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Enhancing indoor activity recognition for disabled persons using multi head self attention recurrent neural network

Munya A Arasi1, Hanadi Alkhudhayr2, Abdulwhab Alkharashi3

  • 1Department of Computer Science, Applied College at RijalAlmaa, King Khalid University, Abha, Saudi Arabia. marasi@kku.edu.sa.

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|September 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an Improved Pelican Optimisation for Indoor Activity Recognition in Persons with Disabilities (IPOIAR-DPRNN) using deep learning. The method achieves 97.11% accuracy in detecting indoor activities for enhanced safety and well-being.

Keywords:
Adaptive bilateral filteringDisabled personIndoor activity detectionPelican optimization algorithmRecurrent neural network

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Indoor activity monitoring is crucial for the well-being and security of vulnerable populations like the elderly and visually impaired.
  • Deep learning (DL) models, particularly human activity recognition (HAR) techniques, offer advanced capabilities for precise indoor monitoring.
  • Current DL approaches require careful selection and optimization of architectures and parameters for effective indoor activity detection.

Purpose of the Study:

  • To enhance indoor activity detection systems for individuals with disabilities using a novel deep learning approach.
  • To improve the accuracy and reliability of recognizing human actions within indoor environments.
  • To introduce the Improved Pelican Optimisation for Indoor Activity Recognition in Persons with Disabilities (IPOIAR-DPRNN) method.

Main Methods:

  • Image pre-processing using adaptive bilateral filtering (ABF) to reduce image distortions.
  • Feature extraction utilizing the EfficientNetB7 model.
  • Activity detection and classification via bidirectional long short-term memory with multi-head self-attention (BiLSTM-MHSA).
  • Hyperparameter tuning of the BiLSTM-MHSA model using the improved pelican optimization algorithm (IPOA).

Main Results:

  • The IPOIAR-DPRNN method demonstrated superior performance in indoor activity recognition.
  • Achieved a high accuracy of 97.11% on the Florence 3D Actions dataset.
  • Outperformed existing techniques in detecting and classifying human activities in indoor settings.

Conclusions:

  • The proposed IPOIAR-DPRNN method significantly enhances indoor activity recognition for individuals with disabilities.
  • The integration of adaptive bilateral filtering, EfficientNetB7, BiLSTM-MHSA, and IPOA provides a robust solution.
  • This approach offers a promising advancement for improving safety, security, and personalized care in indoor environments.