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: Jul 16, 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

MoCap-Referenced Neck-Shoulder sEMG-IMU Decoding for Discrete Assistive Commands: A Pilot Study.

Ameer H Majeed1, Farah Masood1, Hussein A Abdullah2

  • 1Biomedical Engineering Department, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad 17635, Iraq.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

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

Comparison Study of Different Feature Selection Techniques for the Diagnosis of Alzheimer's Disease.

American journal of Alzheimer's disease and other dementias·2026
Same author

Smart Compression Sock for Early Detection of Diabetic Foot Ulcers.

Sensors (Basel, Switzerland)·2024
Same author

Development and Testing of a Soft Exoskeleton Robotic Hand Training Device.

Sensors (Basel, Switzerland)·2023
Same author

Initial Testing of Robotic Exoskeleton Hand Device for Stroke Rehabilitation.

Sensors (Basel, Switzerland)·2023
Same author

Modeling contact forces during human-robot interactions for performing activities of daily living.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine·2023
Same author

A Novel Application of Deep Learning (Convolutional Neural Network) for Traumatic Spinal Cord Injury Classification Using Automatically Learned Features of EMG Signal.

Sensors (Basel, Switzerland)·2022

This study introduces a new neck and shoulder surface electromyography (sEMG) system for hands-free control. The novel framework shows high accuracy in decoding user commands, offering a promising alternative for assistive technology.

Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Human-Computer Interaction

Background:

  • Hands-free command interfaces are crucial for individuals with motor impairments.
  • Neck-shoulder surface electromyography (sEMG) offers a viable alternative control method.
  • Previous validation methods may overestimate accuracy; false-trigger behavior needs quantification.

Purpose of the Study:

  • To present a motion-capture (MoCap)-referenced decoding framework using sEMG and inertial measurement units (IMUs).
  • To evaluate the accuracy and generalization of this framework for decoding seven to eight distinct user commands.
  • To quantify false-trigger rates during a REST state and assess trade-offs.

Main Methods:

  • Utilized four bilateral upper trapezius (UT) and sternocleidomastoid (SCM) sEMG channels with integrated IMUs.
Keywords:
IMUMoCapassistive devicesneck–shoulder musclessEMG

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

Related Experiment Videos

Last Updated: Jul 16, 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

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

  • Employed optical MoCap for kinematic reference and quality control.
  • Classified features using LDA, kNN, and SVM, evaluated with within-subject, trial-wise, and leave-one-subject-out (LOSO) testing.

    • Main Results:

    • Achieved high within-subject accuracy (96.02% - 96.35%) and pooled trial-wise accuracy (90.5% - 92.1%).
    • LOSO accuracy decreased to 60.4% - 63.8%, indicating generalization challenges.
    • REST false activation rate (FAR) ranged from approximately 9.8% to 25.6%.

    Conclusions:

    • Demonstrated controlled offline pilot feasibility of the MoCap-referenced sEMG decoding framework.
    • Quantified key generalization trade-offs and REST false-activation rates.
    • Provides a foundation for future validation in diverse clinical populations.