Predicting Human Behaviour with Recurrent Neural Networks
AbstractAs 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
Share & Cite This Article
Almeida, A.; Azkune, G. Predicting Human Behaviour with Recurrent Neural Networks. Appl. Sci. 2018, 8, 305.
Almeida A, Azkune G. Predicting Human Behaviour with Recurrent Neural Networks. Applied Sciences. 2018; 8(2):305.Chicago/Turabian Style
Almeida, Aitor; Azkune, Gorka. 2018. "Predicting Human Behaviour with Recurrent Neural Networks." Appl. Sci. 8, no. 2: 305.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.