A Cloud Based Architecture for Massive Sensor Data Analysis in Health Monitoring Systems

In recent analysis conducted by the European Commission it is estimated that within the EU population the number of the elderly (65≥) is growing from 17.4% of the total in 2010 up to 30.0% in 2060. At the same time, the EU population within the working age (15-64 years old) is expected to dramatically decrease from 61% to 51% of the total. These demographic changes impact on the public budgets, a decreasing number of health personnel, higher incidence of chronic diseases and growing demands and expectations from citizens for higher quality services and social care. In this scenario, ICT-solutions are necessary in order to reduce the cost of formal health care, to allow disease prevention and related lifestyle changes. Several research efforts are devoted to provide innovative and not-intrusive systems to continuously monitor in real-time the state and behavior of patients. Those health monitoring systems rely on heterogeneous data acquisition from sensors, video, historical and simulated data, performing inferences and data elaboration in order to provide alternatives to the traditional management of patients, e.g. allowing them to manage their health conditions at home. Depending on the functionalities to implement, the amount of data that has to be elaborated could represent the bottleneck of a monitoring system and it is critical in real-time applications. To achieve an increment on computational power, cloud computing in combination of hardware solutions should be adopted. In this work we present a layered architecture infrastructure for data analysis, based on two Decision Tree predictor hardware implementations. The first one is a high performance architecture, able to compute a massive analysis. The second one is a lightweight architecture suitable to execute prediction with few hardware resources.