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","2509"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Journal of Neurophysiology"],["dc.bibliographiccitation.lastpage","2527"],["dc.bibliographiccitation.volume","114"],["dc.contributor.author","Sartori, Massimo"],["dc.contributor.author","Maculan, Marco"],["dc.contributor.author","Pizzolato, Claudio"],["dc.contributor.author","Reggiani, Monica"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2018-11-07T09:50:44Z"],["dc.date.available","2018-11-07T09:50:44Z"],["dc.date.issued","2015"],["dc.description.abstract","This work presents an electrophysiologically and dynamically consistent musculoskeletal model to predict stiffness in the human ankle and knee joints as derived from the joints constituent biological tissues (i.e., the spanning musculotendon units). The modeling method we propose uses electromyography (EMG) recordings from 13 muscle groups to drive forward dynamic simulations of the human leg in five healthy subjects during over-ground walking and running. The EMG-driven musculoskeletal model estimates musculotendon and resulting joint stiffness that is consistent with experimental EMG data as well as with the experimental joint moments. This provides a framework that allows for the first time observing 1) the elastic interplay between the knee and ankle joints, 2) the individual muscle contribution to joint stiffness, and 3) the underlying co-contraction strategies. It provides a theoretical description of how stiffness modulates as a function of muscle activation, fiber contraction, and interacting tendon dynamics. Furthermore, it describes how this differs from currently available stiffness definitions, including quasi-stiffness and short-range stiffness. This work offers a theoretical and computational basis for describing and investigating the neuromuscular mechanisms underlying human locomotion."],["dc.identifier.doi","10.1152/jn.00989.2014"],["dc.identifier.isi","000363548300041"],["dc.identifier.pmid","26245321"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/35769"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Amer Physiological Soc"],["dc.relation.issn","1522-1598"],["dc.relation.issn","0022-3077"],["dc.title","Modeling and simulating the neuromuscular mechanisms regulating ankle and knee joint stiffness during human locomotion"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","13465"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Scientific reports"],["dc.bibliographiccitation.volume","7"],["dc.contributor.author","Sartori, Massimo"],["dc.contributor.author","Yavuz, Utku Ş."],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2019-07-09T11:44:37Z"],["dc.date.available","2019-07-09T11:44:37Z"],["dc.date.issued","2017"],["dc.description.abstract","Human motor function emerges from the interaction between the neuromuscular and the musculoskeletal systems. Despite the knowledge of the mechanisms underlying neural and mechanical functions, there is no relevant understanding of the neuro-mechanical interplay in the neuro-musculo-skeletal system. This currently represents the major challenge to the understanding of human movement. We address this challenge by proposing a paradigm for investigating spinal motor neuron contribution to skeletal joint mechanical function in the intact human in vivo. We employ multi-muscle spatial sampling and deconvolution of high-density fiber electrical activity to decode accurate α-motor neuron discharges across five lumbosacral segments in the human spinal cord. We use complete α-motor neuron discharge series to drive forward subject-specific models of the musculoskeletal system in open-loop with no corrective feedback. We perform validation tests where mechanical moments are estimated with no knowledge of reference data over unseen conditions. This enables accurate blinded estimation of ankle function purely from motor neuron information. Remarkably, this enables observing causal associations between spinal motor neuron activity and joint moment control. We provide a new class of neural data-driven musculoskeletal modeling formulations for bridging between movement neural and mechanical levels in vivo with implications for understanding motor physiology, pathology, and recovery."],["dc.identifier.doi","10.1038/s41598-017-13766-6"],["dc.identifier.pmid","29044165"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14841"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59050"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation","info:eu-repo/grantAgreement/EC/H2020/737570/EU//INTERSPINE"],["dc.relation.issn","2045-2322"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","610"],["dc.title","In Vivo Neuromechanics: Decoding Causal Motor Neuron Behavior with Resulting Musculoskeletal Function."