[u' @article{almeida_predicting_2018, title = {Predicting {Human} {Behaviour} with {Recurrent} {Neural} {Networks}}, volume = {8}, copyright = {http://creativecommons.org/licenses/by/3.0/}, url = {http://www.mdpi.com/2076-3417/8/2/305}, doi = {10.3390/app8020305}, 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 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\u2019s next actions and to identify anomalous user behaviours.}, language = {en}, number = {2}, urldate = {2018-02-23}, journal = {Applied Sciences}, author = {Almeida, Aitor and Azkune, Gorka}, month = feb, year = {2018}, keywords = {AI for health, Activity Recognition, Artificial Intelligence, City4Age, Deep Learning, Intelligent Environments, LSTM, Q2, behavior modelling, jcr2.217, long short-term memory networks, machine learning}, pages = {305}, } ']