Fall Detection and Posture Tracking

Indoor Positioning and Fall Detection System Without Wearables

Bachelor thesis (2023)

Authors

C.T. Kopar Electrical Engineering, Mathematics and Computer Science
D.J. Rugge Electrical Engineering, Mathematics and Computer Science
M. Rozema Electrical Engineering, Mathematics and Computer Science

Contributors

K. Rassels Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering (mentor)
P.J. French Bio-Electronics - EEMCS (mentor)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Cahit Tolga Kopar, Daniël Rugge, Mischa Rozema
Fall detection Eldery care Posture tracking Pressure injury risk
More Info
expand_more

Published Date

27-06-2023

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

In the past decades, the workload and the pressure on medical personnel has been growing to an unprecedented peak. This is partly due to the ageing population and the increasing capabilities to be independent at an older age, increasing the age people enter nursing homes. This paper focuses on a novel way to detect incidents that could occur in the daily life of the elderly. Unlike most systems already proposed by others, there will be no use of wearable positioning sensors and the system is implemented on an Single Board Computer (SBC). This thesis report is one of a set of two reports discussing the final implementation of the system.