Hybrid deep learning framework for human activity recognition

Document Type : Research Paper


1 Research Scholar, Dept. of ISE, Dr. Ambedkar Institute of Technology, Bengaluru, India

2 Dept. of CSE, Shri Krishna Institute of Technology, Bengaluru, India


The aim of the recognition in the human activity is to recognize the actions of the individuals using a set of observations and their environmental conditions. Since last two decades, the research on this Human Activity Recognition (HAR) has captured the attention of several computer science communities because of the strength to provide support to different applications and the connection to different fields of study such as, human-computer interaction, healthcare, monitoring, entertainment and education. There are many existing methods like deep learning which have been used to develop to recognize the different activities of the human, but couldn’t identify the sudden change of the activities in the human. This paper presents a method using the deep learning methods which can recognize the specific identities and identify a change from one activity to another for the applications of the healthcare. In this method, a deep convolutional neural network is built using which the features are extracted for the collection of the data from the sensors. After which the Gated Recurrent Unit (GRU) captures the long-tern dependency between the different actions which helps to improve the identification rate of the HAR. From the CNN and GRU, a model of wearable sensor can be proposed which can identify the changes of the activities and can accurately recognize these activities. Experiment have been conducted using open-source University of California (UCI) HAR dataset which composed of six different activity such as lying, standing, sitting, walking downstairs, walking upstairs and walking. The CNN-based model achieves a detection accuracy of 95.99% whereas the CNN-GRU model achieves a detection accuracy of 96.79% which is better than most existing HAR methods.


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Volume 13, Issue 1
March 2022
Pages 1225-1237
  • Receive Date: 19 June 2021
  • Revise Date: 15 August 2021
  • Accept Date: 02 September 2021