Sartori, Massimo

[["dc.bibliographiccitation.artnumber","e1368"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Wiley Interdisciplinary Reviews Systems Biology and Medicine"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Sartori, Massimo"],["dc.contributor.author","Fernandez, J. W."],["dc.contributor.author","Modenese, L."],["dc.contributor.author","Carty, Christopher P."],["dc.contributor.author","Barber, L. A."],["dc.contributor.author","Oberhofer, K."],["dc.contributor.author","Zhang, J."],["dc.contributor.author","Handsfield, G. G."],["dc.contributor.author","Stott, N. S."],["dc.contributor.author","Besier, Thor F."],["dc.contributor.author","Farina, Dario"],["dc.contributor.author","Lloyd, David G."],["dc.date.accessioned","2018-11-07T10:27:01Z"],["dc.date.available","2018-11-07T10:27:01Z"],["dc.date.issued","2017"],["dc.description.abstract","This position paper proposes a modeling pipeline to develop clinically relevant neuromusculoskeletal models to understand and treat complex neurological disorders. Although applicable to a variety of neurological conditions, we provide direct pipeline applicative examples in the context of cerebral palsy (CP). This paper highlights technologies in: (1) patient-specific segmental rigid body models developed from magnetic resonance imaging for use in inverse kinematics and inverse dynamics pipelines; (2) efficient population-based approaches to derive skeletal models and muscle origins/insertions that are useful for population statistics and consistent creation of continuum models; (3) continuum muscle descriptions to account for complex muscle architecture including spatially varying material properties with muscle wrapping; (4) muscle and tendon properties specific to CP; and (5) neural-based electromyography-informed methods for muscle force prediction. This represents a novel modeling pipeline that couples for the first time electromyography extracted features of disrupted neuromuscular behavior with advanced numerical methods for modeling CP-specific musculoskeletal morphology and function. The translation of such pipeline to the clinical level will provide a new class of biomarkers that objectively describe the neuromusculoskeletal determinants of pathological locomotion and complement current clinical assessment techniques, which often rely on subjective judgment. (C) 2016 Wiley Periodicals, Inc."],["dc.identifier.doi","10.1002/wsbm.1368"],["dc.identifier.isi","000394898500001"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/43161"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","PUB_WoS_Import"],["dc.publisher","Wiley"],["dc.relation.issn","1939-005X"],["dc.relation.issn","1939-5094"],["dc.title","Toward modeling locomotion using electromyography-informed 3D models: application to cerebral palsy"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","3613"],["dc.bibliographiccitation.issue","15"],["dc.bibliographiccitation.journal","Journal of Biomechanics"],["dc.bibliographiccitation.lastpage","3621"],["dc.bibliographiccitation.volume","47"],["dc.contributor.author","Sartori, Massimo"],["dc.contributor.author","Farina, Dario"],["dc.contributor.author","Lloyd, David G."],["dc.date.accessioned","2018-11-07T09:32:25Z"],["dc.date.available","2018-11-07T09:32:25Z"],["dc.date.issued","2014"],["dc.description.abstract","Current electromyography (EMG)-driven musculoskeletal models are used to estimate joint moments measured from an individual's extremities during dynamic movement with varying levels of accuracy. The main benefit is the underlying musculoskeletal dynamics is simulated as a function of realistic, subject-specific, neural-excitation patterns provided by the EMG data. The main disadvantage is surface EMG cannot provide information on deeply located muscles. Furthermore, EMG data may be affected by cross-talk, recording and post-processing artifacts that could adversely influence the EMG's information content This limits the EMG-driven model's ability to calculate the multi-muscle dynamics and the resulting joint moments about multiple degrees of freedom. We present a hybrid neuromusculoskeletal model that combines calibration, subject-specificity, EMG-driven and static optimization methods together. In this, the joint moment tracking errors are minimized by balancing the information content extracted from the experimental EMG data and from that generated by a static optimization method. Using movement data from five healthy male subjects during walking and running we explored the hybrid model's best configuration to minimally adjust recorded EMGs and predict missing EMGs while attaining the best tracking of joint moments. Minimally adjusted and predicted excitations substantially improved the experimental joint moment tracking accuracy than current EMG-driven models. The ability of the hybrid model to predict missing