Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-29154-8-9
Title: Mining emerging sequential patterns for activity recognition in body sensor networks
Authors: Gu, T.
Wang, L.
Chen, H.
Liu, G. 
Tao, X.
Lu, J.
Keywords: activity recognition
Body sensor networks
data mining
Issue Date: 2012
Source: Gu, T.,Wang, L.,Chen, H.,Liu, G.,Tao, X.,Lu, J. (2012). Mining emerging sequential patterns for activity recognition in body sensor networks. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST 73 LNICST : 102-113. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-29154-8-9
Abstract: Body Sensor Networks offer many applications in healthcare, well-being and entertainment. One of the emerging applications is recognizing activities of daily living. In this paper, we introduce a novel knowledge pattern named Emerging Sequential Pattern (ESP) - a sequential pattern that discovers significant class differences - to recognize both simple (i.e., sequential) and complex (i.e., interleaved and concurrent) activities. Based on ESPs, we build our complex activity models directly upon the sequential model to recognize both activity types. We conduct comprehensive empirical studies to evaluate and compare our solution with the state-of-the-art solutions. The results demonstrate that our approach achieves an overall accuracy of 91.89%, outperforming the existing solutions. © 2012 Springer-Verlag Berlin Heidelberg.
Source Title: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
URI: http://scholarbank.nus.edu.sg/handle/10635/78228
ISBN: 9783642291531
ISSN: 18678211
DOI: 10.1007/978-3-642-29154-8-9
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