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Intelligent Autonomous Systems 13 pp 1185–1197Cite as

Human Muscle-Tendon Stiffness Estimation During Normal Gait Cycle Based on Gaussian Mixture Model

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 302))

Abstract

The aim of this study is to estimate the stiffness of the muscle-tendon unit, of human lower limb, during the execution of a normal gait cycle. Unlike the analytical techniques already widely validated in literature and discussed below, a probabilistic approach based on the Gaussian Mixture Model (GMM) has been adopted here for the computation of the muscle-tendon unit stiffness. The obtained results for the major muscle groups are shown. The effectiveness of the proposed approach has been evaluated by computing the Root Mean Square (RMS) error between the stiffness calculated analytically and those calculated using the GMM, for each subject.

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Notes

  1. 1.

    https://simtk.org/home/mspeedwalksims.

  2. 2.

    https://simtk.org/xml/index.xml.

  3. 3.

    The musculoskeletal setting files are provided free of charge with the OpenSim software: https://simtk.org/home/opensim.

  4. 4.

    A detailed description of the adopted knee model is available at: http://simtk-confluence.stanford.edu:8080/display/OpenSim/Gait+2392+and+2354+Models.

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Acknowledgments

This research has been supported by “Consorzio Ethics” through a grant for research activity on the project “Rehabilitation Robotics”, and by the Faculty research grant at Gannon University.

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Authors and Affiliations

  1. Intelligent Autonomous Systems Laboratory (IAS-Lab), Department of Information Engineering, University of Padua, Via G. Gradenigo, 6/b, 35131, Padua, Italy

    Roberto Bortoletto, Stefano Michieletto & Enrico Pagello

  2. Biomedical Program, Mechanical Engineering Department, Gannon University, 109 University Square, PMB #3251, Erie, PA, 16541, USA

    Davide Piovesan

Authors
  1. Roberto Bortoletto
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  2. Stefano Michieletto
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  3. Enrico Pagello
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  4. Davide Piovesan
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Corresponding author

Correspondence to Roberto Bortoletto .

Editor information

Editors and Affiliations

  1. Information Engineering, University of Padua, Padua, Italy

    Emanuele Menegatti

  2. Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Nathan Michael

  3. Fachbereich Informatik, Technische Universität Kaiserslautern Arbeitsgruppe Robotersysteme, Kaiserslautern, Germany

    Karsten Berns

  4. Integrated Information Technology, Aoyama Gakuin University, Tokyo, Japan

    Hiroaki Yamaguchi

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Bortoletto, R., Michieletto, S., Pagello, E., Piovesan, D. (2016). Human Muscle-Tendon Stiffness Estimation During Normal Gait Cycle Based on Gaussian Mixture Model. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds) Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-319-08338-4_86

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  • DOI: https://doi.org/10.1007/978-3-319-08338-4_86

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08337-7

  • Online ISBN: 978-3-319-08338-4

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