatics·2026

Same journal

An Ultra-Lightweight Cross-scale Attention Mamba Network for Accurate Skin Lesion Segmentation.

IEEE journal of biomedical and health informatics·2026

Same journal

Explanation-Guided Reconstruction of Missing Clinical Features for Survival Prediction in Pancreatic Cancer.

IEEE journal of biomedical and health informatics·2026

Same journal

stDGCN: A dual-augmentation graph convolutional network for identifying spatial domains with attention mechanism.

IEEE journal of biomedical and health informatics·2026

Same journal

Patient-specific Biomechanical Investigation of Percutaneous Pulmonary Valves: Towards the Integration of Routinely Acquired Clinical Data and Fluid-structure Interaction Simulations.

IEEE journal of biomedical and health informatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 7, 2026

Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality

Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality

Published on: September 3, 2015

Supervised hierarchical Bayesian model-based electomyographic control and analysis.

Hyonyoung Han, Sungho Jo

IEEE Journal of Biomedical and Health Informatics

|October 11, 2013

Summary

This summary is machine-generated.

This study introduces a Bayesian model for classifying limb motions using surface electromyography (sEMG) signals. The model identifies latent neural states to interpret muscle activation strategies, enhancing sEMG-based control applications.

More Related Videos

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

Surface Electromyographic Biofeedback as a Rehabilitation Tool for Patients with Global Brachial Plexus Injury Receiving Bionic Reconstruction

Surface Electromyographic Biofeedback as a Rehabilitation Tool for Patients with Global Brachial Plexus Injury Receiving Bionic Reconstruction

Published on: September 28, 2019

Related Experiment Videos

Last Updated: May 7, 2026

Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality

Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality

Published on: September 3, 2015

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

Surface Electromyographic Biofeedback as a Rehabilitation Tool for Patients with Global Brachial Plexus Injury Receiving Bionic Reconstruction

Surface Electromyographic Biofeedback as a Rehabilitation Tool for Patients with Global Brachial Plexus Injury Receiving Bionic Reconstruction

Published on: September 28, 2019

Area of Science:

Biomedical Engineering
Machine Learning
Neuroscience

Background:

Surface electromyography (sEMG) is crucial for understanding muscle activity.
Accurate motion classification from sEMG signals is challenging.
Interpreting muscle activation strategies requires advanced modeling.

Purpose of the Study:

To propose a supervised hierarchical Bayesian model for sEMG-based motion classification and strategy analysis.
To unify feature extraction and classification using probabilistic inference.
To identify latent neural states (LNSs) governing sEMG signals and interpret muscle activation strategies.

Main Methods:

Developed a supervised hierarchical Bayesian model.
Utilized probabilistic inference and learning to identify LNSs.
Applied the model to nine-class limb motion classification using four sEMG sensors.

Main Results:

The model achieved high performance in classifying sEMG patterns across various activation levels.
Demonstrated generalized classification across different subjects.
Successfully performed online classification and provided interpretation of sEMG strategic patterns.

Conclusions:

The proposed Bayesian model effectively classifies sEMG patterns and interprets muscle activation strategies.
LNSs are key to capturing diverse motions and understanding control mechanisms.
The model shows significant potential for sEMG control-based applications.