IDEAS home Printed from https://ideas.repec.org/a/taf/gcmbxx/v19y2016i9p954-963.html
   My bibliography  Save this article

A superellipsoid-plane model for simulating foot-ground contact during human gait

Author

Listed:
  • D. S. Lopes
  • R. R. Neptune
  • J. A. Ambrósio
  • M. T. Silva

Abstract

Musculoskeletal models and forward dynamics simulations of human movement often include foot–ground interactions, with the foot–ground contact forces often determined using a constitutive model that depends on material properties and contact kinematics. When using soft constraints to model the foot–ground interactions, the kinematics of the minimum distance between the foot and planar ground needs to be computed. Due to their geometric simplicity, a considerable number of studies have used point–plane elements to represent these interacting bodies, but few studies have provided comparisons between point contact elements and other geometrically based analytical solutions. The objective of this work was to develop a more general-purpose superellipsoid–plane contact model that can be used to determine the three-dimensional foot–ground contact forces. As an example application, the model was used in a forward dynamics simulation of human walking. Simulation results and execution times were compared with a point-like viscoelastic contact model. Both models produced realistic ground reaction forces and kinematics with similar computational efficiency. However, solving the equations of motion with the surface contact model was found to be more efficient (~18% faster), and on average numerically ~37% less stiff. The superellipsoid–plane elements are also more versatile than point-like elements in that they allow for volumetric contact during three-dimensional motions (e.g. rotating, rolling, and sliding). In addition, the superellipsoid–plane element is geometrically accurate and easily integrated within multibody simulation code. These advantages make the use of superellipsoid–plane contact models in musculoskeletal simulations an appealing alternative to point-like elements.

Suggested Citation

  • D. S. Lopes & R. R. Neptune & J. A. Ambrósio & M. T. Silva, 2016. "A superellipsoid-plane model for simulating foot-ground contact during human gait," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 19(9), pages 954-963, July.
  • Handle: RePEc:taf:gcmbxx:v:19:y:2016:i:9:p:954-963
    DOI: 10.1080/10255842.2015.1081181
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/10255842.2015.1081181
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/10255842.2015.1081181?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99.
    2. Tim Dorn & Yi-Chung Lin & Marcus Pandy, 2012. "Estimates of muscle function in human gait depend on how foot-ground contact is modelled," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 15(6), pages 657-668.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Thomas Baudin & Robert Stelter, 2022. "The rural exodus and the rise of Europe," Journal of Economic Growth, Springer, vol. 27(3), pages 365-414, September.
    2. Luca Benati & Paolo Surico, 2009. "VAR Analysis and the Great Moderation," American Economic Review, American Economic Association, vol. 99(4), pages 1636-1652, September.
    3. Asgharian, Hossein & Hess, Wolfgang & Liu, Lu, 2013. "A spatial analysis of international stock market linkages," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 4738-4754.
    4. Luca Benati & Paolo Surico, 2008. "Evolving U.S. Monetary Policy and The Decline of Inflation Predictability," Journal of the European Economic Association, MIT Press, vol. 6(2-3), pages 634-646, 04-05.
    5. John M. Abowd & Francis Kramarz & Sébastien Pérez-Duarte & Ian M. Schmutte, 2018. "Sorting Between and Within Industries: A Testable Model of Assortative Matching," Annals of Economics and Statistics, GENES, issue 129, pages 1-32.
    6. Jason Matthew DeBacker, 2015. "Flip‐Flopping: Ideological Adjustment Costs In The United States Senate," Economic Inquiry, Western Economic Association International, vol. 53(1), pages 108-128, January.
    7. Luca Benati & Pierpaolo Benigno, 2023. "Gibson s Paradox and the Natural Rate of Interest," Diskussionsschriften dp2303, Universitaet Bern, Departement Volkswirtschaft.
    8. Haan, Peter & Prowse, Victoria L., 2010. "The Design of Unemployment Transfers: Evidence from a Dynamic Structural Life-Cycle Model," IZA Discussion Papers 4792, Institute of Labor Economics (IZA).
    9. Dufour, Jean-Marie, 2006. "Monte Carlo tests with nuisance parameters: A general approach to finite-sample inference and nonstandard asymptotics," Journal of Econometrics, Elsevier, vol. 133(2), pages 443-477, August.
    10. Green, Rikard & Larsson, Karl & Lunina, Veronika & Nilsson, Birger, 2018. "Cross-commodity news transmission and volatility spillovers in the German energy markets," Journal of Banking & Finance, Elsevier, vol. 95(C), pages 231-243.
    11. Kapetanios, George & Marcellino, Massimiliano & Papailias, Fotis, 2016. "Forecasting inflation and GDP growth using heuristic optimisation of information criteria and variable reduction methods," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 369-382.
    12. Roman Sustek, 2011. "Monetary Business Cycle Accounting," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 14(4), pages 592-612, October.
    13. Jeffrey M. Wooldridge, 2002. "Inverse probability weighted M-estimators for sample selection, attrition and stratification," CeMMAP working papers 11/02, Institute for Fiscal Studies.
    14. Martin Andreasen, 2010. "How to Maximize the Likelihood Function for a DSGE Model," Computational Economics, Springer;Society for Computational Economics, vol. 35(2), pages 127-154, February.
    15. Max Jerrell, 2000. "Applications Of Public Global Optimization Software To Difficult Econometric Functions," Computing in Economics and Finance 2000 161, Society for Computational Economics.
    16. Robert G. King & Alexander Wolman & Michael Dotsey, 2009. "Inflation and Real Activity with Firm Level Productivity Shocks," 2009 Meeting Papers 367, Society for Economic Dynamics.
    17. Terasvirta, Timo, 2006. "Forecasting economic variables with nonlinear models," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 8, pages 413-457, Elsevier.
    18. Deb, Partha & Trivedi, Pravin K., 2002. "The structure of demand for health care: latent class versus two-part models," Journal of Health Economics, Elsevier, vol. 21(4), pages 601-625, July.
    19. Catherine Kyrtsou & Michel Terraza, 2003. "Is it Possible to Study Chaotic and ARCH Behaviour Jointly? Application of a Noisy Mackey–Glass Equation with Heteroskedastic Errors to the Paris Stock Exchange Returns Series," Computational Economics, Springer;Society for Computational Economics, vol. 21(3), pages 257-276, June.
    20. Pudney, Stephen, 2011. "Perception and retrospection: The dynamic consistency of responses to survey questions on wellbeing," Journal of Public Economics, Elsevier, vol. 95(3), pages 300-310.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:gcmbxx:v:19:y:2016:i:9:p:954-963. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/gcmb .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.