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: Apr 30, 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

1.5K

Movement error rate for evaluation of machine learning methods for sEMG-based hand movement classification.

Arjan Gijsberts, Manfredo Atzori, Claudio Castellini

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |April 25, 2014
    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

    Multi-class segmentation of aortic branches and zones in computed tomography angiography: The AortaSeg24 challenge.

    Medical image analysis·2026
    Same author

    From muscles to motion: the role of sensor layout and physiological factors in hand motion decoding.

    Scientific reports·2026
    Same author

    A comparative study of deep learning for cortical lesion MRI segmentation with explainability analysis in multiple sclerosis.

    NeuroImage. Clinical·2026
    Same author

    Optimization-enhanced Gaussian mixture modeling for data-driven subphenotyping of septic shock.

    Computer methods in biomechanics and biomedical engineering·2026
    Same author

    Multi-structure segmentation in CBCT volumes: The ToothFairy2 challenge.

    Medical image analysis·2026
    Same author

    Usability, Acceptability, and Feasibility of a Personalized Adaptive Mirror Therapy for Upper-Limb Poststroke Rehabilitation Using Immersive Virtual Reality and Myoelectric Control: Single-Arm Pre-Post Study.

    JMIR rehabilitation and assistive technologies·2026

    Learning algorithms enhance myoelectric prostheses. Combining surface electromyography and accelerometry improves hand movement classification accuracy and reduces prediction delay, outperforming individual sensor use.

    Area of Science:

    • Biomedical Engineering
    • Machine Learning
    • Rehabilitation Robotics

    Background:

    • Myoelectric prostheses aim to restore hand function using muscle signals.
    • Dexterity improvement in prostheses is crucial for user independence.
    • Learning algorithms offer potential for advanced prosthetic control.

    Purpose of the Study:

    • To benchmark machine learning algorithms for myoelectric prosthesis control.
    • To evaluate feature representations and kernel functions for surface electromyography (sEMG) data.
    • To assess the impact of multimodal sensor fusion (sEMG and accelerometry) on performance.

    Main Methods:

    • Utilized the NinaPro database (v2) with 6 DOF force and 40 discrete hand movement data.
    • Employed a kernel method comparing three feature representations and three kernel functions.

    More Related Videos

    Assessment and Communication for People with Disorders of Consciousness
    07:37

    Assessment and Communication for People with Disorders of Consciousness

    Published on: August 1, 2017

    11.4K
    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
    07:05

    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

    Published on: October 27, 2016

    8.8K

    Related Experiment Videos

    Last Updated: Apr 30, 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

    1.5K
    Assessment and Communication for People with Disorders of Consciousness
    07:37

    Assessment and Communication for People with Disorders of Consciousness

    Published on: August 1, 2017

    11.4K
    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
    07:05

    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

    Published on: October 27, 2016

    8.8K
  • Introduced a movement error rate metric, independent of prediction delay, to evaluate performance.

    • Main Results:

    • Nonlinear kernel functions successfully learned force regression and movement classification.
    • The exp- χ(2) kernel consistently outperformed the radial basis function kernel.
    • Multimodal classification using sEMG and accelerometry significantly increased accuracy compared to single modalities.

    Conclusions:

    • Kernel methods, particularly exp- χ(2), are effective for myoelectric control.
    • Sensor fusion of sEMG and accelerometry substantially enhances prosthetic performance.
    • The proposed movement error rate provides a more comprehensive evaluation of prosthetic controllability.