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

You might also read

Related Articles

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

Sort by
Same author

Polymer Replicas of Fs-Laser-Induced Periodic Surface Structures for Cell Attachment.

Materials (Basel, Switzerland)·2026
Same author

Electrosensitivity in planthoppers (Insecta: Hemiptera: Auchenorrhyncha: Fulgoromorpha).

Journal of comparative physiology. A, Neuroethology, sensory, neural, and behavioral physiology·2026
Same author

Oriented artificial nanofibers and laser induced periodic surface structures as substrates for Schwann cells alignment.

Open research Europe·2024
Same author

Simulating the Effect of Removing Circulating Tumor Cells (CTCs) from Blood Reveals That Only Implantable Devices Can Significantly Reduce Metastatic Burden of Patients.

Cancers·2024
Same author

Potential Predictors for Deterioration of Renal Function After Transfusion.

Anesthesia and analgesia·2024
Same author

A Novel Sensory Feedback Approach to Facilitate Both Predictive and Corrective Control of Grasping Force in Myoelectric Prostheses.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2023

Related Experiment Video

Updated: Feb 20, 2026

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

1.4K

Capacitively coupled EMG detection via ultra-low-power microcontroller STFT.

Theresa Roland, Werner Baumgartner, Sebastian Amsuess

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 25, 2017
    PubMed
    Summary

    A new algorithm effectively distinguishes motion artifacts from electromyography (EMG) signals, enhancing myoelectric prosthesis performance. This real-time solution is optimized for low-power microcontrollers, improving prosthetic control.

    More Related Videos

    A Real-Time Wearable Electromyography Measurement System for Small Animals
    05:00

    A Real-Time Wearable Electromyography Measurement System for Small Animals

    Published on: November 15, 2024

    1.4K
    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

    44.2K

    Related Experiment Videos

    Last Updated: Feb 20, 2026

    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

    1.4K
    A Real-Time Wearable Electromyography Measurement System for Small Animals
    05:00

    A Real-Time Wearable Electromyography Measurement System for Small Animals

    Published on: November 15, 2024

    1.4K
    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

    44.2K

    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Rehabilitation Technology

    Background:

    • Motion artifacts significantly degrade electromyography (EMG) sensor data.
    • Accurate EMG signal processing is crucial for effective myoelectric prosthesis control.
    • Existing methods struggle with real-time artifact removal on resource-constrained devices.

    Purpose of the Study:

    • To develop a novel algorithm for differentiating motion artifacts from contraction EMG signals.
    • To enhance the performance and reliability of myoelectric prostheses.
    • To implement the algorithm on an ultra-low-power microcontroller for real-time applications.

    Main Methods:

    • Developed a new algorithm to classify EMG signals, distinguishing artifacts from muscle contractions.
    • Utilized Short Time Fourier Transformation (STFT) for real-time signal analysis.
    • Introduced the Sum of Differences (SOD) as a novel parameter for EMG classification.

    Main Results:

    • The algorithm successfully differentiates motion artifacts from valid EMG signals.
    • Implementation on an ultra-low-power microcontroller demonstrated feasibility for real-time use.
    • Measurements with a capacitive coupling EMG prototype showed satisfactory error rates.

    Conclusions:

    • The developed algorithm significantly improves myoelectric prosthesis performance by filtering motion artifacts.
    • The novel SOD parameter provides an effective method for EMG classification.
    • The real-time, low-power implementation enables practical application in advanced prosthetics.