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Related Experiment Video

Updated: May 10, 2026

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

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Advanced smart human activity recognition system for disabled people using artificial intelligence with snake

Manal Abdullah Alohali1, Mohammed Yahya Alzahrani2, Asmaa Mansour Alghamdi3

  • 1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia. maalohaly@pnu.edu.sa.

Scientific Reports
|August 26, 2025
PubMed
Summary
This summary is machine-generated.

This study presents an Advanced Smart Human Activity Recognition for Disabled People Using Deep Learning with a Snake Optimiser (AHARDP-DLSO) approach. The AHARDP-DLSO model achieves 95.81% accuracy in recognizing daily activities for individuals with disabilities.

Keywords:
Deep belief networkDeep learningDisabled peopleHuman activity recognitionSnake optimiser algorithm

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Area of Science:

  • Artificial Intelligence
  • Biomedical Engineering
  • Computer Science

Background:

  • Human Activity Recognition (HAR) is crucial for elderly care and intelligent homes.
  • Aging decreases physical activity and daily task performance, impacting health.
  • Limited research focuses on HAR for older adults and individuals with disabilities.

Purpose of the Study:

  • To introduce an Advanced Smart Human Activity Recognition for Disabled People Using Deep Learning with a Snake Optimiser (AHARDP-DLSO) approach.
  • To develop an efficient deep learning-based HAR model for detecting and classifying daily activities of disabled individuals.
  • To achieve high precision and adaptability in HAR for the target population.

Main Methods:

  • Data normalization using min-max scaling.
  • Classification using a deep belief network (DBN).
  • Hyperparameter optimization of DBN with the snake optimizer algorithm (SOA).

Main Results:

  • The AHARDP-DLSO model demonstrated superior performance on the WISDM dataset.
  • Achieved a high accuracy of 95.81% in activity recognition.
  • Outperformed existing HAR models in experimental validation.

Conclusions:

  • The AHARDP-DLSO approach offers an effective solution for HAR in disabled individuals.
  • Deep learning combined with snake optimizer shows promise for specialized HAR applications.
  • The model provides a foundation for enhanced assistive technologies for people with disabilities.