A novel hybrid deep learning model for human activity recognition based on transitional activities

Saad Irfan, Nadeem Anjum*, Nayyer Masood, Ahmad S. Khattak, Naeem Ramzan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
11 Downloads (Pure)

Abstract

In recent years, a plethora of algorithms have been devised for efficient human activity recognition. Most of these algorithms consider basic human activities and neglect postural transitions because of their subsidiary occurrence and short duration. However, postural transitions assume a significant part in the enforcement of an activity recognition framework and cannot be neglected. This work proposes a hybrid multi-model activity recognition approach that employs basic and transition activities by utilizing multiple deep learning models simultaneously. For final classification, a dynamic decision fusion module is introduced. The experiments are performed on the publicly available datasets. The proposed approach achieved a classification accuracy of 96.11% and 98.38% for the transition and basic activities, respectively. The outcomes show that the proposed method is superior to the state-of-the-art methods in terms of accuracy and precision.

Original languageEnglish
Article number8227
Number of pages20
JournalSensors
Volume21
Issue number24
DOIs
Publication statusPublished - 9 Dec 2021

Keywords

  • deep learning
  • human activity recognition
  • hybrid models
  • transition activities

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