Abstract
As the average age of the urban population increases, cities must adapt to improve the quality of life of their citizens. The City4Age H2020 project is working on the early detection of the risks related to Mild Cognitive Impairment and Frailty and on providing meaningful interventions that prevent those risks. As part of the risk detection process we have developed a multilevel conceptual model that describes the user behaviour using actions, activities, intra-activity behaviour and inter-activity behaviour. Using that conceptual model we have created a deep learning architecture based on Long Short-Term Memory Networks that models the inter-activity behaviour. The presented architecture offers a probabilistic model that allows to predict the users next actions and to identify anomalous user behaviours.
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Acknowledgment
This work has been supported by the European Commission under the City4Age project grant agreement (number 689731). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research.
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Almeida, A., Azkune, G. (2017). Inter-activity Behaviour Modelling Using Long Short-Term Memory Networks. In: Ochoa, S., Singh, P., Bravo, J. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2017. Lecture Notes in Computer Science(), vol 10586. Springer, Cham. https://doi.org/10.1007/978-3-319-67585-5_41
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DOI: https://doi.org/10.1007/978-3-319-67585-5_41
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