Real-World Stair Ambulation Characteristics Differ Between Prospective Fallers and Non-Fallers in Parkinson’s Disease

Roth N, Ullrich M, Küderle A, Gladow T, Marxreiter F, Gaßner H, Kluge F, Klucken J, Eskofier B (2022)

Publication Type: Journal article

Publication year: 2022

Journal

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

Pages Range: 1-9

DOI: 10.1109/JBHI.2022.3186766

Abstract

Falls are among the leading causes of injuries or death for individuals from the age of 65 and the prevalence of falls is especially high for patients suffering from neurological diseases like Parkinson’s disease (PD). Due to advancements in wearable sensor technology, inertial measurement units (IMUs) can be integrated unobtrusively into patients’ everyday lives to monitor various mobility and gait parameters, which are related to common risk factors like reduced balance and reduced lower-limb muscle strength, or lower range of joints. Although stair ambulation is a fundamental part of our daily lives and is known for its unique challenges for the gait and balance system, long-term gait analysis studies have not investigated real-world stair ambulation parameters yet. Therefore, we applied a recently published gait analysis pipeline on real-world foot-worn IMU data of 40 PD patients over a recording period of two weeks to extract objective gait parameters from level walking but also from stair ascending and stair descending gait. In combination with fall records from a prospective three-month follow-up phase, we investigated group differences in gait parameters of future fallers compared to non-fallers for each individual gait activity. We found significant differences in stair ascending and descending parameters. Stance time was increased by up to 20% and gait speed reduced by up to 16% for fallers compared to non-fallers during stair walking. These differences were not present in level walking parameters. Hence, these results suggest that real-world stair ambulation provides sensitive parameters for mobility and fall risk due to the unique challenges stairs add to the balance and control system. Our work complements existing gait analysis studies by adding new insights into mobility and gait performance during real-world gait.

Authors with CRIS profile

Nils Roth Machine Learning and Data Analytics Lab Martin Ullrich Machine Learning and Data Analytics Lab Arne Küderle Machine Learning and Data Analytics Lab Franz Marxreiter Department of Molecular Neurology Heiko Gaßner Department of Molecular Neurology Felix Kluge Machine Learning and Data Analytics Lab Björn Eskofier Machine Learning and Data Analytics Lab

Involved external institutions

Medical Valley - Europäische Metropolregion Nürnberg DE Germany (DE) University of Luxembourg LU Luxembourg (LU)

How to cite

APA:

Roth, N., Ullrich, M., Küderle, A., Gladow, T., Marxreiter, F., Gaßner, H.,... Eskofier, B. (2022). Real-World Stair Ambulation Characteristics Differ Between Prospective Fallers and Non-Fallers in Parkinson’s Disease. IEEE Journal of Biomedical and Health Informatics, 1-9. https://dx.doi.org/10.1109/JBHI.2022.3186766

MLA:

Roth, Nils, et al. "Real-World Stair Ambulation Characteristics Differ Between Prospective Fallers and Non-Fallers in Parkinson’s Disease." IEEE Journal of Biomedical and Health Informatics (2022): 1-9.

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