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      The extensibility of the plantar fascia influences the windlass mechanism during human running

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          Abstract

          The arch of the human foot is unique among hominins as it is compliant at ground contact but sufficiently stiff to enable push-off. These behaviours are partly facilitated by the ligamentous plantar fascia whose role is central to two mechanisms. The ideal windlass mechanism assumes that the plantar fascia has a nearly constant length to directly couple toe dorsiflexion with a change in arch shape. However, the plantar fascia also stretches and then shortens throughout gait as the arch-spring stores and releases elastic energy. We aimed to understand how the extensible plantar fascia could behave as an ideal windlass when it has been shown to strain throughout gait, potentially compromising the one-to-one coupling between toe arc length and arch length. We measured foot bone motion and plantar fascia elongation using high-speed X-ray during running. We discovered that toe plantarflexion delays plantar fascia stretching at foot strike, which probably modifies the distribution of the load through other arch tissues. Through a pure windlass effect in propulsion, a quasi-isometric plantar fascia's shortening is delayed to later in stance. The plantar fascia then shortens concurrently to the windlass mechanism, likely enhancing arch recoil at push-off.

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          Point set registration: coherent point drift.

          Point set registration is a key component in many computer vision tasks. The goal of point set registration is to assign correspondences between two sets of points and to recover the transformation that maps one point set to the other. Multiple factors, including an unknown nonrigid spatial transformation, large dimensionality of point set, noise, and outliers, make the point set registration a challenging problem. We introduce a probabilistic method, called the Coherent Point Drift (CPD) algorithm, for both rigid and nonrigid point set registration. We consider the alignment of two point sets as a probability density estimation problem. We fit the Gaussian mixture model (GMM) centroids (representing the first point set) to the data (the second point set) by maximizing the likelihood. We force the GMM centroids to move coherently as a group to preserve the topological structure of the point sets. In the rigid case, we impose the coherence constraint by reparameterization of GMM centroid locations with rigid parameters and derive a closed form solution of the maximization step of the EM algorithm in arbitrary dimensions. In the nonrigid case, we impose the coherence constraint by regularizing the displacement field and using the variational calculus to derive the optimal transformation. We also introduce a fast algorithm that reduces the method computation complexity to linear. We test the CPD algorithm for both rigid and nonrigid transformations in the presence of noise, outliers, and missing points, where CPD shows accurate results and outperforms current state-of-the-art methods.
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            The spring in the arch of the human foot.

            Large mammals, including humans, save much of the energy needed for running by means of elastic structures in their legs and feet. Kinetic and potential energy removed from the body in the first half of the stance phase is stored briefly as elastic strain energy and then returned in the second half by elastic recoil. Thus the animal runs in an analogous fashion to a rubber ball bouncing along. Among the elastic structures involved, the tendons of distal leg muscles have been shown to be important. Here we show that the elastic properties of the arch of the human foot are also important.
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              X-ray reconstruction of moving morphology (XROMM): precision, accuracy and applications in comparative biomechanics research.

              X-Ray Reconstruction of Moving Morphology (XROMM) comprises a set of 3D X-ray motion analysis techniques that merge motion data from in vivo X-ray videos with skeletal morphology data from bone scans into precise and accurate animations of 3D bones moving in 3D space. XROMM methods include: (1) manual alignment (registration) of bone models to video sequences, i.e., Scientific Rotoscoping; (2) computer vision-based autoregistration of bone models to biplanar X-ray videos; and (3) marker-based registration of bone models to biplanar X-ray videos. Here, we describe a novel set of X-ray hardware, software, and workflows for marker-based XROMM. Refurbished C-arm fluoroscopes retrofitted with high-speed video cameras offer a relatively inexpensive X-ray hardware solution for comparative biomechanics research. Precision for our biplanar C-arm hardware and analysis software, measured as the standard deviation of pairwise distances between 1 mm tantalum markers embedded in rigid objects, was found to be +/-0.046 mm under optimal conditions and +/-0.084 mm under actual in vivo recording conditions. Mean error in measurement of a known distance between two beads was within the 0.01 mm fabrication tolerance of the test object, and mean absolute error was 0.037 mm. Animating 3D bone models from sets of marker positions (XROMM animation) makes it possible to study skeletal kinematics in the context of detailed bone morphology. The biplanar fluoroscopy hardware and computational methods described here should make XROMM an accessible and useful addition to the available technologies for studying the form, function, and evolution of vertebrate animals.
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Proceedings of the Royal Society B: Biological Sciences
                Proc. R. Soc. B.
                The Royal Society
                0962-8452
                1471-2954
                January 27 2021
                January 20 2021
                January 27 2021
                : 288
                : 1943
                : 20202095
                Affiliations
                [1 ]Department of Mechanical and Materials Engineering, Queen's University, Kingston, Ontario, Canada
                [2 ]School of Human Movement and Nutrition Sciences, The University of Queensland, Brisbane, Queensland, Australia
                [3 ]Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
                [4 ]Department of Kinesiology, University of Rhode Island, Kingston, RI, USA
                Article
                10.1098/rspb.2020.2095
                33468002
                01858a62-842f-4583-949c-52bc639a52b8
                © 2021

                https://royalsociety.org/-/media/journals/author/Licence-to-Publish-20062019-final.pdf

                https://royalsociety.org/journals/ethics-policies/data-sharing-mining/

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