muscle EMGs was also examined. The proposed hybrid model enables muscle-driven simulations of human movement while enforcing physiological constraints on muscle excitation patterns. This might have important implications for studying pathological movement for which EMG recordings are limited. (C) 2014 Elsevier Ltd. All rights reserved."],["dc.identifier.doi","10.1016/j.jbiomech.2014.10.009"],["dc.identifier.isi","000345805500004"],["dc.identifier.pmid","25458151"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/31754"],["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.title","Hybrid neuromusculoskeletal modeling to best track joint moments using a balance between muscle excitations derived from electromyograms and optimization"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["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.firstpage","28"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","International Journal for Numerical Methods in Biomedical Engineering"],["dc.bibliographiccitation.lastpage","41"],["dc.bibliographiccitation.volume","30"],["dc.contributor.author","Fernandez, Justin"],["dc.contributor.author","Sartori, Massimo"],["dc.contributor.author","Lloyd, David G."],["dc.contributor.author","Munro, Jacob"],["dc.contributor.author","Shim, Vickie"],["dc.date.accessioned","2018-11-07T09:46:08Z"],["dc.date.available","2018-11-07T09:46:08Z"],["dc.date.issued","2014"],["dc.description.abstract","A modelling framework using the international Physiome Project is presented for evaluating the role of muscles on acetabular stress patterns in the natural hip. The novel developments include the following: (i) an efficient method for model generation with validation; (ii) the inclusion of electromyography-estimated muscle forces from gait; and (iii) the role that muscles play in the hip stress pattern. The 3D finite element hip model includes anatomically based muscle area attachments, material properties derived from Hounsfield units and validation against an Instron compression test. The primary outcome from this study is that hip loading applied as anatomically accurate muscle forces redistributes the stress pattern and reduces peak stress throughout the pelvis and within the acetabulum compared with applying the same net hip force without muscles through the femur. Muscle forces also increased stress where large muscles have small insertion sites. This has implications for the hip where bone stress and strain are key excitation variables used to initiate bone remodelling based on the strain-based bone remodelling theory. Inclusion of muscle forces reduces the predicted sites and degree of remodelling. The secondary outcome is that the key muscles that influenced remodelling in the acetabulum were the rectus femoris, adductor magnus and iliacus.Copyright (c) 2013 John Wiley & Sons, Ltd."],["dc.description.sponsorship","Wishbone trust; Robertson Foundation"],["dc.identifier.doi","10.1002/cnm.2586"],["dc.identifier.isi","000337630500002"],["dc.identifier.pmid","23982908"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/34797"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Wiley-blackwell"],["dc.relation.issn","2040-7947"],["dc.relation.issn","2040-7939"],["dc.title","Bone remodelling in the natural acetabulum is influenced by muscle force-induced bone stress"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["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.artnumber","20150084"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Interface Focus"],["dc.bibliographiccitation.volume","6"],["dc.contributor.author","Fernandez, J."],["dc.contributor.author","Zhang, J."],["dc.contributor.author","Heidlauf, T."],["dc.contributor.author","Sartori, Massimo"],["dc.contributor.author","Besier, Thor F."],["dc.contributor.author","Roehrle, O."],["dc.contributor.author","Lloyd, David G."],["dc.date.accessioned","2018-11-07T10:16:01Z"],["dc.date.available","2018-11-07T10:16:01Z"],["dc.date.issued","2016"],["dc.description.abstract","This paper proposes methods and technologies that advance the state of the art for modelling the musculoskeletal system across the spatial and temporal scales; and storing these using efficient ontologies and tools. We present population-based modelling as an efficient method to rapidly generate individual morphology from only a few measurements and to learn from the ever-increasing supply of imaging data available. We present multiscale methods for continuum muscle and bone models; and efficient mechanostatistical methods, both continuum and particle-based, to bridge the scales. Finally, we examine both the importance that muscles play in bone remodelling stimuli and the latest muscle force prediction methods that use electromyography-assisted modelling techniques to compute musculoskeletal forces that best reflect the underlying