Validation of a sensor-based gait analysis system with a gold-standard motion capture system in patients with parkinson’s disease

Jakob V, Küderle A, Klucken J, Eskofier B, Winkler J, Winterholler M, Gaßner H, Kluge F (2021)

Publication Type: Journal article

Publication year: 2021

Journal

Sensors MDPI

Book Volume: 21

Article Number: 7680

Journal Issue: 22

DOI: 10.3390/s21227680

Abstract

Digital technologies provide the opportunity to analyze gait patterns in patients with Par-kinson’s Disease using wearable sensors in clinical settings and a home environment. Confirming the technical validity of inertial sensors with a 3D motion capture system is a necessary step for the clinical application of sensor-based gait analysis. Therefore, the objective of this study was to com-pare gait parameters measured by a mobile sensor-based gait analysis system and a motion capture system as the gold standard. Gait parameters of 37 patients were compared between both systems after performing a standardized 5 × 10 m walking test by reliability analysis using intra-class correlation and Bland–Altman plots. Additionally, gait parameters of an age-matched healthy control group (n = 14) were compared to the Parkinson cohort. Gait parameters representing bradykinesia and short steps showed excellent reliability (ICC > 0.96). Shuffling gait parameters reached ICC > 0.82. In a stridewise synchronization, no differences were observed for gait speed, stride length, stride time, relative stance and swing time (p > 0.05). In contrast, heel strike, toe off and toe clearance significantly differed between both systems (p < 0.01). Both gait analysis systems distinguish Parkinson patients from controls. Our results indicate that wearable sensors generate valid gait parameters compared to the motion capture system and can consequently be used for clinically relevant gait recordings in flexible environments.

Authors with CRIS profile

Arne Küderle Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics) Björn Eskofier Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics) Jürgen Winkler Professur für Molekulare Neurologie Heiko Gaßner Department of Molecular Neurology Felix Kluge Machine Learning and Data Analytics Lab

Additional Organisation(s)

Sonderforschungsbereich 1483/1 Empathokinästhetische Sensorik – Sensortechniken und Datenanalyseverfahren zur empathokinästhetischen Modellbildung und Zustandsbestimmung (EmpkinS)

Involved external institutions

Krankenhaus Rummelsberg DE Germany (DE) University of Luxembourg LU Luxembourg (LU)

How to cite

APA:

Jakob, V., Küderle, A., Klucken, J., Eskofier, B., Winkler, J., Winterholler, M.,... Kluge, F. (2021). Validation of a sensor-based gait analysis system with a gold-standard motion capture system in patients with parkinson’s disease. Sensors, 21(22). https://doi.org/10.3390/s21227680

MLA:

Jakob, Verena, et al. "Validation of a sensor-based gait analysis system with a gold-standard motion capture system in patients with parkinson’s disease." Sensors 21.22 (2021).

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