An approach for recommending personalized contents for homecare users in the context of health 2.0
Francisco Mendes Neto, Alisson Alan Lima da Costa, Ênio Lopes Sombra, Jonathan Darlan Cunegundes Moreira, J. Javier Samper, Ricardo Alexsandro de Medeiros Valentim, Rogério Patrício Chagas do Nascimento, Cecília Dias Flores · 2014
This paper proposes a content recommendation mechanism as part of a model for implementing ubiquitous learning for supporting people with chronic diseases who are treated at home, so that they can learn more about treatments for their disease. In the proposed approach, the learning takes place based on day-to-day activities and real situations. In this case, the model supports the development of tools that can learn about the user's context, based on data obtained via sensors installed on users or in their home, as well as data supplied directly by the user interface of their mobile devices, and data provided by the healthcare team, and, after that, recommend contents about their diseases.