Fall Risk Prediction in Parkinson's Disease Using Real-World Inertial Sensor Gait Data.

Ullrich M, Roth N, Küderle A, Richer R, Gladow T, Gaßner H, Marxreiter F, Klucken J, Eskofier B, Kluge F (2023)

Publication Type: Journal article

Publication year: 2023

Journal

IEEE Journal of Biomedical and Health Informatics Institute of Electrical and Electronics Engineers (IEEE)

Book Volume: 27

Pages Range: 319-328

Journal Issue: 1

DOI: 10.1109/JBHI.2022.3215921

Abstract

Falls are an eminent risk for older adults and especially patients with neurodegenerative disorders, such as Parkinson's disease (PD). Recent advancements in wearable sensor technology and machine learning may provide a possibility for an individualized prediction of fall risk based on gait recordings from standardized gait tests or from unconstrained real-world scenarios. However, the most effective aggregation of continuous real-world data as well as the potential of unsupervised gait tests recorded over multiple days for fall risk prediction still need to be investigated. Therefore, we present a data set containing real-world gait and unsupervised 4x10-Meter-Walking-Tests of 40 PD patients, continuously recorded with foot-worn inertial sensors over a period of two weeks. In this prospective study, falls were self-reported during a three-month follow-up phase, serving as ground truth for fall risk prediction. The purpose of this study was to compare different data aggregation approaches and machine learning models for the prospective prediction of fall risk using gait parameters derived either from continuous real-world recordings or from unsupervised gait tests. The highest balanced accuracy of 74.0% (sensitivity: 60.0%, specificity: 88.0%) was achieved with a Random Forest Classifier applied to the real-world gait data when aggregating all walking bouts and days of each participant. Our findings suggest that fall risk can be predicted best by merging the entire two-week real-world gait data of a patient, outperforming the prediction using unsupervised gait tests (68.0% balanced accuracy) and contribute to an improved understanding of fall risk prediction.

Authors with CRIS profile

Martin Ullrich Machine Learning and Data Analytics Lab Nils Roth Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics) Arne Küderle Graduiertenzentrum der FAU Robert Richer Sonderforschungsbereich 1483/1 Empathokinästhetische Sensorik – Sensortechniken und Datenanalyseverfahren zur empathokinästhetischen Modellbildung und Zustandsbestimmung (EmpkinS) Till Gladow Department of Molecular Neurology Heiko Gaßner Department of Molecular Neurology Franz Marxreiter Department of Molecular Neurology Jochen Klucken Department of Molecular Neurology Björn Eskofier Machine Learning and Data Analytics Lab Felix Kluge Machine Learning and Data Analytics Lab

How to cite

APA:

Ullrich, M., Roth, N., Küderle, A., Richer, R., Gladow, T., Gaßner, H.,... Kluge, F. (2023). Fall Risk Prediction in Parkinson's Disease Using Real-World Inertial Sensor Gait Data. IEEE Journal of Biomedical and Health Informatics, 27(1), 319-328. https://doi.org/10.1109/JBHI.2022.3215921

MLA:

Ullrich, Martin, et al. "Fall Risk Prediction in Parkinson's Disease Using Real-World Inertial Sensor Gait Data." IEEE Journal of Biomedical and Health Informatics 27.1 (2023): 319-328.

BibTeX: Download