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

Serum IgG, definite anti-dsDNA positivity, and advanced HBV-related liver disease: a laboratory-based retrospective study.

Clinica chimica acta; international journal of clinical chemistry·2026
Same author

Corrigendum to "Cheek acupuncture can improve the depressive and anxious symptoms of patients with moderate depression disorders" [Complement Ther Med 99 (2026), 103383].

Complementary therapies in medicine·2026
Same author

Spatial transcriptome and single-cell reveal the role of sorbitol metabolism in hepatocellular carcinoma progression and tumor microenvironment.

SLAS technology·2026
Same author

Acupuncture for chronic insomnia with mild cognitive impairment: protocol for a randomized controlled trial.

Frontiers in neurology·2026
Same author

Multimodal Feature Prototype Learning for Interpretable and Discriminative Cancer Survival Prediction.

IEEE journal of biomedical and health informatics·2026
Same author

A novel prognostic zinc finger gene model for hepatocellular carcinoma via machine learning.

Discover oncology·2026

Related Experiment Video

Updated: Nov 6, 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

898

Emerging Wearable Interfaces and Algorithms for Hand Gesture Recognition: A Survey.

Shuo Jiang, Peiqi Kang, Xinyu Song

    IEEE Reviews in Biomedical Engineering
    |May 7, 2021
    PubMed
    Summary

    Wearable sensors and algorithms offer new ways to recognize hand gestures for rehabilitation and human-computer interaction. Future work aims to improve accuracy, robustness, and comfort for daily use.

    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

    4.7K
    Capturing Representative Hand Use at Home Using Egocentric Video in Individuals with Upper Limb Impairment
    06:25

    Capturing Representative Hand Use at Home Using Egocentric Video in Individuals with Upper Limb Impairment

    Published on: December 23, 2020

    2.7K

    Related Experiment Videos

    Last Updated: Nov 6, 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

    898
    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

    4.7K
    Capturing Representative Hand Use at Home Using Egocentric Video in Individuals with Upper Limb Impairment
    06:25

    Capturing Representative Hand Use at Home Using Egocentric Video in Individuals with Upper Limb Impairment

    Published on: December 23, 2020

    2.7K

    Area of Science:

    • Biomedical Engineering
    • Human-Computer Interaction
    • Rehabilitation Technology

    Background:

    • Neurological diseases significantly impair hand function, impacting daily life.
    • Wearable hand gesture interfaces can restore function and enhance communication.
    • Applications span rehabilitation, prosthetics, human-computer interaction, and more.

    Purpose of the Study:

    • To review novel sensing interfaces and algorithms for hand gesture recognition.
    • To synthesize current applications in diverse fields.
    • To identify future research directions.

    Main Methods:

    • Review of sensing modalities: electrical, mechanical, acoustical/vibratory, and optical.
    • Analysis of algorithms: classification for poses and regression for joint angles.
    • Examination of machine learning and deep learning approaches.

    Main Results:

    • Identified key sensing modalities and algorithm categories for gesture recognition.
    • Highlighted the use of conventional and deep learning algorithms.
    • Demonstrated broad applicability across various domains.

    Conclusions:

    • Sensing interfaces and algorithms are crucial for restoring and augmenting hand function.
    • Further research is needed to enhance accuracy, reliability, and user-friendliness.
    • Development of softer, less obtrusive interfaces is a key future direction.