DocumentCode :
1338839
Title :
A Pattern Mining Approach to Sensor-Based Human Activity Recognition
Author :
Gu, Tao ; Wang, Liang ; Wu, Zhanqing ; Tao, Xianping ; Lu, Jian
Author_Institution :
Dept. of Math. & Comput. Sci., Univ. of Southern Denmark, Odense, Denmark
Volume :
23
Issue :
9
fYear :
2011
Firstpage :
1359
Lastpage :
1372
Abstract :
Recognizing human activities from sensor readings has recently attracted much research interest in pervasive computing due to its potential in many applications, such as assistive living and healthcare. This task is particularly challenging because human activities are often performed in not only a simple (i.e., sequential), but also a complex (i.e., interleaved or concurrent) manner in real life. Little work has been done in addressing complex issues in such a situation. The existing models of interleaved and concurrent activities are typically learning-based. Such models lack of flexibility in real life because activities can be interleaved and performed concurrently in many different ways. In this paper, we propose a novel pattern mining approach to recognize sequential, interleaved, and concurrent activities in a unified framework. We exploit Emerging Pattern-a discriminative pattern that describes significant changes between classes of data-to identify sensor features for classifying activities. Different from existing learning-based approaches which require different training data sets for building activity models, our activity models are built upon the sequential activity trace only and can be applied to recognize both simple and complex activities. We conduct our empirical studies by collecting real-world traces, evaluating the performance of our algorithm, and comparing our algorithm with static and temporal models. Our results demonstrate that, with a time slice of 15 seconds, we achieve an accuracy of 90.96 percent for sequential activity, 88.1 percent for interleaved activity, and 82.53 percent for concurrent activity.
Keywords :
data mining; learning (artificial intelligence); pattern classification; sensors; ubiquitous computing; building activity model; concurrent activity; healthcare; human activity; learning based approach; pattern mining approach; pervasive computing; sensor based human activity recognition; temporal model; training data set; Correlation; Data mining; Hidden Markov models; Humans; Radiofrequency identification; Training; Human activity recognition; classifier design and evaluation.; emerging pattern; pattern analysis;
fLanguage :
English
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1041-4347
Type :
jour
DOI :
10.1109/TKDE.2010.184
Filename :
5590245
Link To Document :
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