Ambient Intelligence Model for Monitoring, Alerting and Adaptively Recommending Patient’s Health-Care Agenda Based on User Profile

Abstract

Currently, healthcare is a crucial issue for the entire population, especially for individuals who suffer from a chronic disease such as hypertension or diabetes. However, this care is carried out in medical centers, limiting the scope of health professionals. In fact, some monitoring, early warning processes, and health supporting that are not presently performed, could be carried out at the patient’s location. The aim of this paper is to integrate WSN, ambient intelligence, multi-agent systems, and ontologies, in order to develop an ambient intelligence model that provides alerts, personalized recommendations, and adaptive health-care agendas. Personalized agendas based on chronic patient profiles offer appropriate physical activity, personalized food diet, and specific activities in order to control stress levels. For the validation of the proposed model, a prototype was constructed and applied to a case study considering several chronic patients. The results demonstrate the effectiveness of the proposed health-care ambient intelligence multi-agent model.