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Human Activity Detection in Smart Home Environment with Self-Adaptive Neural Networks

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3 Author(s)
Huiru Zheng ; Member, IEEE, School of Computing and Mathematics, University of Ulster, Jordanstown, BT37 0QB, Northern Ireland, U.K. e-mail: h.zheng@ulster.ac.uk ; Haiying Wang ; Norman Black

One of key components in the development of smart home technology is the detection and recognition of activities of daily life. Based on a self-adaptive neural network called growing self-organizing maps (GSOM), this paper presents a new computational approach to cluster analysis of human activities of daily living within smart home environment. It was tested on a dataset collected from a set of simple state-change sensors installed on a one-bedroom apartment during a period of about two weeks. The results obtained indicate that, due to its advanced evolving, self- adaptive properties, the GSOM exhibits several appealing features in the analysis of useful patterns encoded in daily activity data. The approaches described in this paper contribute to the development of a user-friendly and interactive data-mining platform for the analysis of human activities within smart home environment through the improvement of pattern discovery, visualization and interpretation.

Published in:

Networking, Sensing and Control, 2008. ICNSC 2008. IEEE International Conference on

Date of Conference:

6-8 April 2008