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.
Original language | English |
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Title of host publication | 2009 IEEE International Conference on Pervasive Computing and Communications (PerCom 2009) |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 1-9 |
Number of pages | 9 |
ISBN (Electronic) | 9781424433049 |
DOIs | |
Publication status | Published - 2009 |
Externally published | Yes |
Event | IEEE International Conference on Pervasive Computing and Communications (7th : 2009) - Galveston, United States Duration: 9 Mar 2009 → 13 Mar 2009 |
Conference
Conference | IEEE International Conference on Pervasive Computing and Communications (7th : 2009) |
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Abbreviated title | PerCom 2009 |
Country/Territory | United States |
City | Galveston |
Period | 9/03/09 → 13/03/09 |
Keywords
- activity recognition
- emerging patterns
- sequential
- interleaved and concurrent activities
- wireless sensor networks