Abstract
In the last decades, datasets have emerged as an essential component in the process of generating automated Activity Recognition (AR) solutions. Nevertheless, some challenges still remain: the lack of recommendations about which kind of information should be represented inside a dataset has resulted in the implementation of a variety of different non-standardized formalisms. On the other hand, this information is usually not sufficient to fully characterize the dataset. To address these challenges, this paper introduces a series of recommendations in the form of a dataset model with a well-defined semantic definition, for supporting those who are responsible for the creation, documentation and management of datasets. In addition, in order to better characterize datasets from a statistical point-of-view, we describe eight statistical analyses which should be included as additional measures within the dataset itself. We have validated our concepts through retrospectively analyzing a well-known dataset.
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© 2015 Springer International Publishing Switzerland
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Patara, F., Nugent, C.D., Vicario, E. (2015). Recommendations for the Creation of Datasets in Support of Data Driven Activity Recognition Models. In: Geissbühler, A., Demongeot, J., Mokhtari, M., Abdulrazak, B., Aloulou, H. (eds) Inclusive Smart Cities and e-Health. ICOST 2015. Lecture Notes in Computer Science(), vol 9102. Springer, Cham. https://doi.org/10.1007/978-3-319-19312-0_7
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DOI: https://doi.org/10.1007/978-3-319-19312-0_7
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