neuromuscular activity. Our proposal is that, in order to have a clinically relevant virtual physiological human, (i) bone and muscle mechanics must be considered together; (ii) models should be trained on population data to permit rapid generation and use underlying principal modes that describe both muscle patterns and morphology; and (iii) these tools need to be available in an open-source repository so that the scientific community may use, personalize and contribute to the database of models."],["dc.identifier.doi","10.1098/rsfs.2015.0084"],["dc.identifier.isi","000375410900005"],["dc.identifier.pmid","27051510"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/40948"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Royal Soc"],["dc.relation.issn","2042-8901"],["dc.relation.issn","2042-8898"],["dc.title","Multiscale musculoskeletal modelling, data - model fusion and electromyography-informed modelling"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","2778"],["dc.bibliographiccitation.issue","16"],["dc.bibliographiccitation.journal","Journal of Biomechanics"],["dc.bibliographiccitation.lastpage","2786"],["dc.bibliographiccitation.volume","46"],["dc.contributor.author","Gerus, Pauline"],["dc.contributor.author","Sartori, Massimo"],["dc.contributor.author","Besier, Thor F."],["dc.contributor.author","Fregly, Benjamin J."],["dc.contributor.author","Delp, Scott L."],["dc.contributor.author","Banks, Scott A."],["dc.contributor.author","Pandy, Marcus G."],["dc.contributor.author","D'Lima, Darryl D."],["dc.contributor.author","Lloyd, David G."],["dc.date.accessioned","2018-11-07T09:17:31Z"],["dc.date.available","2018-11-07T09:17:31Z"],["dc.date.issued","2013"],["dc.description.abstract","Estimating tibiofemoral joint contact forces is important for understanding the initiation and progression of knee osteoarthritis. However, tibiofemoral contact force predictions are influenced by many factors including muscle forces and anatomical representations of the knee joint. This study aimed to investigate the influence of subject-specific geometry and knee joint kinematics on the prediction of tibiofemoral contact forces using a calibrated EMG-driven neuromusculoskeletal model of the knee. One participant fitted with an instrumented total knee replacement walked at a self-selected speed while medial and lateral tibiofemoral contact forces, ground reaction forces, whole-body kinematics, and lower-limb muscle activity were simultaneously measured. The combination of generic and subject-specific knee joint geometry and kinematics resulted in four different OpenSim models used to estimate muscle-tendon lengths and moment arms. The subject-specific geometric model was created from CT scans and the subject-specific knee joint kinematics representing the translation of the tibia relative to the femur was obtained from fluoroscopy. The EMG-driven model was calibrated using one walking trial, but with three different cost functions that tracked the knee flexion/extension moments with and without constraint over the estimated joint contact forces. The calibrated models then predicted the medial and lateral tibiofemoral contact forces for five other different walking trials. The use of subject-specific models with minimization of the peak tibiofemoral contact forces improved the accuracy of medial contact forces by 47% and lateral contact forces by 7%, respectively compared with the use of generic musculoskeletal model. (C) 2013 Elsevier Ltd. All rights reserved."],["dc.identifier.doi","10.1016/j.jbiomech.2013.09.005"],["dc.identifier.isi","000328093200004"],["dc.identifier.pmid","24074941"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/28192"],["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.title","Subject-specific knee joint geometry improves predictions of medial tibiofemoral contact forces"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1341"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","IEEE Transactions on Biomedical Engineering"],["dc.bibliographiccitation.lastpage","1341"],["dc.bibliographiccitation.volume","63"],["dc.contributor.author","Sartori, Massimo"],["dc.contributor.author","Lloyd, David G."],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2020-12-10T18:26:16Z"],["dc.date.available","2020-12-10T18:26:16Z"],["dc.date.issued","2016"],["dc.identifier.doi","10.1109/TBME.2016.2563138"],["dc.identifier.eissn","1558-2531"],["dc.identifier.issn","0018-9294"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/76019"],["dc.notes.intern","DOI Import GROB-354"],["dc.relation.iserratumof","/handle/2/76018"],["dc.title","Corrections to “Neural Data-Driven Musculoskeletal Modeling for Personalized Neurorehabilitation Technologies” [May 16 879-893]"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","erratum_ja"],["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"]]