Journal article
Activity Discovery and Activity Recognition: A New Partnership
IEEE transactions on cybernetics, Vol.43(3), pp.820-828
06/2013
Handle:
https://hdl.handle.net/2376/103260
PMCID: PMC3772991
PMID: 23033328
Abstract
Activity recognition has received increasing attention from the machine learning community. Of particular interest is the ability to recognize activities in real time from streaming data, but this presents a number of challenges not faced by traditional offline approaches. Among these challenges is handling the large amount of data that does not belong to a predefined class. In this paper, we describe a method by which activity discovery can be used to identify behavioral patterns in observational data. Discovering patterns in the data that does not belong to a predefined class aids in understanding this data and segmenting it into learnable classes. We demonstrate that activity discovery not only sheds light on behavioral patterns, but it can also boost the performance of recognition algorithms. We introduce this partnership between activity discovery and online activity recognition in the context of the CASAS smart home project and validate our approach using CASAS datasets.
Metrics
7 Record Views
Details
- Title
- Activity Discovery and Activity Recognition: A New Partnership
- Creators
- Diane Cook - School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, 99163Narayanan Krishnan - School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, 99163Parisa Rashidi - Computer and Information Science and Engineering Department, University of Florida, Gainesville, FL, 32611
- Publication Details
- IEEE transactions on cybernetics, Vol.43(3), pp.820-828
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Grant note
- R01 EB015853 || EB / National Institute of Biomedical Imaging and Bioengineering : NIBIB
- Identifiers
- 99900546798501842
- Language
- English
- Resource Type
- Journal article