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Appl. Sci. 2018, 8(2), 305;

Predicting Human Behaviour with Recurrent Neural Networks

DeustoTech-Deusto Foundation, University of Deusto, Av. Universidades 24, 48007 Bilbao, Spain
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Received: 15 December 2017 / Revised: 25 January 2018 / Accepted: 9 February 2018 / Published: 20 February 2018
(This article belongs to the Special Issue Socio-Cognitive and Affective Computing)
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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 these risks. As part of the risk detection process, we have developed a multilevel conceptual model that describes the user behaviour using actions, activities, and intra- and inter-activity behaviour. Using this conceptual model, we have created a deep learning architecture based on long short-term memory networks (LSTMs) that models the inter-activity behaviour. The presented architecture offers a probabilistic model that allows us to predict the user’s next actions and to identify anomalous user behaviours. View Full-Text
Keywords: long short-term memory networks; behavior modelling; intelligent environments; activity recognition long short-term memory networks; behavior modelling; intelligent environments; activity recognition

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Almeida, A.; Azkune, G. Predicting Human Behaviour with Recurrent Neural Networks. Appl. Sci. 2018, 8, 305.

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