Categoria: 2016

Abstract: This paper proposes a better way to represent the architecture of LARIISA, an intelligent system for decision making in healthcare. The proposed representation weaves health and computational domains in a multidimensional architecture, which facilitates the visualization of specialized applications added to the LARIISA framework. New concepts as Big Data, Internet of Things and Linked Data are also introduced to the proposed architecture. Acquiring new data from different sources to the LARIISA database, and making use of it, will permit a more efficient decision-making process for the system.

Abstract: VITESSE is a low cost system to support users in two scenarios: home care and accidents (fainting, trampling, etc.). Initially, the system was based on the digital TV technology in scenarios of home care. Nowadays, the system adds new functions to support urgent and emergency care of individuals in mobility. In both cases, the key idea of VITESSE is to improve the time of consuming process, taking into account the real time and contextual information, in particular in the case of accidents of mobile users. Therefore, VITESSE is a context-aware system that makes use of the concept of Internet of Things (IoT) and ontologies in the process of generating inferences, increasing the efficiency of health care systems.

Abstract: The home care consists in a form of primary care performed by a lay caregiver, a specialist or a multidisciplinary team. This modality is applied in elderly people or patients in treatment of chronic disease who are not at risk of death. The aim of this work is to present a set of context-aware health applications in a prototype of software and hardware that will assist caregivers and/or patients in home care situations. For this, a Set-Top Box (STB) connected to a TV with access to the Internet is used as a way of user interaction, which may enter information about its current state. Furthermore, health sensors can be used to capture data continuously to feed the system. The raw data and information provided by the user are later used, allowing, then, an inference about the patient condition.