Detection of Gait From Continuous Inertial Sensor Data Using Harmonic Frequencies

Ullrich M, Küderle A, Hannink J, Del Din S, Gaßner H, Marxreiter F, Klucken J, Eskofier B, Kluge F (2020)

Publication Type: Journal article, Original article

Publication year: 2020

Journal

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

Book Volume: 24

Pages Range: 1869 - 1878

Journal Issue: 7

URI: https://www.mad.tf.fau.de/files/2020/11/2020_ullrich_gaitsequencedetection.pdf

DOI: 10.1109/JBHI.2020.2975361

Abstract

Mobile gait analysis using wearable inertial measurement units (IMUs) provides valuable insights for the assessment of movement impairments in different neurological and musculoskeletal diseases, for example Parkinson's disease (PD). The increase in data volume due to arising long-term monitoring requires valid, robust and efficient analysis pipelines. In many studies an upstream detection of gait is therefore applied. However, current methods do not provide a robust way to successfully reject non-gait signals. Therefore, we developed a novel algorithm for the detection of gait from continuous inertial data of sensors worn at the feet. The algorithm is focused not only on a high sensitivity but also a high specificity for gait. Sliding windows of IMU signals recorded from the feet of PD patients were processed in the frequency domain. Gait was detected if the frequency spectrum contained specific patterns of harmonic frequencies. The approach was trained and evaluated on 150 clinical measurements containing standardized gait and cyclic movement tests. The detection reached as sensitivity of 0.98 and a specificity of 0.96 for the best sensor configuration (angular rate around the medio-lateral axis). On an independent validation data set including 203 unsupervised, semi-standardized gait tests, the algorithm achieved a sensitivity of 0.97. Our algorithm for the detection of gait from continuous IMU signals works reliably and showed promising results for the application in the context of free-living and non-standardized monitoring scenarios.

Authors with CRIS profile

Martin Ullrich Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics) Arne Küderle Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics) Heiko Gaßner Department of Molecular Neurology Franz Marxreiter Department of Molecular Neurology Jochen Klucken Department of Molecular Neurology Björn Eskofier Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics) Felix Kluge Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics)

Additional Organisation(s)

Machine Learning and Data Analytics Lab

Related research project(s)

Connecting digital mobility assessment to clinical outcomes for regulatory and clinical endorsement (MOBILISE-D) April 1, 2019 - March 31, 2024 Fall risk detection for Parkinson's disease via intelligent gait analysis (FallRiskPD) Jan. 1, 2018 - Dec. 31, 2020

Involved external institutions

Newcastle University GB United Kingdom (GB)

How to cite

APA:

Ullrich, M., Küderle, A., Hannink, J., Del Din, S., Gaßner, H., Marxreiter, F.,... Kluge, F. (2020). Detection of Gait From Continuous Inertial Sensor Data Using Harmonic Frequencies. IEEE Journal of Biomedical and Health Informatics, 24(7), 1869 - 1878. https://doi.org/10.1109/JBHI.2020.2975361

MLA:

Ullrich, Martin, et al. "Detection of Gait From Continuous Inertial Sensor Data Using Harmonic Frequencies." IEEE Journal of Biomedical and Health Informatics 24.7 (2020): 1869 - 1878.

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