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|Title:||epSICAR: An emerging patterns based approach to sequential, interleaved and concurrent activity recognition||Authors:||Gu, T.
Interleaved and concurrent activities
Wireless sensor networks
|Issue Date:||2009||Citation:||Gu, T.,Wu, Z.,Tao, X.,Pung, H.K.,Lu, J. (2009). epSICAR: An emerging patterns based approach to sequential, interleaved and concurrent activity recognition. 7th Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2009. ScholarBank@NUS Repository. https://doi.org/10.1109/PERCOM.2009.4912776||Abstract:||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. © 2009 IEEE.||Source Title:||7th Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2009||URI:||http://scholarbank.nus.edu.sg/handle/10635/39991||ISBN:||9781424433049||DOI:||10.1109/PERCOM.2009.4912776|
|Appears in Collections:||Staff Publications|
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