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

Synaptic neurotransmitter concentration modulation during learning in bio-inspired spiking neural network.

Cognitive neurodynamics·2026
Same author

Spatial Phase Coherence in Femtosecond Coherent Raman Scattering.

Physical review letters·2026
Same author

Gait rehabilitation outcomes with EksoNR: an exploratory study comparing progressive vs. non-progressive neurological impairments.

Disability and rehabilitation·2026
Same author

Mixture of experts extra tree-based sEMG hand gesture recognition.

Scientific reports·2026
Same author

A climate-informed dengue transmission model with Bayesian decision support.

Acta tropica·2026
Same author

Exploring Familiarization Phases to Establish Baseline Sessions in Lower Limb Exoskeleton Therapy for Neurological Patients.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Related Experiment Video

Updated: Jan 14, 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

1.2K

L-SHADE optimized learning framework for sEMG hand gesture recognition.

Naveen Gehlot1,2, Ankit Vijayvargiya3, Ashutosh Jena4

  • 1Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India. naveen.gehlot@manipal.edu.

Scientific Reports
|October 21, 2025
PubMed
Summary

This study optimizes hand gesture recognition (HGR) using an Extra Tree (ET) classifier with Linear Population Size Reduction Success-History Adaptation Differential Evolution (L-SHADE). The L-SHADE-optimized ET framework significantly improves accuracy and reduces computational time for real-time gesture recognition.

Keywords:
Electromyography signalHand gesture recognitionHuman machine interactionHyperparameterMachine learning modelsOptimization techniques

More Related Videos

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.3K
Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

2.1K

Related Experiment Videos

Last Updated: Jan 14, 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

1.2K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.3K
Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

2.1K

Area of Science:

  • * Human-Computer Interaction
  • * Machine Learning
  • * Signal Processing

Background:

  • * Real-time Hand Gesture Recognition (HGR) relies on machine-learning classifiers (MLCs).
  • * MLC performance is highly dependent on hyperparameter tuning with real-time data.
  • * Existing methods require optimization for enhanced accuracy and efficiency.

Purpose of the Study:

  • * To develop an optimized Extra Tree (ET) MLC framework for HGR.
  • * To enhance real-time gesture recognition accuracy and reduce computational load.
  • * To evaluate the efficacy of Linear Population Size Reduction Success-History Adaptation Differential Evolution (L-SHADE) for hyperparameter optimization.

Main Methods:

  • * Utilized real-time surface electromyography (sEMG) signals from two forearm muscles.
  • * Captured six distinct hand gesture movements for classification.
  • * Employed ten MLCs, including an Extra Tree (ET) classifier, and optimized it using L-SHADE.

Main Results:

  • * The L-SHADE-optimized ET framework achieved a mean accuracy of 87.89% on acquired data, an improvement from 84.14%.
  • * Mean computational time decreased from 8.62 to 3.16 milliseconds.
  • * Demonstrated a >3.0% mean accuracy improvement on a public 15-hand gesture dataset.

Conclusions:

  • * The L-SHADE optimization significantly enhances the performance of the ET classifier for HGR.
  • * The proposed framework offers a robust and efficient solution for real-time hand gesture recognition.
  • * This approach provides a valuable contribution to the field of human-computer interaction and wearable technology.