Please use this identifier to cite or link to this item:
https://doi.org/10.1109/PERCOM.2009.4912776
DC Field | Value | |
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dc.title | epSICAR: An emerging patterns based approach to sequential, interleaved and concurrent activity recognition | |
dc.contributor.author | Gu, T. | |
dc.contributor.author | Wu, Z. | |
dc.contributor.author | Tao, X. | |
dc.contributor.author | Pung, H.K. | |
dc.contributor.author | Lu, J. | |
dc.date.accessioned | 2013-07-04T07:54:12Z | |
dc.date.available | 2013-07-04T07:54:12Z | |
dc.date.issued | 2009 | |
dc.identifier.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. <a href="https://doi.org/10.1109/PERCOM.2009.4912776" target="_blank">https://doi.org/10.1109/PERCOM.2009.4912776</a> | |
dc.identifier.isbn | 9781424433049 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/39991 | |
dc.description.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. | |
dc.description.uri | http://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/PERCOM.2009.4912776 | |
dc.source | Scopus | |
dc.subject | Activity recognition | |
dc.subject | Emerging patterns | |
dc.subject | Interleaved and concurrent activities | |
dc.subject | Sequential | |
dc.subject | Wireless sensor networks | |
dc.type | Conference Paper | |
dc.contributor.department | COMPUTER SCIENCE | |
dc.description.doi | 10.1109/PERCOM.2009.4912776 | |
dc.description.sourcetitle | 7th Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2009 | |
dc.identifier.isiut | NOT_IN_WOS | |
Appears in Collections: | Staff Publications |
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