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Motor Outcomes of Repetitive Transcranial Magnetic Stimulation Are Dependent on the Specific Interneuron Circuit Targeted Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Predictive Framework of Human Locomotion Based on Neuromuscular Primitives and Modeling

Authors : Massimo Sartori, José González-Vargas, Strahinja Došen, José L. Pons, Dario Farina

Published in: Converging Clinical and Engineering Research on Neurorehabilitation II

Publisher: Springer International Publishing

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Abstract

Synthesizing human movement in computational neuro-mechanical models is a complex problem. In this study, we describe a predictive framework that combines computational models of neural, sensory and mechanical processes. Our proposed formulation facilitates the transition towards the design of a new class of bio-mimetic assistive devices that do not rely on explicit representations of task-specific motor control models.

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previous chapter Soft Robots that Mimic the Neuromusculoskeletal System
next chapter Towards a Better Understanding of Stability in Human Walking Using Model-Based Optimal Control and Experimental Data
Literature
1.
M. Sartori, D.G. Llyod, D. Farina, Neural data-driven musculoskeletal modeling for personalized neurorehabilitation technologies. IEEE Trans. Biomed. Eng. (5), 879−893 (2016)
2.
S.L. Delp, F.C. Anderson, A.S. Arnold, P. Loan, A. Habib, C.T. John, E. Guendelman, D.G. Thelen, OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans. Biomed. Eng. 54(11), 1940–1950 (2007)CrossRef
3.
J. Gonzalez-Vargas, M. Sartori, S. Dosen, D. Torricelli, J.L. Pons, D. Farina, A predictive model of muscle excitations based on muscle modularity for a large repertoire of human locomotion conditions. Front. Comput. Neurosci. 9(114), 1–14 (2015)
4.
M. Sartori, L. Gizzi, D.G. Lloyd, D. Farina, A musculoskeletal model of human locomotion driven by a low dimensional set of impulsive excitation primitives. Front. Comput. Neurosci. 7, 79 (2013)
5.
M. Sartori, M. Reggiani, D. Farina, D.G. Lloyd, EMG-driven forward-dynamic estimation of muscle force and joint moment about multiple degrees of freedom in the human lower extremity. PLoS One 7(12), 1–11 (2012)CrossRef
6.
C. Pizzolato, D.G. Lloyd, M. Sartori, E. Ceseracciu, T.F. Besier, B.J. Fregly, M. Reggiani, CEINMS: a toolbox to investigate the influence of different neural control solutions on the prediction of muscle excitation and joint moments during dynamic motor tasks. J. Biomech. (2015)
7.
M. Sartori, M. Reggiani, A.J. van den Bogert, D.G. Lloyd, Estimation of musculotendon kinematics in large musculoskeletal models using multidimensional B-splines. J. Biomech. 45(3), 595–601 (2012)CrossRef
8.
M. Sartori, D. Farina, D.G. Lloyd, Hybrid neuromusculoskeletal modeling to best track joint moments using a balance between muscle excitations derived from electromyograms and optimization. J. Biomech. 47(15), 3613–3621 (2014)CrossRef
9.
P. Gerus, M. Sartori, T.F. Besier, B.J. Fregly, S.L. Delp, S. Banks, M.G. Pandy, D.D. D’Lima, D.G. Lloyd, Subject-specific knee joint geometry improves predictions of medial tibiofemoral contact forces. J. Biomech. 46(16), 2778–2786 (2013)CrossRef
10.
M. Sartori, M. Maculan, C. Pizzolato, M. Reggiani, D. Farina, Modeling and Simulating the Neuromuscular Mechanisms regulating Ankle and Knee Joint Stiffness during Human Locomotion. J. Neurophysiol. 114, 2509–2527 (2015)CrossRef
Metadata
Title
Predictive Framework of Human Locomotion Based on Neuromuscular Primitives and Modeling
Authors
Massimo Sartori
José González-Vargas
Strahinja Došen
José L. Pons
Dario Farina
Copyright Year
2017
Book
Converging Clinical and Engineering Research on Neurorehabilitation II

Print ISBN: 978-3-319-46668-2

Electronic ISBN: 978-3-319-46669-9

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-46669-9_46