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Open AccessArticle

Clustering-Based Ensemble Learning for Activity Recognition in Smart Homes

School of Computing and Mathematics, University of Ulster, Jordanstown, Shore Road, Newtownabbey, Co. Antrim BT37 0QB, UK
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Sensors 2014, 14(7), 12285-12304; https://doi.org/10.3390/s140712285
Received: 14 April 2014 / Revised: 26 June 2014 / Accepted: 30 June 2014 / Published: 10 July 2014
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. View Full-Text
Keywords: activity recognition; classifier ensembles; clustering; smart homes activity recognition; classifier ensembles; clustering; smart homes
MDPI and ACS Style

Jurek, A.; Nugent, C.; Bi, Y.; Wu, S. Clustering-Based Ensemble Learning for Activity Recognition in Smart Homes. Sensors 2014, 14, 12285-12304.

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