On uncertainty determination in eHealth sensors

A remote health infrastructure is proposed based on emerging technologies including e-health sensors and cloud. The challenge is to collect accurate health data for the use of remote diagnosis by the doctors in spite of the presence of uncertainty due to physical sensors (PS). Virtual sensors (VS) provide a layer of abstraction over the PS layer. The “possibly erroneous” data from PSs can be filtered by adding a level of intelligence to VS. In this paper, we introduce algorithms for uncertainty reduction by virtual sensing techniques in remote health care, by approximating errors using physicians perception and fuzzy system modeling (FSM).