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

Intellectual Disability01:29

Intellectual Disability

159
Intellectual disability (ID) is a neurodevelopmental condition characterized by deficits in intellectual and adaptive functioning that manifest during the developmental period. This condition encompasses challenges in reasoning, memory, problem-solving, and learning, accompanied by impairments in everyday life skills, such as communication, self-care, and social interactions. Intellectual disability affects approximately 1% of the population in the United States, impacting an estimated 5...
159
Learning Disabilities01:25

Learning Disabilities

271
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...
271

You might also read

Related Articles

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

Sort by
Same author

An intelligent context-aware facial emotion recognition system for hearing-impaired individuals using large language models and neural network-based feature fusion.

Scientific reports·2026
Same author

CavitoMod-UTMDNet: A Mechanistic Cavitation-Diffusion Framework for Ultrasound-Targeted Microbubble Destruction-Enhanced Liposomal Doxorubicin Delivery in Pancreatic Cancer.

Cancer biotherapy & radiopharmaceuticals·2026
Same author

Hybrid deep learning techniques for adaptive routing and congestion control in urban VANET for wireless mobile networking.

Scientific reports·2026
Same author

A lightweight deep evidence fusion framework for smart home appliance detection and classification via internet of things devices.

Scientific reports·2025
Same author

Harnessing optimization with deep learning approach on intelligent transportation system for anomaly detection in pedestrian walkways.

Scientific reports·2025
Same author

Advanced internet of things enhanced activity recognition for disability people using deep learning model with nature-inspired optimization algorithms.

Scientific reports·2025

Related Experiment Video

Updated: Sep 11, 2025

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

4.1K

Intelligent deep learning for human activity recognition in individuals with disabilities using sensor based IoT and

Mohammed Maray1,2

  • 1Department of Information Systems, College of Computer Science, King Khalid University, Abha, Saudi Arabia. Mmarey@kku.edu.sa.

Scientific Reports
|August 13, 2025
PubMed
Summary

This study introduces an Intelligent Deep Learning Technique for Human Activity Recognition of Persons with Disabilities using Sensor Technology (IDLTHAR-PDST). The novel method achieves 98.75% accuracy in recognizing activities, enhancing independent living for individuals.

Keywords:
Cloud computingDeep learningDisability personsHuman activity recognitionInternet of thingsSensor technology

More Related Videos

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

9.0K
Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.3K

Related Experiment Videos

Last Updated: Sep 11, 2025

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

4.1K
Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

9.0K
Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.3K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Aging and disabilities often lead to reduced physical activity and independence.
  • Human Activity Recognition (HAR) systems, especially with Internet of Things (IoT) integration, offer solutions for monitoring and supporting individuals.
  • Existing HAR methods face challenges due to complex activity patterns and sensor configurations.

Purpose of the Study:

  • To propose an Intelligent Deep Learning Technique for Human Activity Recognition of Persons with Disabilities using Sensor Technology (IDLTHAR-PDST).
  • To leverage sensor technology within an IoT-Edge-Cloud framework for efficient activity recognition.
  • To improve the accuracy and reliability of HAR systems for individuals with disabilities.

Main Methods:

  • Data pre-processing using min-max normalization for sensor data optimization.
  • Feature subset selection employing the enhanced honey badger algorithm (EHBA) for dimensionality reduction.
  • Activity classification using a deep belief network (DBN) model within an IoT-Edge-Cloud continuum.

Main Results:

  • The IDLTHAR-PDST technique demonstrated superior performance in activity recognition.
  • Achieved a high accuracy rate of 98.75%, outperforming existing methods.
  • Validated the effectiveness of the proposed technique through comprehensive simulations.

Conclusions:

  • The IDLTHAR-PDST technique offers an effective solution for recognizing human activities using sensor technology.
  • The integration of deep learning, IoT, and advanced algorithms enhances HAR capabilities for persons with disabilities.
  • This approach supports independent living and well-being by accurately monitoring daily routines.