Social Web for Large-Scale Biosensors

Abstract

Recent technological developments on mobile technologies associated with the growing computational capabilities of sensing enabled devices have given rise to mobile sensing systems that can target community level problems. These systems are capable of inferring intelligence from acquired raw sensed data, through the use of data mining and machine learning techniques. However, due to their recent advent, associated issues remain to be solved in a systematized way. Various areas can benefit from these initiatives, with public health systems having a major application gain. There has been interest in the use of social networks as a mean of epidemic prediction. Still, the integration between large-scale sensor networks and these initiatives, required to achieve seamless epidemic detection and prediction, is yet to be achieved. In this context, it is essential to review systems applied to epidemic prediction. This paper presents an application scenario for such predictions, namely fetus health monitoring in pregnant woman, presenting a new non-invasive portable alternative system that allows long-term pregnancy surveillance.