Sartori, Massimo

[["dc.bibliographiccitation.artnumber","12"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Source Code for Biology and Medicine"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Mantoan, Alice"],["dc.contributor.author","Pizzolato, Claudio"],["dc.contributor.author","Sartori, Massimo"],["dc.contributor.author","Sawacha, Zimi"],["dc.contributor.author","Cobelli, Claudio"],["dc.contributor.author","Reggiani, Monica"],["dc.date.accessioned","2019-07-09T11:41:49Z"],["dc.date.available","2019-07-09T11:41:49Z"],["dc.date.issued","2015"],["dc.description.abstract","Abstract Background Neuromusculoskeletal modeling and simulation enable investigation of the neuromusculoskeletal system and its role in human movement dynamics. These methods are progressively introduced into daily clinical practice. However, a major factor limiting this translation is the lack of robust tools for the pre-processing of experimental movement data for their use in neuromusculoskeletal modeling software. Results This paper presents MOtoNMS (matlab MOtion data elaboration TOolbox for NeuroMusculoSkeletal applications), a toolbox freely available to the community, that aims to fill this lack. MOtoNMS processes experimental data from different motion analysis devices and generates input data for neuromusculoskeletal modeling and simulation software, such as OpenSim and CEINMS (Calibrated EMG-Informed NMS Modelling Toolbox). MOtoNMS implements commonly required processing steps and its generic architecture simplifies the integration of new user-defined processing components. MOtoNMS allows users to setup their laboratory configurations and processing procedures through user-friendly graphical interfaces, without requiring advanced computer skills. Finally, configuration choices can be stored enabling the full reproduction of the processing steps. MOtoNMS is released under GNU General Public License and it is available at the SimTK website and from the GitHub repository. Motion data collected at four institutions demonstrate that, despite differences in laboratory instrumentation and procedures, MOtoNMS succeeds in processing data and producing consistent inputs for OpenSim and CEINMS. Conclusions MOtoNMS fills the gap between motion analysis and neuromusculoskeletal modeling and simulation. Its support to several devices, a complete implementation of the pre-processing procedures, its simple extensibility, the available user interfaces, and its free availability can boost the translation of neuromusculoskeletal methods in daily and clinical practice."],["dc.identifier.doi","10.1186/s13029-015-0044-4"],["dc.identifier.pmid","26579208"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12427"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/58523"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation","info:eu-repo/grantAgreement/EC/FP7/611695/EU//BioMot"],["dc.relation.euproject","BioMot"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","MOtoNMS: A MATLAB toolbox to process motion data for neuromusculoskeletal modeling and simulation"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["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"]]