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: Oct 21, 2025

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

854

Pattern recognition of EMG signals for low level grip force classification.

Salman Mohd Khan1, Abid Ali Khan1, Omar Farooq2

  • 1Department of Mechanical Engineering, AMU, Aligarh, UP, India.

Biomedical Physics & Engineering Express
|September 2, 2021
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

An optimized EEG-based intrinsic brain network for depression detection using differential graph centrality.

Biomedical physics & engineering express·2025
Same author

Prevalence of Musculoskeletal Disorders in Heavy Vehicle Drivers and Office Workers: A Comparative Analysis Using a Machine Learning Approach.

Healthcare (Basel, Switzerland)·2025
Same author

Assessment of Changes in Craniofacial Structures, Bite Force and Periodontal Status Following Fixed Functional Appliance Therapy.

Turkish journal of orthodontics·2023
Same author

EMG based classification for pick and place task.

Biomedical physics & engineering express·2021
Same author

Lower limb rehabilitation using multimodal measurement of sit-to-stand and stand-to-sit task.

Disability and rehabilitation. Assistive technology·2019
Same author

Ergonomics investigation for orientation of the handles of wood routers.

International journal of occupational safety and ergonomics : JOSE·2017

This study enhances prosthetic control by accurately classifying hand grasp force levels using muscle signals. A novel two-step feature selection method achieved up to 99% classification accuracy, improving prosthetic functionality.

Area of Science:

  • Biomedical Engineering
  • Rehabilitation Engineering
  • Signal Processing

Background:

  • Upper limb amputation necessitates advanced prosthetic devices for restoring function.
  • Myoelectric prosthetics utilize muscle signals for hand gesture and force level control.
  • Accurate classification of force levels is crucial for intuitive prosthetic control.

Purpose of the Study:

  • To develop and validate a pattern recognition algorithm for classifying different force levels using Electromyography (EMG) signals.
  • To implement a two-step feature selection process combining ReliefF and Neighborhood Component Analysis (NCA).
  • To optimize the number of muscles required for effective force level classification.

Main Methods:

  • Acquisition of Electromyography (EMG) and fingertip force signals during varying force contractions.
Keywords:
EMG classificationEMG signals for low level grip force classificationFeature extractionrecognition of grip force

More Related Videos

Measurement of Spatial Stability in Precision Grip
09:36

Measurement of Spatial Stability in Precision Grip

Published on: June 4, 2020

3.3K
An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles
09:27

An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles

Published on: August 25, 2020

4.4K

Related Experiment Videos

Last Updated: Oct 21, 2025

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

854
Measurement of Spatial Stability in Precision Grip
09:36

Measurement of Spatial Stability in Precision Grip

Published on: June 4, 2020

3.3K
An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles
09:27

An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles

Published on: August 25, 2020

4.4K
  • Application of a two-step feature selection: ReliefF for general ranking and NCA for personalized selection.
  • Classification using Support Vector Machines (SVM) and Random Forest (RF) algorithms.
  • Optimization of muscle set size for classification accuracy.

    • Main Results:

    • A maximum classification accuracy of 99% was achieved using SVM with two muscles.
    • Optimal features included Auto Regressive coefficients, Willison Amplitude, and Slope Sign Change.
    • Mean classification accuracies were 94.5% for SVM and 91.7% for RF across subjects.

    Conclusions:

    • The proposed two-step feature selection effectively identifies key features for myoelectric control.
    • The algorithm enables high-accuracy classification of hand grasp force levels, enhancing prosthetic usability.
    • Optimizing muscle selection and feature extraction is vital for robust prosthetic control systems.