A Novel Human Activity Recognition Model

Xinyi Zeng, Menghua Huang, Haiyang Zhang, Zhanlin Ji*, Ivan Ganchev*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

With the continuous improvement of the living standard, people have changed their concept of disease treatment to health management. However, most of the current health management software makes recommendations based on users' static information, with a low frequency of updates. The effect of targeted suggestions becomes weak with the passage of time, and it is hard for their recommendation effect to be satisfactory. Based on the use of smartphones for recognizing human activities on a real-time basis, a novel 'CNN+GRU' model is proposed in this paper†, utilizing both convolutional neural networks (CNNs) and gated recurrent units (GRUs). 'CNN+GRU' is able to extract the features in sensor data more accurately and improve the recognition speed. The proposed model was evaluated on a public data set, where it achieved an average accuracy of 91.27%, thus outperforming other models participating in the performance comparison experiments. In summary, the 'CNN+GRU' model can effectively recognize mobile users' activities based on the sensor data collected by their smartphones.

Original languageEnglish
Title of host publicationProceedings - 2023 8th International Conference on Mathematics and Computers in Sciences and Industry, MCSI 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages101-106
Number of pages6
ISBN (Electronic)9798350341652
DOIs
Publication statusPublished - 2023
Event8th International Conference on Mathematics and Computers in Sciences and Industry, MCSI 2023 - Athens, Greece
Duration: 14 Oct 202316 Oct 2023

Publication series

NameProceedings - 2023 8th International Conference on Mathematics and Computers in Sciences and Industry, MCSI 2023

Conference

Conference8th International Conference on Mathematics and Computers in Sciences and Industry, MCSI 2023
Country/TerritoryGreece
CityAthens
Period14/10/2316/10/23

Keywords

  • convolutional neural network (CNN)
  • feature extraction
  • gated recurrent unit (GRU)
  • health management
  • human activity recognition (HAR)
  • UCI data set

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