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

Learning Disabilities01:25

Learning Disabilities

296
Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
Dyslexia is a...
296

You might also read

Related Articles

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

Sort by
Same author

Deep neural network for food image classification and nutrient identification: A systematic review.

Reviews in endocrine & metabolic disorders·2023
Same author

Epidemic efficacy of Covid-19 vaccination against Omicron: An innovative approach using enhanced residual recurrent neural network.

PloS one·2023
Same author

Predicting risk of obesity and meal planning to reduce the obese in adulthood using artificial intelligence.

Endocrine·2022
Same author

Prediction of Conversion from CIS to Clinically Definite Multiple Sclerosis Using Convolutional Neural Networks.

Computational and mathematical methods in medicine·2022
Same author

Optical coherence tomography image based eye disease detection using deep convolutional neural network.

Health information science and systems·2022
Same author

Gene Mutation Classification through Text Evidence Facilitating Cancer Tumour Detection.

Journal of healthcare engineering·2021

Related Experiment Video

Updated: Oct 1, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

480

CNN-LSTM Hybrid Real-Time IoT-Based Cognitive Approaches for ISLR with WebRTC: Auditory Impaired Assistive

Meenu Gupta1, Narina Thakur2, Dhruvi Bansal3

  • 1Department of Computer Science and Engineering, Chandigarh University, Punjab, India.

Journal of Healthcare Engineering
|March 3, 2022
PubMed
Summary

This study introduces an Indian Sign Linguistic Recognition system using 3D-CNNs and LSTM to bridge communication gaps for the hearing impaired. The web application achieves a 97.21% recognition rate, enhancing online interaction.

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

4.3K
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.7K

Related Experiment Videos

Last Updated: Oct 1, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

480
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

4.3K
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.7K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • The Internet of Medical Things (IoMT) offers new avenues for healthcare, yet communication barriers persist for individuals with hearing and listening impairments, especially on online platforms.
  • Existing communication tools often do not adequately address the specific needs of the auditory and hearing-impaired community, particularly in the context of evolving digital interactions.
  • The COVID-19 pandemic highlighted the challenges faced by individuals with disabilities in accessing and utilizing online communication platforms.

Purpose of the Study:

  • To develop a communication bridge for the hearing-impaired community, facilitating interaction with the wider global community.
  • To propose and evaluate an Indian Sign Linguistic Recognition (ISLR) system designed to overcome communication challenges on online platforms.
  • To enhance accessibility and inclusivity in digital communication for individuals with auditory impairments.

Main Methods:

  • Implementation of a novel Indian Sign Linguistic Recognition system utilizing three-dimensional convolutional neural networks (3D-CNNs) and long short-term memory (LSTM) for gesture analysis.
  • Development of a web application incorporating WebRTC for real-time communication and teleprompting technology to convert sign language into audible sound.
  • Leveraging kinesics linguistics principles to interpret and understand sign language communication between individuals.

Main Results:

  • The proposed ISLR system demonstrated a high average recognition rate of 97.21% in the developed web application.
  • Successful integration of advanced machine learning techniques (3D-CNNs and LSTM) for accurate hand gesture and sign language recognition.
  • The web application effectively transforms sign language into audible sound, improving communication accessibility.

Conclusions:

  • The developed ISLR system significantly enhances communication for the hearing-impaired on online platforms, acting as a vital bridge.
  • The combination of 3D-CNNs, LSTM, WebRTC, and teleprompting technology offers a robust solution for real-time sign language interpretation.
  • This research contributes to greater inclusivity in the digital age by addressing critical communication challenges faced by individuals with hearing impairments.