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

doi:10.1109/EMBC44109.2020.9176574

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

Institut für Neurotechnologie (7090)

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

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.

Originalsprache	englisch
Titel	42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
Untertitel	Enabling Innovative Technologies for Global Healthcare, EMBC 2020
Herausgeber (Verlag)	Institute of Electrical and Electronics Engineers
Seiten	808-811
Seitenumfang	4
ISBN (elektronisch)	9781728119908
DOIs	https://doi.org/10.1109/EMBC44109.2020.9176574
Publikationsstatus	Veröffentlicht - Juli 2020
Veranstaltung	42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: EMBC 2020 - Virtuell, Montreal, Kanada Dauer: 20 Juli 2020 → 24 Juli 2020

Publikationsreihe

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Band	2020-July
ISSN (Print)	1557-170X

Konferenz

Konferenz	42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
Kurztitel	EMBC 2020
Land/Gebiet	Kanada
Ort	Virtuell, Montreal
Zeitraum	20/07/20 → 24/07/20

ASJC Scopus subject areas

Signalverarbeitung
Biomedizintechnik
Maschinelles Sehen und Mustererkennung
Gesundheitsinformatik

UN SDGs

Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung

Zugriff auf Dokument

10.1109/EMBC44109.2020.9176574

Andere Dateien und Links

http://www.scopus.com/inward/record.url?scp=85091012902&partnerID=8YFLogxK

Dieses zitieren

Ziegl, A., Hayn, D., Kastner, P., Loffler, K., Weidinger, L., Brix, B., Goswami, N., & Schreier, G. (2020). Machine Learning Based Walking Aid Detection in Timed Up-and-Go Test Recordings of Elderly Patients. in 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020 (S. 808-811). Artikel 9176574 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Band 2020-July). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/EMBC44109.2020.9176574

Machine Learning Based Walking Aid Detection in Timed Up-and-Go Test Recordings of Elderly Patients. / Ziegl, Andreas; Hayn, Dieter; Kastner, Peter et al.
42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020. Institute of Electrical and Electronics Engineers, 2020. S. 808-811 9176574 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Band 2020-July).

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

Ziegl, A, Hayn, D, Kastner, P, Loffler, K, Weidinger, L, Brix, B, Goswami, N & Schreier, G 2020, Machine Learning Based Walking Aid Detection in Timed Up-and-Go Test Recordings of Elderly Patients. in 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020., 9176574, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Bd. 2020-July, Institute of Electrical and Electronics Engineers, S. 808-811, 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, Virtuell, Montreal, Quebec, Kanada, 20/07/20. https://doi.org/10.1109/EMBC44109.2020.9176574

Ziegl A, Hayn D, Kastner P, Loffler K, Weidinger L, Brix B et al. Machine Learning Based Walking Aid Detection in Timed Up-and-Go Test Recordings of Elderly Patients. in 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020. Institute of Electrical and Electronics Engineers. 2020. S. 808-811. 9176574. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC44109.2020.9176574

Ziegl, Andreas ; Hayn, Dieter ; Kastner, Peter et al. / Machine Learning Based Walking Aid Detection in Timed Up-and-Go Test Recordings of Elderly Patients. 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020. Institute of Electrical and Electronics Engineers, 2020. S. 808-811 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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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.",

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AU - Weidinger, Lisa

AU - Brix, Bianca

AU - Goswami, Nandu

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AB - 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.

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

Abstract

Publikationsreihe

Konferenz

ASJC Scopus subject areas

UN SDGs

Zugriff auf Dokument

Andere Dateien und Links

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