Volume IV-4/W1
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W1, 105-110, 2016
https://doi.org/10.5194/isprs-annals-IV-4-W1-105-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W1, 105-110, 2016
https://doi.org/10.5194/isprs-annals-IV-4-W1-105-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  05 Sep 2016

05 Sep 2016

REAL TIME LOCALIZATION OF ASSETS IN HOSPITALS USING QUUPPA INDOOR POSITIONING TECHNOLOGY

M. F. S. van der Ham1, S. Zlatanova2, E. Verbree3, and R. Voûte4 M. F. S. van der Ham et al.
  • 1Delft University of Technology, Faculty of Architecture and the Built Environment, MSc Geomatics, Julianalaan 134, 2628 BL, Delft, the Netherlands
  • 2Delft University of Technology, Faculty of Architecture and the Built Environment, Department of Urbanism, 3D Geoinformation, Julianalaan 134, 2628 BL, Delft, the Netherlands
  • 3Delft University of Technology, Faculty of Architecture and the Built Environment, OTB - Research Institute for the Built Environment, GIS Technology, Julianalaan 134, 2628 BL, Delft, the Netherlands
  • 4CGI Nederland BV, Meander 901, P.O. Box 7015, 6801 HA Arnhem, the Netherlands

Keywords: Smart buildings, indoor localization, positioning, BLE, space subdivision, asset management

Abstract. At the most fundamental level, smart buildings deliver useful building services that make occupants productive. Smart asset management in hostipals starts with knowing the whereabouts of medical equipment. This paper investigates the subject of indoor localization of medical equipment in hospitals by defining functional spaces. In order to localize the assets indoors, a localization method is developed that takes into account several factors such as geometrical influences, characteristics of the Quuppa 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 high-performance level can be achieved for locations that are within the high-resolution range of the receiver. The performance at the smallest subspaces can only be achieved when having a dense distribution of receivers. Test cases that were defined for specific situations in the test-area show successful localization in these subspaces for the majority of the test data.