Reducing the Uncertainty on Location Estimation of Mobile Users to Support Hospital Work

The nature of a context-aware application in hospital work demands a reliable and accurate location system. The activity for which this location information is needed determines to a great extent the relevancy of this contextual variable, since a minor error in delivering patient-based information can be critical. In this correspondence, we present an enhanced technique to infer the location of users in a hospital setting based on the strength of radio-frequency signals received by mobile devices that are used to train a neural network. The approach uses the neighbors surrounding the location to be estimated to track users continuously. This neighborhood eases the training and is used to simulate previous time instant guesses to reduce the location estimation error and alleviate the hopping trajectories of users. The results obtained by using this approach are in the order of 1.3 m for the average distance error during continuous motion.