Abstract
The advances in electronic devices have entailed the development of smart environments which have the aim to help and make easy the life of their inhabitants. In this kind of environments, an important task is the process of activity recognition of an inhabitant in the environment in order to anticipate the occupant necessities and to adapt such smart environment. Due to the cost to checking activity recognition approaches in real environments, usually, they use datasets generated from smart environments. Although there are many datasets for activity recognition in smart environments, it is difficult to find single, interleaved or multioccupancy activity datasets, or combinations of these classes of activities according to the researchers’ needs. In this work, the design and development of a complete dataset with 14 sensors and 9 different activities daily living is described, being this dataset divided into partitions with different classes of activities.
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http://ailab.wsu.edu/casas/datasets/ (last checked on August 27, 2015).
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www.tynetec.co.uk (last checked on August 27, 2015).
- 3.
http://ceatic.ujaen.es/smartlab (last checked on August 27, 2015).
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Acknowledgments
This contribution was supported by Research Projects TIN-2012-31263, CEATIC-2013-001, UJA2014/06/14 and by the Doctoral School of the University of Jaén. Invest Northern Ireland is acknowledge for partially supporting this project under the Competence Centre Program Grant RD0513853 - Connected Health Innovation Centre.
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Quesada, F.J., Moya, F., Medina, J., Martínez, L., Nugent, C., Espinilla, M. (2015). Generation of a Partitioned Dataset with Single, Interleave and Multioccupancy Daily Living Activities. In: García-Chamizo, J., Fortino, G., Ochoa, S. (eds) Ubiquitous Computing and Ambient Intelligence. Sensing, Processing, and Using Environmental Information. UCAmI 2015. Lecture Notes in Computer Science(), vol 9454. Springer, Cham. https://doi.org/10.1007/978-3-319-26401-1_6
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DOI: https://doi.org/10.1007/978-3-319-26401-1_6
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