The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Activity Discovery and Activity Recognition: A New Partnership
Open accessAbstract
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 data sets.
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Cites
Activity recognition using cell phone accelerometers
Kwapisz, J. R., Weiss, G. M., & Moore, S. A. (2011) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1964897.1964918 · Cited by 2,912 · cited 2×
“Researchers have used phones to recognize gestures and motion patterns [11], [12].”
BackgroundWadley, V. G., Okonkwo, O., Crowe, M., & Ross-Meadows, L. A. (2008) · The American Journal of Geriatric Psychiatry · doi:10.1097/01.jgp.0000310780.04465.13 · Cited by 187
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Guo, K., Lu, Y., Gao, H., & Cao, R. (2018) · Sensors · doi:10.3390/s18051341 · Cited by 104
Human Activity Recognition Based on Dynamic Active Learning
Bi, H., Perello-Nieto, M., Santos-Rodriguez, R., & Flach, P. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3013403 · Cited by 81
Personalized Human Activity Recognition Based on Integrated Wearable Sensor and Transfer Learning
Fu, Z., He, X., Wang, E., Huo, J., Huang, J., & Wu, D. (2021) · Sensors · doi:10.3390/s21030885 · Cited by 79
An integrated system for voice command recognition and emergency detection based on audio signals
Principi, E., Squartini, S., Bonfigli, R., Ferroni, G., & Piazza, F. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.02.036 · Cited by 52
Activity-Aware Energy-Efficient Automation of Smart Buildings
Thomas, B., & Cook, D. (2016) · Energies · doi:10.3390/en9080624 · Cited by 42
Self-citation
Metadata
- Authors
- Cook, D. J., Krishnan, N. C., & Rashidi, P.
- Venue
- IEEE Transactions on Cybernetics
- Published
- 2013-06
- Volume
- 43
- Issue
- 3
- Pages
- 820-828
- ISSN
- 2168-2267 (ISSN-L)
- 2168-2275
- OpenAlex
- W1987522330
- Cited by
- 235
- References
- 2
- FWCI
- 16.72
- Type
- journal article
- Language
- en
- Open access
- green
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- Cook, D. J., Krishnan, N. C., & Rashidi, P. (2013). Activity Discovery and Activity Recognition: A New Partnership. IEEE Transactions on Cybernetics. https://doi.org/10.1109/tsmcb.2012.2216873
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