1 Department of Mathematics and Computer Science (IMADA), Faculty of Science, SDU2 unknown3 Department of Mathematics and Computer Science (IMADA), Faculty of Science, SDU
Body Sensor Networks oer many applications in healthcare, well-being and entertainment. One of the emerging applications is recognizing activities of daily living. In this paper, we introduce a novel knowledge pattern named Emerging Sequential Pattern (ESP)|a sequential pattern that discovers signicant class dierences|to recognize both simple (i.e., sequential) and complex (i.e., interleaved and concurrent) activities. Based on ESPs, we build our complex activity models directly upon the sequential model to recognize both activity types. We conduct comprehensive empirical studies to evaluate and compare our solution with the state-of-the-art solutions. The results demonstrate that our approach achieves an overall accuracy of 91.89%, outperforming the existing solutions.
In Proc. of the 7th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (mobiquitous 2010), Sydney, Asustralia, December 6-9, 2010, 2010
Main Research Area:
The Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (ICST)