Real Time Localization of Assets in Hospitals using Quuppa Indoor Positioning Technology

Abstract

Indoor positioning is becoming a hot topic in public areas that are used by large numbers of people. Finding people and assets in buildings has become more difficult because of the complexity and scale of today’s public space. On the subject of locating assets in the hospital the following use case is defined for this project. Positioning and localization of assets in a hospital is useful to do for several reasons. Loss and theft of equipment takes a large expense of the hospital’s budget. When it is possible to have the position of a device in real time, a system could be developed that locates the assets through the hospital building. The main goal of this project is to develop a model for an indoor positioning system for localization of assets in a hospital. The indoor positioning technology developed by Quuppa forms the basis for this. Their indoor positioning solution consists of Bluetooth powered tags measured by monitors on the ceiling (locators). The hospital that is going to be involved in this project is the Rijnstate hospital, located in Arnhem. They provided the input necessary to define the requirements for the use case. Based on the requirements from the use case and the specifications of the positioning system six test cases were defined for analysis of the test data and development of the localization model. This MSc thesis describes a scientific approach to investigate the subject of indoor localization by performing data acquisition, processing and analysis of indoor position data. In order to localize the assets indoors, a map matching method is developed that takes into account several factors such as geometrical influences, characteristics of the positioning system and obstructions in the indoor environment. For matching the position data to a real world location, several location types are developed by subdividing the floor plan into location clusters. The research has shown that a sub-meter accuracy level can be achieved for locations that are within the high-resolution range of the locator. The performance for positioning at the smallest cluster levels can only be achieved when having a dense distribution of locators. Test cases that were defined for specific situations related to the hospital case show successful localization for the majority of the test data. A correction model for making coordinate adjustments of the position estimates is described based on the reliability of the data from the test cases.