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A NEW DESIGN METHOD FOR GENERATING SURROGATE KINEMATIC TRUSS ORTHOSES TO SUPPORT PATHOLOGICAL GAIT PATTERNS IN HUMAN MODEL SIMULATIONS

Published online by Cambridge University Press:  19 June 2023

Patrick Steck*
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg
David Scherb
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg
Johannes Mayer
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg
Michael Jäger
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg
Jörg Miehling
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg
Harald Völkl
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg
Sandro Wartzack
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg
*
Steck, Patrick, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany, steck@mfk.fau.de

Abstract

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With increasing life expectancy, the risk of diseases of the central nervous system, such as cancer, strokes, etc., also increases. Strokes often result in injury to the sciatic nerve, which is responsible for controlling the calf muscles (plantar and dorsal flexors). A so-called ankle joint orthosis (AFO) helps to support the pathological gait and to avoid foot drop during gait. Passive orthoses are of particular importance for research, as they do not require additional incoming energy from outside to the orthotic system. However, current passive orthoses are often not personalized. On the one hand, because they usually have only a temporary muscle-building function and, on the other hand, because the individual design process is computationally time consuming and thus expensive. This paper presents a possibility to pre-dimension and pre-design passive orthoses fast and cost-efficiently by reducing the complexity of the model based on volume-optimized truss elements. Therefor a traditional high calculation intensive design procedure is compared with the complexity reduced model to show its effectiviness and the similarity of the results.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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