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

Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

192
The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
192

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Optimizing plane detection in point clouds through line sampling.

Scientific reports·2025
Same author

Integrated water resource management in the Segura Hydrographic Basin: An artificial intelligence approach.

Journal of environmental management·2024
Same author

Editorial for the Special Issue Recognition Robotics.

Sensors (Basel, Switzerland)·2023
Same author

Convolutional neural network-based classification and monitoring models for lung cancer detection: 3D perspective approach.

Heliyon·2023
Same author

RANSAC for Robotic Applications: A Survey.

Sensors (Basel, Switzerland)·2023
Same author

Brain Mapping of Behavioral Domains Using Multi-Scale Networks and Canonical Correlation Analysis.

Frontiers in neuroscience·2022
Same journal

Modeling and analysis of forward and inverse kinematics for a flexible Stewart platform.

PloS one·2026
Same journal

Barriers and facilitators to healthcare utilization amongst people living with sickle cell disease in the United States: A scoping review.

PloS one·2026
Same journal

Enhancing data completeness in time series: Imputation strategies for missing data using significant periodically correlated components.

PloS one·2026
Same journal

Key targets and mechanisms by which gut microbiota-derived metabolites regulate Alzheimer's disease through the immune - inflammatory pathway: Based on network pharmacology and molecular docking.

PloS one·2026
Same journal

Grid-tied Transformer-less Boost Switched Capacitor Topology (TLBSCT) for PV applications.

PloS one·2026
Same journal

The load-velocity profiles and exercise-specific velocity zones for seven commonly used weightlifting exercises.

PloS one·2026
See all related articles

Related Experiment Video

Updated: Aug 23, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K

Sign language recognition by means of common spatial patterns: An analysis.

Itsaso Rodríguez-Moreno1, José María Martínez-Otzeta1, Izaro Goienetxea1

  • 1Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), Donostia-San Sebastián, Spain.

Plos One
|October 31, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an Argentinian Sign Language (LSA) recognition system using hand landmarks and Common Spatial Patterns (CSP) for improved communication. The system achieved high accuracy, aiding deaf and hard-of-hearing individuals.

More Related Videos

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.1K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.0K

Related Experiment Videos

Last Updated: Aug 23, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.1K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.0K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Linguistics

Background:

  • Millions face communication barriers due to hearing loss, highlighting the need for accessible solutions.
  • Existing communication methods for deaf and hard-of-hearing individuals often rely on interpreters, limiting spontaneous interaction.
  • Developing automated sign language recognition systems can bridge this gap.

Purpose of the Study:

  • To develop and evaluate an Argentinian Sign Language (LSA) recognition system.
  • To address the communication challenges faced by LSA signers and non-signers.
  • To explore the efficacy of hand landmark analysis and machine learning for LSA recognition.

Main Methods:

  • Utilized the LSA64 dataset, extracting hand landmarks from video data.
  • Applied the Common Spatial Patterns (CSP) algorithm for dimensionality reduction of landmark signals.
  • Employed classifiers including Random Forest (RF), K-Nearest Neighbors (KNN), and Multilayer Perceptron (MLP).

Main Results:

  • Achieved high accuracy rates between 0.90 and 0.95 for recognizing 42 distinct LSA signs.
  • Demonstrated the effectiveness of CSP in feature extraction for sign language recognition.
  • Validated the performance of RF, KNN, and MLP classifiers on the processed data.

Conclusions:

  • The developed LSA recognition system shows significant promise in facilitating communication.
  • Hand landmark analysis combined with CSP and machine learning offers a viable approach for sign language recognition.
  • This technology has the potential to enhance accessibility for the deaf and hard-of-hearing community.