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 Experiment Video

Updated: May 10, 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

Biometric recognition based on free-text keystroke dynamics.

Ahmed A Ahmed, Issa Traore

    IEEE Transactions on Cybernetics
    |June 13, 2013
    PubMed
    Summary
    This summary is machine-generated.

    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

    Duodeno-jejunal intussusception: an unusual manifestation of ampullary adenocarcinoma.

    Journal of surgical case reports·2026
    Same author

    Classic Hodgkin lymphoma with IGH::CCND1 rearrangement: evidence of clonal relation to preceding mantle cell lymphoma.

    Blood·2026
    Same author

    Collision Tumors Involving Metastatic Carcinoma and Plasma Cell Myeloma: Report of Two Cases.

    Case reports in pathology·2026
    Same author

    Primary Cutaneous Gamma-Delta T-Cell Lymphoma With Aberrant CD20 Expression: Answer.

    The American Journal of dermatopathology·2026
    Same author

    Rapidly Enlarging Indurated and Ulcerated Nodules in an Elderly Woman.

    The American Journal of dermatopathology·2026
    Same author

    EBV-positive CD8 + peripheral T-cell lymphoma in the post-transplant setting: case report and review of the literature.

    Journal of hematopathology·2026
    Same journal

    An Evolutionary Algorithm Assisted by an Ensemble of Pareto-Optimal Surrogate Models.

    IEEE transactions on cybernetics·2026
    Same journal

    A Quantum Self-Attention Neural Network Model on Quantum Circuits.

    IEEE transactions on cybernetics·2026
    Same journal

    Semi-Explicit Solution of Some Discrete-Time Higher-Order-Cost Mean-Field-Type Control.

    IEEE transactions on cybernetics·2026
    Same journal

    A Novel One-Step Small Object Detector for Autonomous Aerial Vehicles.

    IEEE transactions on cybernetics·2026
    Same journal

    Online Data-Driven-Based Optimal Output Tracking Control Without Initial Stabilizing Policy.

    IEEE transactions on cybernetics·2026
    Same journal

    Digital Redesign-Based Interval State Estimation for Continuous Systems With Aperiodic Discrete Measurements.

    IEEE transactions on cybernetics·2026
    See all related articles

    This study introduces a novel method for analyzing keystroke dynamics in free text, significantly reducing errors and processing time. The approach combines monograph and digraph analysis with neural networks for improved accuracy in keystroke recognition.

    Area of Science:

    • Computer Science
    • Biometrics
    • Human-Computer Interaction

    Background:

    • Keystroke dynamics analysis for free text recognition is hindered by data sparsity and variability.
    • Existing methods often report high error rates, limiting practical applications.

    Purpose of the Study:

    • To develop a more accurate and efficient method for free text keystroke dynamics recognition.
    • To improve upon existing techniques by addressing data limitations and processing time.

    Main Methods:

    • A novel approach combining monograph and digraph analysis of keystrokes.
    • Utilizing a neural network to predict missing digraphs based on monitored keystroke relationships.

    Main Results:

    • Achieved accuracy comparable to state-of-the-art methods with significantly lower processing time.

    More Related Videos

    An Assessment Method and Toolkit to Evaluate Keyboard Design on Smartphones
    05:42

    An Assessment Method and Toolkit to Evaluate Keyboard Design on Smartphones

    Published on: October 5, 2020

    Related Experiment Videos

    Last Updated: May 10, 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

    An Assessment Method and Toolkit to Evaluate Keyboard Design on Smartphones
    05:42

    An Assessment Method and Toolkit to Evaluate Keyboard Design on Smartphones

    Published on: October 5, 2020

  • Experimental results in a heterogeneous environment: False Acceptance Ratio (FAR) = 0.0152%, False Rejection Ratio (FRR) = 4.82%, Equal Error Rate (EER) = 2.46%.
  • Follow-up experiment in a homogeneous environment: FAR = 0%, FRR = 5.01%, EER = 2.13%.

    • Conclusions:

    • The proposed method offers a robust and efficient solution for free text keystroke dynamics analysis.
    • Demonstrated superior performance in terms of accuracy and speed compared to previous approaches.