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

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

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

Published on: July 26, 2013

Preferred sensor sites for surface EMG signal decomposition.

Farah Zaheer1, Serge H Roy, Carlo J De Luca

  • 1NeuroMuscular Research Center, Boston University, Boston, MA 02215, USA. farahz@bu.edu

Physiological Measurement
|January 21, 2012
PubMed
Summary

Surface EMG (sEMG) sensor placement impacts motor unit yield. Preferred sites are between muscle centers and tendons, optimizing signal-to-noise ratio for reliable motor unit decomposition.

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

Platform Design for Optical Screening and Conditioning for Injury Resilience.

Military medicine·2024
Same author

System Architecture for VR Yoga Therapy Platform with 6-DoF Whole-Body Avatar Tracking.

... IEEE International Conference on Artificial Intelligence and Virtual Reality. IEEE International Conference on Artificial Intelligence and Virtual Reality·2024
Same author

Classification performance of sEMG and kinematic parameters for distinguishing between non-lame and induced lameness conditions in horses.

Frontiers in veterinary science·2024
Same author

A Virtual Reality Exergame: Clinician-Guided Breathing and Relaxation for Children with Muscular Dystrophy.

2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)·2023
Same author

Electromyographic and Kinematic Comparison of the Leading and Trailing Fore- and Hindlimbs of Horses during Canter.

Animals : an open access journal from MDPI·2023
Same author

Adaptations in equine axial movement and muscle activity occur during induced fore- and hindlimb lameness: A kinematic and electromyographic evaluation during in-hand trot.

Equine veterinary journal·2022

Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Kinesiology

Background:

  • Non-invasive surface electromyography (sEMG) enables motor unit action potential train decomposition.
  • This technique offers advantages over invasive needle EMG, including ease of use and reduced infection risk.

Purpose of the Study:

  • To investigate how sensor site influences motor unit yield from lower and upper limb muscles.
  • To identify optimal sensor locations for maximizing motor unit decomposition.

Main Methods:

  • Surface EMG sensors were placed on six lower limb and one upper limb muscle.
  • Motor unit yield was quantified across different sensor locations.
  • Signal-to-noise ratio (SNR) and tissue thickness were analyzed in relation to motor unit yield.

More Related Videos

Extraction of the EPP Component from the Surface EMG
07:16

Extraction of the EPP Component from the Surface EMG

Published on: December 16, 2009

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
09:42

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Published on: January 24, 2025

Related Experiment Videos

Last Updated: May 25, 2026

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

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

Published on: July 26, 2013

Extraction of the EPP Component from the Surface EMG
07:16

Extraction of the EPP Component from the Surface EMG

Published on: December 16, 2009

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
09:42

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Published on: January 24, 2025

Main Results:

  • Motor unit yield varied significantly depending on sensor placement.
  • Optimal sensor sites were identified between the muscle's center and its tendinous regions.
  • Higher motor unit yield correlated positively with SNR, which was inversely related to subcutaneous tissue thickness.

Conclusions:

  • Sensor site selection is critical for maximizing motor unit yield in sEMG decomposition.
  • A minimum SNR of 3 is recommended for reliable motor unit yield.
  • Findings guide optimal sensor placement for improved motor unit analysis.