Online Updating Extended Belief Rule-Based System for Sensor-Based Activity Recognition

Long-Hao Yang, J. Liu, Ying-Ming Wang, CD Nugent, Luis Martinez

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
45 Downloads (Pure)


Sensor-based activity recognition (AR) is a core problem with the research domain of smart environments. It has, however, the potential to provide solutions to address the problems associated with the growing size and ageing profile of the global population. The work presented within this paper focuses on the extended belief rule-based system (EBRBS), which offered promising performance compared with popular benchmark AR models and exhibited a high robustness in the situation of sensor failure. Nevertheless, efficiency remains one of the major issues to be improved for determining and updating the extended belief rule base (EBRB) within the EBRBS. This is critical for further utilizing the EBRBS in AR situations within dynamic smart environments. An eigendecomposition-based sensor selection method is firstly proposed to select an effective subset of sensors and to also enable efficient implementation to facilitate online AR. A novel domain division-based rule generation method is also proposed to generate and update an EBRB efficiently when new sensor data are available or when some sensors should be included or excluded in the EBRB. The combination of these two methods leads to an enhanced EBRBS, called online updating EBRBS. Two datasets (in a balanced class situation) obtained from simulation and actual environments are studied to provide detailed experimental analysis as a preliminary study and basis to handle further the imbalanced situation of real AR. The experimental results demonstrate an enhanced performance of the online updating EBRBS compared with the original EBRBS and some benchmark AR models, in terms of efficiency and effectiveness.
Original languageEnglish
Article number115737
JournalExpert Systems with Applications
Early online date13 Aug 2021
Publication statusPublished (in print/issue) - 30 Dec 2021

Bibliographical note

Funding Information:
This research was supported by the National Natural Science Foundation of China (Nos. 72001043, 61773123, and 71872047), the Natural Science Foundation of Fujian Province, China (No. 2020 J05122), the Humanities and Social Science Foundation of the Ministry of Education, China (No. 20YJC630188), and the Social Science Foundation of Fujian Province, China (No. FJ2019C032).

Publisher Copyright:
© 2021 Elsevier Ltd


  • Activity recognition
  • Extended belief rule base
  • Feature selection
  • Online model updating
  • Smart environment


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