Intelligent context-aware monitoring of hypertensive patients

Abstract

We present a decision-level data fusion technique for monitoring and reporting critical health conditions of a hypertensive patient at home. Variables associated to the patient (physiological and behavioral) and to the living environment are considered in the solution, contributing to improve the confidence on the system outputs. In the paper, we model the problem variables as fuzzy, aiming to capture their intrinsic essence, and draw rules based on medical recommendations to identify the health condition of the patient. This initiative move towards to build an abstract framework for context-aware telemonitoring applications. We also describe the relevant components of the framework and provide an initial evaluation of its decision component. Our results demonstrate that a principled choice of rules and variables may lead to a consistent identification of critical patient's conditions. Pervasive health care, context-awareness, home care, decision making I.

BDCaM: Big Data for Context-Aware Monitoring—A Personalized Knowledge Discovery Framework for Assisted Healthcare

“…The necessity of context-awareness in continuous BP monitoring was proven in some previous works [34], [54], [55] and encouraged us to evaluate the experiments of BP monitoring with large context data having massive variations. The effectiveness of context-aware monitoring for anomaly detection and disease prediction is also studied in some research works [24], [52] and our previous works [56], [57], [58].…”

Abordagem Atividade-Intensidade para o Reconhecimento de Atividades

“…Muitos trabalhos [bin Abdullah et al 2012] [Gjoreski, H. et al 2015] [Li 2009] já usam técnicas para reconhecimento de atividades das pessoas, principalmente, com o uso de algoritmos de aprendizado de máquina. Recentemente, algumas pesquisas têm direcionado para contextos específicos de saúde, como, por exemplo, envolvendo o autismo [Albinali 2009], reabilitação [Pärkkä 2009] e hipertensão [Copetti 2009]. Essa diversidade de áreas de aplicação na saúde faz com que a interação tenha que ser maior entre o conhecimento do especialista e o projetista do sistema.…”

Context-Aware Mobile Patient Monitoring Frameworks: A Systematic Review and Research Agenda

“…However, it was found that characterizing patients' medical situations, such as high BP, depends on patient context information such as vital signs and physical activities [8]. For example, the normal BP during sleeping is less than during running [32,33]. Therefore, identifying patient context based on context information enables effective characterization of the medical situation, hence, allowing MPMS to adapt to changes in a patient's medical situation.…”