Machine Learning Based Walking Aid Detection in Timed Up-and-Go Test Recordings of Elderly Patients

Andreas Ziegl, Dieter Hayn, Peter Kastner, Kerstin Loffler, Lisa Weidinger, Bianca Brix, Nandu Goswami, Gunter Schreier

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband

Abstract

Frailty and falls are the main causes of morbidity and disability in elderly people. The Timed Up-and-Go (TUG) test has been proposed as an appropriate method for evaluating elderly individuals' risk of falling. To analyze the TUG's potential for falls prediction, we conducted a clinical study with participants aged ≥ 65 years, living in nursing homes. We harvested 138 TUG recordings with the information, if patients used a walking aid or not and developed a method to predict the use of walking aids using a Random Forest Classifier for ultrasonic based TUG test recordings. We achieved a high accuracy with an Area Under the Curve (AUC) of 96,9% using a 20% leave out evaluation strategy. Automated collection of structured data from TUG recordings - like the use of a walking aid - may help to improve fall risk tools in future.

Originalspracheenglisch
Titel42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
UntertitelEnabling Innovative Technologies for Global Healthcare, EMBC 2020
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers
Seiten808-811
Seitenumfang4
ISBN (elektronisch)9781728119908
DOIs
PublikationsstatusVeröffentlicht - Jul 2020
Veranstaltung42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: EMBC 2020 - Virtuell, Montreal, Kanada
Dauer: 20 Jul 202024 Jul 2020

Publikationsreihe

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Band2020-July
ISSN (Print)1557-170X

Konferenz

Konferenz42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
KurztitelEMBC 2020
LandKanada
OrtVirtuell, Montreal
Zeitraum20/07/2024/07/20

ASJC Scopus subject areas

  • !!Signal Processing
  • !!Biomedical Engineering
  • !!Computer Vision and Pattern Recognition
  • !!Health Informatics

Fingerprint

Untersuchen Sie die Forschungsthemen von „Machine Learning Based Walking Aid Detection in Timed Up-and-Go Test Recordings of Elderly Patients“. Zusammen bilden sie einen einzigartigen Fingerprint.

Dieses zitieren