An Indoor Localization System for Nursing Homes Based on RSSI

Bachelor thesis (2022)

Authors

M.W. Boringa Electrical Engineering, Mathematics and Computer Science
J.J.M. van Erp Electrical Engineering, Mathematics and Computer Science
C.E.J.M. Ploumen Electrical Engineering, Mathematics and Computer Science

Contributors

P.J. French Bio-Electronics - EEMCS (supervisor 1)
I.E. Lager Electrical Engineering Education - EEMCS (coach)
J. Bastemeijer Electronic Instrumentation - EEMCS (supervisor 2)
D. Eldering (supervisor 2)
J.M.S. van Rijn (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Marijn Boringa, Job van Erp, Corné Ploumen
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Published Date

23-12-2022

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Over the years, care givers in nursing homes have seen their workload gradually increase. With no end of this trend in sight, the need for smart support systems increases. Especially systems which decrease the time spent on menial tasks are valued highly, because this frees more time for high quality and personal care.
To achieve this, Momo Medical is expanding on its nurse support system. This is a network of integrated smart solutions aiming at supporting care givers in nursing homes to provide better, faster and more personal care. The backbone of this nurse support system consists of the BedSenses, sensors which are placed under mattresses of each resident and can measure a variety of things.
This thesis describes the process of designing and creating a localization algorithm for this nurse support system. This algorithm can find residents by tracking the panic buttons they wear, so that in case of an emer- gency or whenever a care giver wants to know where a resident is located, they do not need to undertake a time consuming search in order to find them. These panic buttons send out an alive signal once every minute as well as a signal whenever the button is pressed. These signals are received by any nearby BedSenses. The algorithm looks at the signal strength with which each BedSense receives these signals and uses this to per- form localization.