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’s next actions and to identify anomalous user behaviours.
Highlights
As a result of the growth of the urban population worldwide [1], cities are consolidating their position as one of the central structures in human organizations
The project aims to create an innovative framework of ICT tools and services that can be deployed by European cities in order to enhance the early detection of risk related to frailty and mild cognitive impairments (MCI), as well as to provide personalized interventions that can help the elderly population to improve their daily life by promoting positive behaviour changes
As part of the tools created for the framework, we have developed a series of algorithms for activity recognition and behaviour modelling
Summary
As a result of the growth of the urban population worldwide [1], cities are consolidating their position as one of the central structures in human organizations. The project aims to create an innovative framework of ICT tools and services that can be deployed by European cities in order to enhance the early detection of risk related to frailty and MCI, as well as to provide personalized interventions that can help the elderly population to improve their daily life by promoting positive behaviour changes. We present a conceptual classification of the user behaviour in intelligent environments according to the different levels of granularity used to describe it (Section 3). Using this classification, we describe the algorithm developed to automatically model the inter-activity behaviour (Section 3). Our evaluation (Section 4) analyses different architectures for the creation of a statistical model of user behaviour
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