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Application of a Cluster-Based Classifier Ensemble to Activity Recognition in Smart Homes

Jurek, Anna, Bi, Yaxin, Nugent, Chris and Wu, S (2013) Application of a Cluster-Based Classifier Ensemble to Activity Recognition in Smart Homes. In: Ambient Assisted Living and Active Aging - 5th International Work-Conference, IWAAL 2013, Carrillo, Costa Rica, December 2-6, 2013. Lecture Notes in Computer Science 8277. 8 pp. [Conference contribution]

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An increasingly popular technique of monitoring activities within a smart environment involves the use of sensor technologies. With such an approach complex constructs of data are generated which subsequently require the use of activity recognition techniques to infer the underlying activity. The assignment of sensor data to one from a possible set of predefined activities can essentially be considered as a classification task. In this study, we propose the application of a cluster-based classifier ensemble method to the activity recognition problem, as an alternative to single classification models. Experimental evalua-tion has been conducted on publicly available sensor data collected over a period of 26 days from a single person apartment. Two types of sensor data representation have been considered, namely numeric and binary. The results show that the ensemble method can predict a numeric and binary representative activity with accuracies of 94.2% and 97.5%, respectively. These results outper-formed a range of single classifiers.

Item Type:Conference contribution (Paper)
Keywords:Activity recognition, classifier ensembles, smart homes
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute > Smart Environments
Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:33270
Deposited By: Dr Yaxin Bi
Deposited On:10 Feb 2016 14:00
Last Modified:10 Feb 2016 14:00

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