Sartori, Massimo

[["dc.bibliographiccitation.artnumber","UNSP 79"],["dc.bibliographiccitation.journal","Frontiers in Computational Neuroscience"],["dc.bibliographiccitation.volume","7"],["dc.contributor.author","Sartori, Massimo"],["dc.contributor.author","Gizzi, Leonardo"],["dc.contributor.author","Lloyd, David G."],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2018-11-07T09:23:34Z"],["dc.date.available","2018-11-07T09:23:34Z"],["dc.date.issued","2013"],["dc.description.abstract","Human 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 excitation 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 factor were then averaged ad parameterized to obtain tasks-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 join moments (i.e., NRMSE = 0.18 +/- 0.08, and R-2 = 0.73 +/- 0.22 across all tasks and subjects) without substantial loss of accuracy with respect of using experimental electromyograms (i.e., NRMSE = 0.16 +/- 0.07, and R-2 = 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.sponsorship","Open-Access-Publikationsfonds 2013"],["dc.identifier.doi","10.3389/fncom.2013.00079"],["dc.identifier.isi","000321329500001"],["dc.identifier.pmid","23805099"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/9132"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/29613"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Frontiers Research Foundation"],["dc.relation.issn","1662-5188"],["dc.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0"],["dc.title","A musculoskeletal model of human locomotion driven by a low dimensional set of impulsive excitation primitives"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e52618"],["dc.bibliographiccitation.issue","12"],["dc.bibliographiccitation.journal","PLoS ONE"],["dc.bibliographiccitation.volume","7"],["dc.contributor.author","Sartori, Massimo"],["dc.contributor.author","Reggiani, Monica"],["dc.contributor.author","Farina, Dario"],["dc.contributor.author","Lloyd, David G."],["dc.date.accessioned","2018-11-07T09:02:10Z"],["dc.date.available","2018-11-07T09:02:10Z"],["dc.date.issued","2012"],["dc.description.abstract","This work examined if currently available electromyography (EMG) driven models, that are calibrated to satisfy joint moments about one single degree of freedom (DOF), could provide the same musculotendon unit (MTU) force solution, when driven by the same input data, but calibrated about a different DOF. We then developed a novel and comprehensive EMG-driven model of the human lower extremity that used EMG signals from 16 muscle groups to drive 34 MTUs and satisfy the resulting joint moments simultaneously produced about four DOFs during different motor tasks. This also led to the development of a calibration procedure that allowed identifying a set of subject-specific parameters that ensured physiological behavior for the 34 MTUs. Results showed that currently available single-DOF models did not provide the same unique MTU force solution for the same input data. On the other hand, the MTU force solution predicted by our proposed multi-DOF model satisfied joint moments about multiple DOFs without loss of accuracy compared to single-DOF models corresponding to each of the four DOFs. The predicted MTU force solution was (1) a function of experimentally measured EMGs, (2) the result of physiological MTU excitation, (3) reflected different MTU contraction strategies associated to different motor tasks, (4) coordinated a greater number of MTUs with respect to currently available single-DOF models, and (5) was not specific to an individual DOF dynamics. Therefore, our proposed methodology has the potential of producing a more dynamically consistent and generalizable MTU force solution than was possible using single-DOF EMG-driven models. This will help better address the important scientific questions previously approached using single-DOF EMG-driven modeling. Furthermore, it might have applications in the development of human-machine interfaces for assistive devices."],["dc.identifier.doi","10.1371/journal.pone.0052618"],["dc.identifier.isi","000313618800115"],["dc.identifier.pmid","23300725"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/8545"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/24614"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Public Library Science"],["dc.relation.issn","1932-6203"],["dc.rights","CC BY 2.5"],["dc.rights.uri","https://creativecommons.org/licenses/by/2.5"],["dc.title","EMG-Driven Forward-Dynamic Estimation of Muscle Force and Joint Moment about Multiple Degrees of Freedom in the Human Lower Extremity"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","3929"],["dc.bibliographiccitation.issue","14"],["dc.bibliographiccitation.journal","Journal of Biomechanics"],["dc.bibliographiccitation.lastpage","3936"],["dc.bibliographiccitation.volume","48"],["dc.contributor.author","Pizzolato, Claudio"],["dc.contributor.author","Lloyd, David G."],["dc.contributor.author","Sartori, Massimo"],["dc.contributor.author","Ceseracciu, Elena"],["dc.contributor.author","Besier, Thor F."],["dc.contributor.author","Fregly, Benjamin J."],["dc.contributor.author","Reggiani, Monica"],["dc.date.accessioned","2018-11-07T09:49:02Z"],["dc.date.available","2018-11-07T09:49:02Z"],["dc.date.issued","2015"],["dc.description.abstract","Personalized neuromusculoskeletal (NMS) models can represent the neurological, physiological, and anatomical characteristics of an individual and can be used to estimate the forces generated inside the human body. Currently, publicly available software to calculate muscle forces are restricted to static and dynamic optimisation methods, or limited to isometric tasks only. We have created and made freely available for the research community the Calibrated EMG-Informed NMS Modelling Toolbox (CEINMS), an OpenSim plug-in that enables investigators to predict different neural control solutions for the same musculoskeletal geometry and measured movements. CEINMS comprises EMG-driven and EMG-informed algorithms that have been previously published and tested. It operates on dynamic skeletal models possessing any number of degrees of freedom and musculotendon units and can be calibrated to the individual to predict measured joint moments and EMG patterns. In this paper we describe the components of CEINMS and its integration with OpenSim. We then analyse how EMG-driven, EMG-assisted, and static optimisation neural control solutions affect the estimated joint moments, muscle forces, and muscle excitations, including muscle co-contraction. (C) 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)."],["dc.identifier.doi","10.1016/j.jbiomech.2015.09.021"],["dc.identifier.isi","000366064000027"],["dc.identifier.pmid","26522621"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12735"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/35429"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Elsevier Sci Ltd"],["dc.relation.issn","1873-2380"],["dc.relation.issn","0021-9290"],["dc.rights","CC BY-NC-ND 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by-nc-nd/4.0"],["dc.title","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"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]