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A Novel Hybrid Deep Learning Model for Human Activity Recognition Based on Transitional Activities.

Saad Irfan1, Nadeem Anjum1, Nayyer Masood1

  • 1Department of Computer Science, Capital University of Science and Technology, Islamabad 44000, Pakistan.

Sensors (Basel, Switzerland)
|December 28, 2021
PubMed
Summary

This study introduces a hybrid deep learning approach for human activity recognition, accurately identifying both basic and transition activities. The novel method significantly improves classification accuracy, outperforming existing state-of-the-art techniques.

Keywords:
deep learninghuman activity recognitionhybrid modelstransition activities

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Human activity recognition (HAR) algorithms often overlook postural transitions due to their short duration.
  • Accurate HAR requires the inclusion of both basic and transition activities for robust performance.

Purpose of the Study:

  • To propose a hybrid multi-model deep learning approach for enhanced human activity recognition.
  • To incorporate both basic and transition activities into a unified recognition framework.

Main Methods:

  • Utilized multiple deep learning models simultaneously for activity recognition.
  • Implemented a dynamic decision fusion module for final classification.
  • Conducted experiments on publicly available datasets.

Main Results:

  • Achieved 96.11% classification accuracy for transition activities.
  • Achieved 98.38% classification accuracy for basic activities.
  • Demonstrated superior performance compared to state-of-the-art methods in accuracy and precision.

Conclusions:

  • The proposed hybrid approach effectively recognizes both basic and transition human activities.
  • Integrating transition activities significantly enhances the overall performance of HAR systems.
  • The dynamic decision fusion module contributes to the method's superior accuracy and precision.