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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...
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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.
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Related Experiment Video

Updated: Sep 11, 2025

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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

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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.