Clustering-Based Ensemble Learning for Activity Recognition in Smart Homes

Anna Jurek, Chris Nugent, Yaxin Bi, Shengli Wu

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)
396 Downloads (Pure)

Abstract

Application of sensor-based technology within activity monitoring systems is becoming a popular technique within the smart environment paradigm. Nevertheless, the use of such an approach generates complex constructs of data, which subsequently requires the use of intricate activity recognition techniques to automatically infer the underlying activity. This paper explores a cluster-based ensemble method as a new solution for the purposes of activity recognition within smart environments. With this approach activities are modelled as collections of clusters built on different subsets of features. A classification process is performed by assigning a new instance to its closest cluster from each collection. Two different sensor data representations have been investigated, namely numeric and binary. Following the evaluation of the proposed methodology it has been demonstrated that the cluster-based ensemble method can be successfully applied as a viable option for activity recognition. Results following exposure to data collected from a range of activities indicated that the ensemble method had the ability to perform with accuracies of 94.2% and 97.5% for numeric and binary data, respectively. These results outperformed a range of single classifiers considered as benchmarks.
Original languageEnglish
Pages (from-to)12285-12304
Number of pages20
JournalIEEE Sensors Journal
Volume14
Issue number7
DOIs
Publication statusPublished - 10 Jul 2014

Bibliographical note

This article belongs to the Special Issue Select Papers from UCAmI & IWAAL 2013 - the 7th International Conference on Ubiquitous Computing and Ambient Intelligence & the 5th International Workshop on Ambient Assisted Living (UCAmI & IWAAL 2013: Pervasive Sensing Solutions)

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