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

Hand Gesture Intention Detection Using sEMG and Transfer Learning in Stroke Survivors.

IEEE journal of biomedical and health informatics·2026
Same author

A dual-function dry electrode for electromyography recording and transcutaneous electrical stimulation.

Scientific reports·2026
Same author

Evaluation of s-EMG Sensor Locations for Upper-Limb Compensatory Movement Detection.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

Effectiveness of mirror therapy to treat musculoskeletal injuries of the hand and wrist: A systematic review.

Journal of hand therapy : official journal of the American Society of Hand Therapists·2025
Same author

Face Validity and Usability Evaluation of a Wearable Upper-Limb Motion Sensing System for Home Use.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2025
Same author

Estimating Thorax and Shoulder Motion Using Magnetic-Free Quaternion-Based Functional Sensor-To-Segment Calibration.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2025

Related Experiment Video

Updated: Sep 16, 2025

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

43.5K

Demographic-Driven Electromyography Analysis: Advancing Personalized Biosignal Interpretation.

Maedeh Mohammadiazni, Yue Zhou, Ana Luisa Trejos

    IEEE ... International Conference on Rehabilitation Robotics : [Proceedings]
    |July 11, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study developed a novel model to estimate personalized surface electromyography (sEMG) baselines, improving rehabilitation robotics for neuromuscular disorders. Individualized sEMG baselines enhance patient recovery monitoring and treatment strategies.

    More Related Videos

    Author Spotlight: Epimysial Electrode Fabrication and Testing in ACL Injury Studies
    04:48

    Author Spotlight: Epimysial Electrode Fabrication and Testing in ACL Injury Studies

    Published on: April 12, 2024

    559
    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

    865

    Related Experiment Videos

    Last Updated: Sep 16, 2025

    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

    43.5K
    Author Spotlight: Epimysial Electrode Fabrication and Testing in ACL Injury Studies
    04:48

    Author Spotlight: Epimysial Electrode Fabrication and Testing in ACL Injury Studies

    Published on: April 12, 2024

    559
    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

    865

    Area of Science:

    • Biomedical Engineering
    • Rehabilitation Science
    • Neuroscience

    Background:

    • Surface electromyography (sEMG) is crucial for monitoring neuromuscular recovery.
    • Individual variability in sEMG baselines, influenced by demographics, limits current applications.
    • Personalized sEMG baselines are needed for effective rehabilitation robotics.

    Purpose of the Study:

    • To develop a novel model for estimating individualized sEMG baselines, specifically for Root Mean Square (RMS).
    • To enhance the effectiveness of rehabilitation robotics by accounting for demographic differences in sEMG signals.
    • To enable personalized recovery strategies for patients with neuromuscular disorders.

    Main Methods:

    • Collected demographic and physiological data from 30 healthy participants.
    • Recorded sEMG signals from forearm muscles during a pushing task with varying wrist positions.
    • Developed Decision Tree Regression models, optimized with Recursive Feature Elimination, for individualized baseline estimation.

    Main Results:

    • Regression models achieved high accuracies, ranging from 88.81% to 95.6%.
    • Global sensitivity analysis identified key influential factors for sEMG baseline estimation.
    • Findings suggest that comprehensive sEMG data collection can improve model generalizability.

    Conclusions:

    • The proposed model offers a promising approach for creating individualized sEMG baselines.
    • Personalized sEMG baselines can significantly advance rehabilitation robotics.
    • This research paves the way for tailored recovery strategies in neuromuscular disorder treatment.