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Dual Attention-Based recurrent neural network and Two-Tier optimization algorithm for human activity recognition in

Hend Khalid Alkahtani1, Gouse Pasha Mohammed2, Radwa Marzouk3,4

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Summary

This study introduces a new model for Human Activity Recognition (HAR) to aid disabled individuals. The Dual Attention-Based Two-Tier Metaheuristic Optimization Algorithm achieved 98.66% accuracy, significantly improving HAR performance.

Keywords:
DisabilitiesFeature selection, data normalizationHuman activity recognitionTwo-Tier metaheuristic optimization algorithm

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Human Activity Recognition (HAR) is crucial for applications like remote monitoring, healthcare, and security.
  • Existing HAR methods utilize diverse sensors and techniques, including wearable, object-tagged, and device-free approaches.
  • Deep learning (DL) and machine learning (ML) have shown significant promise in enhancing HAR accuracy.

Purpose of the Study:

  • To propose a novel Dual Attention-Based Two-Tier Metaheuristic Optimization Algorithm for Human Activity Recognition with Disabilities (DATTMOA-HARD).
  • To specifically improve HAR systems to better assist individuals with disabilities.
  • To enhance the accuracy and efficiency of human activity detection.

Main Methods:

  • The DATTMOA-HARD model employs Z-score normalization for data preprocessing.
  • Feature selection is performed using the binary firefly algorithm (BFA).
  • Classification is achieved through a dual attention bidirectional gated recurrent unit (DABiG) technique, with hyperparameters optimized by the Tasmanian devil optimizer (TDO).

Main Results:

  • The DATTMOA-HARD model demonstrated superior performance on the HAR dataset.
  • Experimental evaluation showed a high accuracy rate of 98.66%.
  • The proposed model significantly outperformed existing HAR methods in detection accuracy.

Conclusions:

  • The DATTMOA-HARD model offers a significant advancement in Human Activity Recognition, particularly for assisting disabled individuals.
  • The combination of dual attention mechanisms, metaheuristic optimization, and advanced DL techniques leads to highly accurate activity detection.
  • This research highlights the potential of sophisticated AI models in creating more inclusive and supportive technological solutions.