Sensor-based activity recognition using extended belief rule-based inference methodology

A. Calzada, J. Liu, C.D. Nugent, Hui Wang, L. Martinez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

The recently developed extended belief rule-based inference methodology (RIMER+) recognizes the need of modeling different types of information and uncertainty that usually coexist in real environments. A home setting with sensors located in different rooms and on different appliances can be considered as a particularly relevant example of such an environment, which brings a range of challenges for sensor-based activity recognition. Although RIMER+ has been designed as a generic decision model that could be applied in a wide range of situations, this paper discusses how this methodology can be adapted to recognize human activities using binary sensors within smart environments. The evaluation of RIMER+ against other state-of-the-art classifiers in terms of accuracy, efficiency and applicability was found to be significantly relevant, specially in situations of input data incompleteness, and it demonstrates the potential of this methodology and underpins the basis to develop further research on the topic.
Original languageEnglish
Title of host publication2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Place of PublicationUnited States
Publisher IEEE
Pages2694-2697
Number of pages4
ISBN (Print)978-1-4244-7929-0
DOIs
Publication statusPublished - 06 Nov 2014
Externally publishedYes

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology Society: Proceedings
ISSN (Print)1094-687X
ISSN (Electronic)1558-4615

Bibliographical note

36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2014. ; Conference date: 06-11-2014

Keywords

  • belief rule base
  • activity recognition
  • decision support

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