By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Huiru Zheng ; Member, IEEE, School of Computing and Mathematics, University of Ulster, Jordanstown, BT37 0QB, Northern Ireland, U.K. e-mail: ; 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