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

New scale for assessing spasticity based on the pendulum test.

Computer methods in biomechanics and biomedical engineering·2021
Same author

A principal component analysis (PCA) based assessment of the gait performance.

Biomedizinische Technik. Biomedical engineering·2021
Same author

Hybrid Tongue - Myoelectric Control Improves Functional Use of a Robotic Hand Prosthesis.

IEEE transactions on bio-medical engineering·2021
Same author

Does galvanic vestibular stimulation decrease spasticity in clinically complete spinal cord injury?

International journal of rehabilitation research. Internationale Zeitschrift fur Rehabilitationsforschung. Revue internationale de recherches de readaptation·2018
Same author

Assessment of Spasticity by a Pendulum Test in SCI Patients Who Exercise FES Cycling or Receive Only Conventional Therapy.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2018
Same author

Posture in dentists: Sitting vs. standing positions during dentistry work--An EMG study.

Srpski arhiv za celokupno lekarstvo·2016

Related Experiment Video

Updated: Jan 5, 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

1.1K

EMG map image processing for recognition of fingers movement.

Ivan Topalović1, Stevica Graovac2, Dejan B Popović3

  • 1Institute of Technical Sciences of SASA, Knez Mihailova 35/IV, Belgrade, Serbia.

Journal of Electromyography and Kinesiology : Official Journal of the International Society of Electrophysiological Kinesiology
|October 27, 2019
PubMed
Summary

A novel image processing method accurately recognizes individual finger movements using electromyography (EMG) maps. This technique achieves high accuracy, paving the way for advanced assistive devices.

Keywords:
Array ElectrodesDelicate movementsEMG mapsFinger Movements RecognitionImage processingSpatial and temporal model

More Related Videos

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization
09:57

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization

Published on: September 20, 2024

3.3K
Non-Invasive Modulation and Robotic Mapping of Motor Cortex in the Developing Brain
08:26

Non-Invasive Modulation and Robotic Mapping of Motor Cortex in the Developing Brain

Published on: July 1, 2019

7.0K

Related Experiment Videos

Last Updated: Jan 5, 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

1.1K
Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization
09:57

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization

Published on: September 20, 2024

3.3K
Non-Invasive Modulation and Robotic Mapping of Motor Cortex in the Developing Brain
08:26

Non-Invasive Modulation and Robotic Mapping of Motor Cortex in the Developing Brain

Published on: July 1, 2019

7.0K

Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Rehabilitation Technology

Background:

  • Electromyography (EMG) is a standard noninvasive technique for assessing muscle activity.
  • Current methods for analyzing EMG data, especially for fine motor control, can be limited in precision.
  • There is a need for advanced methods to interpret complex muscle activation patterns for assistive technologies.

Purpose of the Study:

  • To develop and validate a new image processing technique for recognizing individual finger movements using EMG maps.
  • To quantify the accuracy of this automated recognition system compared to expert clinical assessment.
  • To explore the potential application of this technology in controlling assistive devices.

Main Methods:

  • EMG signals were recorded using a 24-contact array electrode connected to a wireless digital amplifier.
  • EMG maps were generated from the recorded signals to visualize muscle activity.
  • An image processing algorithm was developed to detect and quantify high activity regions within the EMG maps.
  • The system was tested on individuals without known motor impairments during specific finger movements.

Main Results:

  • The developed method successfully identified temporal and spatial patterns in EMG maps corresponding to distinct finger movements.
  • The automated recognition system achieved an average accuracy of 97.87% ± 0.92% when compared to expert clinician recognition.
  • The results demonstrate the system's capability to differentiate subtle muscle activation patterns.

Conclusions:

  • The novel image processing method based on EMG maps provides a highly accurate and reliable way to recognize individual finger movements.
  • This technology has significant potential for real-time control of wearable assistive systems, such as hand prostheses and exoskeletons.
  • The system's wearable nature and potential for microcomputer implementation make it suitable for practical assistive applications.