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Related Concept Videos

Learning Disabilities01:25

Learning Disabilities

75
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...
75

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Related Experiment Video

Updated: May 17, 2025

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

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Advanced internet of things enhanced activity recognition for disability people using deep learning model with

Mohammed Maray1,2

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

Scientific Reports
|May 14, 2025
PubMed
Summary

This study introduces an Enhanced Activity Recognition for Disability People Using a Deep Learning Model and Nature-Inspired Optimization Algorithms (EARDP-DLMNOA) model. The proposed EARDP-DLMNOA achieved 97.58% accuracy in human activity recognition for disabled individuals.

Keywords:
Activity recognitionData normalizationDeep learningDisability peopleInternet of thingsNature-inspired optimization algorithms

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Human activity recognition (HAR) is crucial for applications like virtual reality, surveillance, and home monitoring.
  • Existing HAR models are being developed for disabled individuals, leveraging data from devices like smartphones and cameras.
  • HAR utilizes AI, deep learning, and machine learning to identify and classify human actions.

Purpose of the Study:

  • To propose an Enhanced Activity Recognition for Disability People Using a Deep Learning Model and Nature-Inspired Optimization Algorithms (EARDP-DLMNOA) model.
  • To improve HAR model performance through advanced optimization algorithms.
  • To enhance activity recognition specifically for disabled individuals.

Main Methods:

  • Data normalization using min-max normalization.
  • Feature subset selection via adaptive chimp optimization (AdCO).
  • Activity recognition using a deep convolutional auto-encoder (DCAE) with hyperparameter optimization by the zebra optimization algorithm (ZOA).

Main Results:

  • The EARDP-DLMNOA model demonstrated superior performance in human activity recognition.
  • Achieved a high accuracy of 97.58% on the HAR through the smartphone dataset.
  • Outperformed existing methods in experimental validation.

Conclusions:

  • The EARDP-DLMNOA model effectively enhances activity recognition for disabled individuals.
  • The integration of deep learning and nature-inspired optimization algorithms significantly improves HAR accuracy.
  • This approach offers a promising solution for advanced human activity recognition in assistive technologies.