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dc.contributor.authorEskinazi, Ilan
Fregly, Benjamin J.
dc.date.accessioned 2019-12-11T15:44:29Z
dc.date.available 2019-12-11T15:44:29Z
dc.date.issued 2018
dc.identifier.citation Eskinazi, Ilan and Fregly, Benjamin J.. "A computational framework for simultaneous estimation of muscle and joint contact forces and body motion using optimization and surrogate modeling." Medical Engineering & Physics, 54, (2018) Elsevier: 56-64. https://doi.org/10.1016/j.medengphy.2018.02.002.
dc.identifier.urihttps://hdl.handle.net/1911/107863
dc.description.abstract Concurrent estimation of muscle activations, joint contact forces, and joint kinematics by means of gradient-based optimization of musculoskeletal models is hindered by computationally expensive and non-smooth joint contact and muscle wrapping algorithms. We present a framework that simultaneously speeds up computation and removes sources of non-smoothness from muscle force optimizations using a combination of parallelization and surrogate modeling, with special emphasis on a novel method for modeling joint contact as a surrogate model of a static analysis. The approach allows one to efficiently introduce elastic joint contact models within static and dynamic optimizations of human motion. We demonstrate the approach by performing two optimizations, one static and one dynamic, using a pelvis-leg musculoskeletal model undergoing a gait cycle. We observed convergence on the order of seconds for a static optimization time frame and on the order of minutes for an entire dynamic optimization. The presented framework may facilitate model-based efforts to predict how planned surgical or rehabilitation interventions will affect post-treatment joint and muscle function.
dc.language.iso eng
dc.publisher Elsevier
dc.rights This is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Elsevier.
dc.title A computational framework for simultaneous estimation of muscle and joint contact forces and body motion using optimization and surrogate modeling
dc.type Journal article
dc.citation.journalTitle Medical Engineering & Physics
dc.subject.keywordMusculoskeletal
Modeling
Contact
Muscle
Optimization
Neural network
Surrogate
Moment arms
Knee
Joint
dc.citation.volumeNumber 54
dc.identifier.digital nihms946226
dc.type.dcmi Text
dc.identifier.doihttps://doi.org/10.1016/j.medengphy.2018.02.002
dc.type.publication post-print
dc.citation.firstpage 56
dc.citation.lastpage 64


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