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  • Kinetics of Lower Limb Prosthesis: Automated Detection of Vertical Loading Rate

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    Frossard267585-Published.pdf (1.943Mb)
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    Version of Record (VoR)
    Author(s)
    Frossard, Laurent
    Jones, Michael WM
    Stewart, Ian
    Leggat, Peter A
    Schuetz, Michael
    Langton, Christian
    Griffith University Author(s)
    Frossard, Laurent
    Langton, Christian
    Year published
    2019
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    Abstract
    Vertical loading rate could be associated with residuum and whole body injuries affecting individuals fitted with transtibial prostheses. The objective of this study was to outline one out of five automated methods of extraction of vertical loading rate that stacked up the best against manual detection, which is considered the gold standard during pseudo-prosthetic gait. The load applied on the long axis of the leg of three males was recorded using a transducer fitted between a prosthetic foot and physiotherapy boot while walking on a treadmill for circa 30 min. The automated method of extraction of vertical loading rate, ...
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    Vertical loading rate could be associated with residuum and whole body injuries affecting individuals fitted with transtibial prostheses. The objective of this study was to outline one out of five automated methods of extraction of vertical loading rate that stacked up the best against manual detection, which is considered the gold standard during pseudo-prosthetic gait. The load applied on the long axis of the leg of three males was recorded using a transducer fitted between a prosthetic foot and physiotherapy boot while walking on a treadmill for circa 30 min. The automated method of extraction of vertical loading rate, combining the lowest absolute average and range of 95% CI difference compared to the manual method, was deemed the most accurate and precise. The average slope of the loading rate detected manually over 150 strides was 5.56 ± 1.33 kN/s, while the other slopes ranged from 4.43 ± 0.98 kN/s to 6.52 ± 1.64 kN/s depending on the automated detection method. An original method proposed here, relying on progressive loading gradient-based automated extraction, produced the closest results (6%) to manual selection. This work contributes to continuous efforts made by providers of prosthetic and rehabilitation care to generate evidence informing reflective clinical decision-making
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    Journal Title
    Prosthesis
    Volume
    1
    Issue
    1
    DOI
    https://doi.org/10.3390/prosthesis1010004
    Copyright Statement
    © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
    Subject
    Sports science and exercise
    Publication URI
    http://hdl.handle.net/10072/390624
    Collection
    • Journal articles

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