Show simple item record

A musculoskeletal model of human locomotion driven by a low dimensional set of impulsive excitation primitives

dc.contributor.authorSartori, Massimo
dc.contributor.authorGizzi, Leonardo
dc.contributor.authorLloyd, David G.
dc.contributor.authorFarina, Dario
dc.date.accessioned2013-07-03T09:37:37Z
dc.date.available2013-07-03T09:37:37Z
dc.date.issued2013-06-26
dc.relation.ISSN1662-5188
dc.identifier.urihttp://resolver.sub.uni-goettingen.de/purl?gs-1/9132
dc.description.abstractHuman locomotion has been described as being generated by an impulsive (burst-like) excitation of groups of musculotendon units, with timing dependent on the biomechanical goal of the task. Despite this view being supported by many experimental observations on specific locomotion tasks, it is still unknown if the same impulsive controller (i.e., a low-dimensional set of time-delayed excitastion primitives) can be used as input drive for large musculoskeletal models across different human locomotion tasks. For this purpose, we extracted, with non-negative matrix factorization, five non-negative factors from a large sample of muscle electromyograms in two healthy subjects during four motor tasks. These included walking, running, sidestepping, and crossover cutting maneuvers. The extracted non-negative factors were then averaged and parameterized to obtain task-generic Gaussian-shaped impulsive excitation curves or primitives. These were used to drive a subject-specific musculoskeletal model of the human lower extremity. Results showed that the same set of five impulsive excitation primitives could be used to predict the dynamics of 34 musculotendon units and the resulting hip, knee and ankle joint moments (i.e., NRMSE = 0.18 ± 0.08, and R2 = 0.73 ± 0.22 across all tasks and subjects) without substantial loss of accuracy with respect to using experimental electromyograms (i.e., NRMSE = 0.16 ± 0.07, and R2 = 0.78 ± 0.18 across all tasks and subjects). Results support the hypothesis that biomechanically different motor tasks might share similar neuromuscular control strategies. This might have implications in neurorehabilitation technologies such as human-machine interfaces for the torque-driven, proportional control of powered prostheses and orthoses. In this, device control commands (i.e., predicted joint torque) could be derived without direct experimental data but relying on simple parameterized Gaussian-shaped curves, thus decreasing the input drive complexity and the number of needed sensors.
dc.description.sponsorshipOpen-Access-Publikationsfonds 2013
dc.format.extent22
dc.language.isoeng
dc.relationinfo:eu-repo/grantAgreement/EC/FP7/600698/EU//H2R
dc.rightsopenAccess
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/
dc.subjectEMG-drivenmodeling;musculoskeletalmodeling;lowerextremity;multipledegreesoffreedom; muscledynamics;musclesynergy
dc.titleA musculoskeletal model of human locomotion driven by a low dimensional set of impulsive excitation primitives
dc.typejournalArticle
dc.identifier.doi10.3389/fncom.2013.00079
dc.type.versionpublishedVersion
dc.identifier.fs596002
dc.bibliographicCitation.volume7
dc.type.subtypejournalArticle
dc.identifier.pmid23805099
dc.bibliographicCitation.articlenumber79
dc.relation.euprojectH2R
dc.description.statuspeerReviewed
dc.bibliographicCitation.journalFrontiers in Computational Neuroscience


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

These documents are avalilable under the license:
openAccess