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Sensors 2015, 15(5), 11953-11971; doi:10.3390/s150511953

User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm

Department of Electrical and Computer Engineering, Pusan National University, Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan 609-735, Korea
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Author to whom correspondence should be addressed.
Academic Editor: Antonio Puliafito
Received: 11 February 2015 / Accepted: 14 May 2015 / Published: 21 May 2015
(This article belongs to the Special Issue Sensors and Smart Cities)
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Abstract

This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things) based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen’s temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home. View Full-Text
Keywords: activity recognition; Allen’s temporal relations; anomaly prediction; neural network; pattern clustering; smart home activity recognition; Allen’s temporal relations; anomaly prediction; neural network; pattern clustering; smart home
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Bourobou, S.T.M.; Yoo, Y. User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm. Sensors 2015, 15, 11953-11971.

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