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epSICAR: An Emerging Patterns based approach to sequential, interleaved and Concurrent Activity Recognition

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5 Author(s)
Tao Gu ; Inst. for Infocomm Res., Singapore ; Zhanqing Wu ; Xianping Tao ; Pung, H.K.
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Recognizing human activities from sensor readings has recently attracted much research interest in pervasive computing. 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 and concurrent) manner in real life. In this paper, we propose a novel emerging patterns based approach to sequential, interleaved and concurrent activity recognition (epSICAR). We exploit emerging patterns as powerful discriminators to differentiate activities. Different from other learning-based models built upon the training dataset for complex activities, we build our activity models by mining a set of emerging patterns from the sequential activity trace only and apply these models in recognizing sequential, interleaved and concurrent activities. We conduct our empirical studies in a real smart home, and the evaluation results demonstrate that with a time slice of 15 seconds, we achieve an accuracy of 90.96% for sequential activity, 87.98% for interleaved activity and 78.58% for concurrent activity.

Published in:

Pervasive Computing and Communications, 2009. PerCom 2009. IEEE International Conference on

Date of Conference:

9-13 March 2009