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

Classification of Signals01:30

Classification of Signals

773
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
773
Force Classification01:22

Force Classification

1.4K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.4K
Classification of Systems-I01:26

Classification of Systems-I

285
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
285
Methods of Classification and Identification01:28

Methods of Classification and Identification

134
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
134
Classification of Systems-II01:31

Classification of Systems-II

219
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
219

You might also read

Related Articles

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

Sort by
Same author

SignBridge Bilingual Sign Language Avatar-Construction Principles and Experts Quality Assessment.

Sensors (Basel, Switzerland)·2026
Same author

Remote sensing for marine oil spill detection, mapping, and monitoring: A systematic review and bibliometric analysis.

Marine pollution bulletin·2026
Same author

Unsupervised Clustering and Ensemble Learning for Classifying Lip Articulation in Fingerspelling.

Sensors (Basel, Switzerland)·2025
Same author

Specifics of Data Collection and Data Processing during Formation of RailVista Dataset for Machine Learning- and Deep Learning-Based Applications.

Sensors (Basel, Switzerland)·2024
Same author

Continuous Sign Language Recognition and Its Translation into Intonation-Colored Speech.

Sensors (Basel, Switzerland)·2023

Related Experiment Video

Updated: Aug 29, 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

714

Sign Language Recognition Method Based on Palm Definition Model and Multiple Classification.

Nurzada Amangeldy1, Saule Kudubayeva1, Akmaral Kassymova2

  • 1Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, Nur-Sultan 010008, Kazakhstan.

Sensors (Basel, Switzerland)
|September 9, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel algorithm for real-time Kazakh sign language recognition, achieving 97.6% accuracy. The low-cost system aims to improve communication for the deaf community by recognizing 41 letters of the Kazakh sign alphabet.

Keywords:
MediaPipe FaceMediaPipe HandsSVMhand shapemultiple classificationpalm definition modelpatternrecognitionsign language

More Related Videos

Super-resolution Imaging of Neuronal Dense-core Vesicles
09:30

Super-resolution Imaging of Neuronal Dense-core Vesicles

Published on: July 2, 2014

9.8K
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

4.0K

Related Experiment Videos

Last Updated: Aug 29, 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

714
Super-resolution Imaging of Neuronal Dense-core Vesicles
09:30

Super-resolution Imaging of Neuronal Dense-core Vesicles

Published on: July 2, 2014

9.8K
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

4.0K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Sign language interpretation systems are crucial for the deaf community, with over 430 million people worldwide facing communication barriers.
  • Existing solutions often rely on expensive equipment, limiting accessibility, especially in low- and middle-income countries.

Purpose of the Study:

  • To develop a low-cost, real-time automatic sign language interpretation system for Kazakh.
  • To analyze and identify optimal gesture recognition methods for sign language applications.
  • To enhance societal integration for individuals with hearing impairments through improved communication tools.

Main Methods:

  • Analysis of various gesture recognition techniques for suitability in automatic sign language interpretation.
  • Development of a novel algorithm utilizing a palm definition model and linear models for Kazakh sign language.
  • Integration of a unified function for frame depth map mode configuration to enhance recognition performance.

Main Results:

  • The proposed algorithm successfully recognizes 41 out of 42 letters in the Kazakh sign alphabet, achieving 97.6% accuracy.
  • The system demonstrates improved recognition performance through the integrated frame depth map mode.
  • This advancement enables the creation of a multimodal database for gesture recognition systems.

Conclusions:

  • The developed algorithm offers a significant improvement in recognizing Kazakh sign language, surpassing previous limitations.
  • The low-cost, high-accuracy system has the potential to greatly benefit the deaf community by facilitating communication.
  • The system's design supports the creation of comprehensive video data resources for further research and development in gesture recognition.