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["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.firstpage","879"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","IEEE Transactions on Biomedical Engineering"],["dc.bibliographiccitation.lastpage","893"],["dc.bibliographiccitation.volume","63"],["dc.contributor.author","Sartori, Massimo"],["dc.contributor.author","Llyod, 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.description.abstract","Objectives: The development of neurorehabilitation technologies requires the profound understanding of the mechanisms underlying an individual's motor ability and impairment. A major factor limiting this understanding is the difficulty of bridging between events taking place at the neurophysiologic level (i.e., motor neuron firings) with those emerging at the musculoskeletal level (i.e. joint actuation), in vivo in the intact moving human. This review presents emerging model-based methodologies for filling this gap that are promising for developing clinically viable technologies. Methods: We provide a design overview of musculoskeletal modeling formulations driven by recordings of neuromuscular activity with a critical comparison to alternative model-free approaches in the context of neurorehabilitation technologies. We present advanced electromyography-based techniques for interfacing with the human nervous system and model-based techniques for translating the extracted neural information into estimates of motor function. Results: We introduce representative application areas where modeling is relevant for accessing neuromuscular variables that could not be measured experimentally. We then show how these variables are used for designing personalized rehabilitation interventions, biologically inspired limbs, and human-machine interfaces. Conclusion: The ability of using electrophysiological recordings to inform biomechanical models enables accessing a broader range of neuromechanical variables than analyzing electrophysiological data or movement data individually. This enables understanding the neuromechanical interplay underlying in vivo movement function, pathology, and robot-assisted motor recovery. Significance: Filling the gap between our understandings of movement neural and mechanical functions is central for addressing one of the major challenges in neurorehabilitation: personalizing current technologies and interventions to an individual's anatomy and impairment."],["dc.format.extent","1341"],["dc.identifier.doi","10.1109/TBME.2016.2538296"],["dc.identifier.eissn","1558-2531"],["dc.identifier.isi","000375001600001"],["dc.identifier.issn","0018-9294"],["dc.identifier.pmid","27046865"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13367"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/76018"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Ieee-inst Electrical Electronics Engineers Inc"],["dc.relation.haserratum","/handle/2/76019"],["dc.relation.issn","1558-2531"],["dc.relation.issn","0018-9294"],["dc.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0"],["dc.title","Neural Data-Driven Musculoskeletal Modeling for Personalized Neurorehabilitation Technologies"],["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","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.issue","2"],["dc.bibliographiccitation.journal","Nature Biomedical Engineering"],["dc.bibliographiccitation.volume","1"],["dc.contributor.author","Farina, Dario"],["dc.contributor.author","Vujaklija, Ivan"],["dc.contributor.author","Sartori, Massimo"],["dc.contributor.author","Kapelner, Tamás"],["dc.contributor.author","Negro, Francesco"],["dc.contributor.author","Jiang, Ning"],["dc.contributor.author","Bergmeister, Konstantin"],["dc.contributor.author","Andalib, Arash"],["dc.contributor.author","Principe, Jose"],["dc.contributor.author","Aszmann, Oskar C."],["dc.date.accessioned","2020-12-10T18:09:54Z"],["dc.date.available","2020-12-10T18:09:54Z"],["dc.date.issued","2017"],["dc.identifier.doi","10.1038/s41551-016-0025"],["dc.identifier.eissn","2157-846X"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/73794"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["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","114"],["dc.bibliographiccitation.journal","Frontiers in Computational Neuroscience"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Gonzalez-Vargas, Jose"],["dc.contributor.author","Sartori, Massimo"],["dc.contributor.author","Dosen, Strahinja"],["dc.contributor.author","Torricelli, Diego"],["dc.contributor.author","Pons, Jose L."],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2018-11-07T09:51:34Z"],["dc.date.available","2018-11-07T09:51:34Z"],["dc.date.issued","2015"],["dc.description.abstract","Humans can efficiently walk across a large variety of terrains and locomotion conditions with little or no mental effort. It has been hypothesized that the nervous system simplifies