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        Instrumented gait analysis in osteoarthritis: From lab towards ambulatory systems

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        Author(s)
        Kluge, Felix
        Language
        English
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        Abstract
        Knee osteoarthritis is a progressive disease characterized by the degeneration of articular cartilage of the knee joint. To understand the causes and effects of pathological gait symptoms and to optimize intervention management, refined methods for the quantitative assessment of gait and its impairment are necessary. However, objective functional gait measures are commonly not assessed in clinical practice. This thesis presents the application of state-of-the-art methods of human gait analysis using infrared cinematography to investigate the fundamentals of gait and the effect of non-invasive interventions in osteoarthritis. Although cinematography is considered the gold standard in movement analysis, its use for gait assessment in clinical routine is limited due to high costs and operational efforts. Therefore, a mobile sensorbased system was validated and then applied to a population of osteoarthritis patients undergoing total knee arthroplasty. The system achieved high discriminative ability and a highly accurate prediction of whether patients‘ gait improved or aggravated after surgery was possible based on pre-operative gait parameters. The findings are beneficial for the domain of intervention research and for the integration of objective and ambulatory gait analysis into clinical practice.
        URI
        https://library.oapen.org/handle/20.500.12657/109164
        Keywords
        Motion Capturing; Maschinelles Lernen; Bewegungsstörung; Beschleunigungssensor; Gelenkkrankheit; Ganganalyse
        DOI
        10.25593/978-3-96147-179-9
        ISBN
        9783961471799, 9783961471799, 9783961471782
        Publisher
        FAU University Press
        Publisher website
        https://www.university-press.fau.de/
        Publication date and place
        Erlangen, 2019
        Series
        FAU Studien aus der Informatik, 7
        Classification
        Biomechanics, human kinetics
        Machine learning
        Pages
        241
        Rights
        https://creativecommons.org/licenses/by/4.0/
        • Imported or submitted locally

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        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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