Abstract
The surge of commodity devices, sensors and apps allows for the continuous monitoring of patient’s health status with relatively low-cost technology. Nonetheless, current solutions focus on presenting data and target at individual health metrics and not intelligent recommendations. In order to advance the state-of-the-art, there is a demand for models that correlate mobile sensor data, health parameters, and situational and/or social environment. We seek to improve current models by combining environmental monitoring, personal data collecting, and predictive analytics. For that, we introduce a middleware called Device Nimbus that provides the structures to integrate data from sensors in existing mobile computing technology. Moreover, it includes the algorithms for context inference and recommendation support. This development leads to innovative solutions in continuous health monitoring, based on recommendations contextualised in the situation and social environment. In this paper we propose a model, position it against state-of-the-art, and outline a proof-of-concept implementation.
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Oliveira, E.A., Koch, F., Kirley, M., dos Passos Barros, C.V.G. (2015). Towards a Middleware for Context-Aware Health Monitoring. In: Koch, F., Guttmann, C., Busquets, D. (eds) Advances in Social Computing and Multiagent Systems. MFSC 2015. Communications in Computer and Information Science, vol 541. Springer, Cham. https://doi.org/10.1007/978-3-319-24804-2_2
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DOI: https://doi.org/10.1007/978-3-319-24804-2_2
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