A Health Prognosis Wearable System with Learning Capabilities Using NNs

The deployment of wearable health monitoring systems (WHMS) is expected to address several important healthcare-related issues such as increasing healthcare costs, the rising number of the elderly population and treatment of chronic conditions. However, most of the currently developed WHMS simply serve as ambulatory physiological data loggers and transmitters in order to make the recorded bio-signals remotely available for inspection from a supervising physician. In this paper we describe our efforts towards setting-up a WHMS prototype that is capable of providing individualized embedded decision/diagnosis support for round-the-clock remote health monitoring of people at risk. To realize this goal an ANN-based approach is adopted, whereby a supervised learning period is required in order to embed patient-specific medical knowledge into the system, which will then enable it to make more accurate and ¿safer¿ estimations about the user´s health condition.