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

Unraveling the flavor development mechanisms of areca-intercropped Hainan Dayezhong white tea.

Journal of the science of food and agriculture·2026
Same author

Phase unwrapping in digital holographic interferometry via physics-informed convolutional-Fourier neural network.

Optics express·2026
Same author

BehaviorDiff: a VAE-diffusion framework for AI-generated synthetic behavioral data in procrastination prediction for sports instruction.

BMC sports science, medicine & rehabilitation·2026
Same author

The translational potential of drug-induced hypothermia in acute ischemic stroke.

Science translational medicine·2026
Same author

Achieving High Selectivity and Stability in Electrocatalytic CO<sub>2</sub> Reduction in Acidic Media via Ion Confinement.

Angewandte Chemie (International ed. in English)·2026
Same author

The Emerging Role of Explainable Artificial Intelligence in EEG-Based Autism Research: A Systematic Review.

NeuroSci·2026

Related Experiment Video

Updated: Apr 12, 2026

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy
13:44

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy

Published on: August 8, 2011

14.8K

A Fuzzy Kernel Motion Classifier for Autonomous Stroke Rehabilitation.

Zhe Zhang, Luca Liparulo, Massimo Panella

    IEEE Journal of Biomedical and Health Informatics
    |May 9, 2015
    PubMed
    Summary

    This study introduces a new fuzzy kernel motion classifier for autonomous post-stroke rehabilitation. It accurately classifies patient movements, even irregular ones, improving remote rehabilitation systems.

    More Related Videos

    Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes
    04:49

    Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes

    Published on: September 6, 2024

    1.7K
    A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
    06:58

    A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

    Published on: November 6, 2015

    10.4K

    Related Experiment Videos

    Last Updated: Apr 12, 2026

    Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy
    13:44

    Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy

    Published on: August 8, 2011

    14.8K
    Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes
    04:49

    Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes

    Published on: September 6, 2024

    1.7K
    A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
    06:58

    A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

    Published on: November 6, 2015

    10.4K

    Area of Science:

    • Biomedical Engineering
    • Rehabilitation Technology
    • Artificial Intelligence in Healthcare

    Background:

    • High costs of inpatient stroke rehabilitation necessitate autonomous, home-based systems.
    • Reliable patient motion monitoring is crucial for effective autonomous rehabilitation.
    • Inertia sensing and pattern recognition offer cost-effective solutions for motion monitoring.

    Purpose of the Study:

    • To develop a novel fuzzy kernel motion classifier for stroke patient rehabilitation training.
    • To address challenges in classifying irregular motions common in stroke survivors.
    • To improve the accuracy of autonomous motion classification in rehabilitation settings.

    Main Methods:

    • A novel fuzzy kernel motion classifier utilizing geometrically unconstrained fuzzy membership functions.
    • Classification of real motion data from stroke patients with varying impairment levels.
    • Comparison of the proposed classifier's error rate against popular algorithms.

    Main Results:

    • The proposed fuzzy kernel classifier demonstrated high accuracy in motion classification.
    • It effectively handled overlapping motion classes and poorly performed motion samples.
    • The classifier showed superior performance with a lower error rate compared to existing algorithms.

    Conclusions:

    • The novel fuzzy kernel motion classifier is effective for autonomous post-stroke rehabilitation.
    • It offers a robust solution for classifying diverse and irregular patient movements.
    • This technology can enhance the feasibility and efficacy of remote stroke rehabilitation.