Human Activity Recognition Using an Ensemble Learning Algorithm with Smartphone Sensor Data

Tan Hsu Tan, Jie Ying Wu, Shing Hong Liu, Munkhjargal Gochoo

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)


Human activity recognition (HAR) can monitor persons at risk of COVID‐19 virus infec-tion to manage their activity status. Currently, many people are isolated at home or quarantined in some specified places due to the spread of COVID‐19 virus all over the world. This situation raises the requirement of using the HAR to observe physical activity levels to assess physical and mental health. This study proposes an ensemble learning algorithm (ELA) to perform activity recognition using the signals recorded by smartphone sensors. The proposed ELA combines a gated recurrent unit (GRU), a convolutional neural network (CNN) stacked on the GRU and a deep neural network (DNN). The input samples of DNN were an extra feature vector consisting of 561 time‐domain and frequency‐domain parameters. The full connected DNN was used to fuse three models for the activity classification. The experimental results show that the precision, recall, F1‐score and accuracy achieved by the ELA are 96.8%, 96.8%, 96.8%, and 96.7%, respectively, which are superior to the existing schemes.

Original languageEnglish
Article number322
JournalElectronics (Switzerland)
Issue number3
Publication statusPublished - Feb 1 2022


  • Convolutional neural network
  • Ensemble learning algorithm
  • Gated recurrent units
  • Human activity recognition

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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