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

PathoTiroid dataset: Indonesian collection (PTIC)-a histopathology image dataset for papillary thyroid carcinoma.

BMC research notes·2026
Same author

Coupling and Preload Analysis of Piezoelectric Actuator and Nonlinear Stiffness Mechanism.

Micromachines·2025
Same author

Research on the prevention of tooth demineralization and the effects and mechanisms of different mineralization solutions on the metabolism of <i>Streptococcus mutans</i>.

Frontiers in oral health·2025
Same author

Subspace-Based Two-Step Iterative Shrinkage/Thresholding Algorithm for Microwave Tomography Breast Imaging.

Sensors (Basel, Switzerland)·2025
Same author

Comparing Inclusion Methods on Juxta-pleural into Lung Parenchyma.

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

Clustering for mitigating subject variability in driving fatigue classification using electroencephalography source-space functional connectivity features.

Journal of neural engineering·2024

Related Experiment Video

Updated: Jul 15, 2025

The Muscle Cuff Regenerative Peripheral Nerve Interface for the Amplification of Intact Peripheral Nerve Signals
07:30

The Muscle Cuff Regenerative Peripheral Nerve Interface for the Amplification of Intact Peripheral Nerve Signals

Published on: January 13, 2022

2.1K

Electric powered wheelchair control using user-independent classification methods based on surface electromyography

Hassam Iqbal1, Jinchuan Zheng2, Rifai Chai2

  • 1Department of Engineering Technologies, Swinburne University of Technology, John Street, 3122, Melbourne, Victoria, Australia. hiqbal@swin.edu.au.

Medical & Biological Engineering & Computing
|September 25, 2023
PubMed
Summary

This study introduces a hand gesture control system for electric-powered wheelchairs (EPWs), improving accessibility for individuals with motor impairments. The system uses multi-class support vector machine (MC-SVM) and decision tree (DT) for accurate, user-independent wheelchair operation.

Keywords:
Assistive technologyElectric powered wheelchairMachine learningSurface electromyography

More Related Videos

Determining and Controlling External Power Output During Regular Handrim Wheelchair Propulsion
08:55

Determining and Controlling External Power Output During Regular Handrim Wheelchair Propulsion

Published on: February 5, 2020

7.5K
Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
11:16

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

Published on: July 22, 2014

16.3K

Related Experiment Videos

Last Updated: Jul 15, 2025

The Muscle Cuff Regenerative Peripheral Nerve Interface for the Amplification of Intact Peripheral Nerve Signals
07:30

The Muscle Cuff Regenerative Peripheral Nerve Interface for the Amplification of Intact Peripheral Nerve Signals

Published on: January 13, 2022

2.1K
Determining and Controlling External Power Output During Regular Handrim Wheelchair Propulsion
08:55

Determining and Controlling External Power Output During Regular Handrim Wheelchair Propulsion

Published on: February 5, 2020

7.5K
Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
11:16

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

Published on: July 22, 2014

16.3K

Area of Science:

  • Assistive Technology
  • Human-Computer Interaction
  • Robotics

Background:

  • Conventional joystick controls for electric-powered wheelchairs (EPWs) pose challenges for individuals with motor impairments, particularly those with finger dexterity issues.
  • Assistive technology (AT) plays a crucial role in enhancing mobility and independence for individuals with disabilities.
  • Existing control methods often require user-specific training, limiting their immediate applicability.

Purpose of the Study:

  • To develop and evaluate a novel hand gesture-based control system for EPWs.
  • To address the limitations of joystick controls for users with finger impairments.
  • To investigate and compare the effectiveness of different gesture recognition algorithms for user-independent control.

Main Methods:

  • A hand gesture recognition system was developed for EPW operation.
  • Four gesture recognition methods were explored: linear regression (LR), regularized linear regression (RLR), decision tree (DT), and multi-class support vector machine (MC-SVM).
  • User-independent classification methods (MC-SVM and DT) were prioritized to overcome training limitations.

Main Results:

  • Linear regression methods (LR, RLR) achieved high accuracy (94.85%, 95.88%) but required user-specific training.
  • User-independent methods, MC-SVM and DT, demonstrated excellent performance with MC-SVM achieving 99.05% accuracy and precision, and DT achieving 97.77% accuracy and precision.
  • All six participants successfully controlled the EPW using the proposed gesture system without collisions.

Conclusions:

  • The developed hand gesture-based control system offers a highly accurate and effective alternative to traditional joystick controls for EPWs.
  • The MC-SVM and DT methods successfully address finger dependency issues, enabling user-independent operation.
  • This technology has the potential to significantly improve the mobility and autonomy of individuals with motor impairments.