Neural Network Ensembles for Sensor-Based Human Activity Recognition Within Smart Environments

Naomi Irvine, CD Nugent, Shuai Zhang, H. Wang, Wing Yin Ng

Research output: Contribution to journalArticlepeer-review

57 Citations (Scopus)
101 Downloads (Pure)


In this paper, we focus on data-driven approaches to human activity recognition (HAR). Data-driven approaches rely on good quality data during training, however, a shortage of high quality, large-scale, and accurately annotated HAR datasets exists for recognizing activities of daily living (ADLs) within smart environments. The contributions of this paper involve improving the quality of an openly available HAR dataset for the purpose of data-driven HAR and proposing a new ensemble of neural networks as a data-driven HAR classifier. Specifically, we propose a homogeneous ensemble neural network approach for the purpose of recognizing activities of daily living within a smart home setting. Four base models were generated and integrated using a support function fusion method which involved computing an output decision score for each base classifier. The contribution of this work also involved exploring several approaches to resolving conflicts between the base models. Experimental results demonstrated that distributing data at a class level greatly reduces the number of conflicts that occur between the base models, leading to an increased performance prior to the application of conflict resolution techniques. Overall, the best HAR performance of 80.39% was achieved through distributing data at a class level in conjunction with a conflict resolution approach, which involved calculating the difference between the highest and second highest predictions per conflicting model and awarding the final decision to the model with the highest differential value.
Original languageEnglish
Article number216
Pages (from-to)1-26
Number of pages26
Issue number1
Early online date30 Dec 2019
Publication statusPublished (in print/issue) - 1 Jan 2020

Bibliographical note

Funding Information:
Funding: This research was supported through a Northern Ireland Department for the Economy (DfE) PhD scholarship. The APC was funded through the DfE PhD scholarship.

Publisher Copyright:
© 2019 by the authors. Licensee MDPI, Basel, Switzerland.

Copyright 2020 Elsevier B.V., All rights reserved.


  • Ensemble neural networks
  • Human activity recognition
  • Model conflict resolution
  • Neural networks
  • Smart environments


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