neuromuscular control by using muscle synergies, thus organizing multi-muscle activity into a small number of coordinative co-activation modules. In the present study we investigated how muscle modularity is structured across a large repertoire of locomotion conditions including five different speeds and five different ground elevations. For this we have used the non-negative matrix factorization technique in order to explain EMG experimental data with a low-dimensional set of four motor components. In this context each motor components is composed of a non-negative factor and the associated muscle weightings. Furthermore, we have investigated if the proposed descriptive analysis of muscle modularity could be translated into a predictive model that could: (1) Estimate how motor components modulate across locomotion speeds and ground elevations. This implies not only estimating the non-negative factors temporal characteristics, but also the associated muscle weighting variations. (2) Estimate how the resulting muscle excitations modulate across novel locomotion conditions and subjects. The results showed three major distinctive features of muscle modularity: (1) the number of motor components was preserved across all locomotion conditions, (2) the non negative factors were consistent in shape and timing across all locomotion conditions, and (3) the muscle weightings were modulated as distinctive functions of locomotion speed and ground elevation. Results also showed that the developed predictive model was able to reproduce well the muscle modularity of un-modeled data, i.e., novel subjects and conditions. Muscle weightings were reconstructed with a cross-correlation factor greater than 70% and a root mean square error less than 0.10. Furthermore, the generated muscle excitations matched well the experimental excitation with a cross correlation factor greater than 85% and a root mean square error less than 0.09. The ability of synthetizing the neuromuscular mechanisms underlying human locomotion across a variety of locomotion conditions will enable solutions in the field of neurorehabilitation technologies and control of bipedal artificial systems. Open access of the model implementation is provided for further analysis at https://simtk.org/home/p-mep/."],["dc.description.sponsorship","Open-Access Publikationsfonds 2015"],["dc.identifier.doi","10.3389/fncom.2015.00114"],["dc.identifier.isi","000361631700001"],["dc.identifier.pmid","26441624"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12160"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/35943"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Frontiers Media Sa"],["dc.relation.issn","1662-5188"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","A predictive model of muscle excitations based on muscle modularity for a large repertoire of human locomotion conditions"],["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","210"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","IEEE Transactions on Neural Systems and Rehabilitation Engineering"],["dc.bibliographiccitation.lastpage","220"],["dc.bibliographiccitation.volume","23"],["dc.contributor.author","Schaffelhofer, S."],["dc.contributor.author","Sartori, M."],["dc.contributor.author","Scherberger, H."],["dc.contributor.author","Farina, D."],["dc.date.accessioned","2015-06-15T12:57:15Z"],["dc.date.accessioned","2021-10-27T13:21:01Z"],["dc.date.available","2015-06-15T12:57:15Z"],["dc.date.available","2021-10-27T13:21:01Z"],["dc.date.issued","2015-03-01"],["dc.description.abstract","Reach-to-grasp tasks have become popular paradigms for exploring the neural origin of hand and arm movements. This is typically investigated by correlating limb kinematic with electrophysiological signals from intracortical recordings. However, it has never been investigated whether reach and grasp movements could be well expressed in the muscle domain and whether this could bring improvements with respect to current joint domain-based task representations. In this study, we trained two macaque monkeys to grasp 50 different objects, which resulted in a high variability of hand configurations. A generic musculoskeletal model of the human upper extremity was scaled and morphed to match the specific anatomy of each individual animal. The primate-specific model was used to perform 3-D reach-to-grasp simulations driven by experimental upper limb kinematics derived from electromagnetic sensors. Simulations enabled extracting joint angles from 27 degrees of freedom and the instantaneous length of 50 musculotendon units. Results demonstrated both a more compact representation and a higher decoding capacity of grasping tasks when movements were expressed in the muscle kinematics domain than when expressed in the joint kinematics domain. Accessing musculoskeletal variables might improve our understanding of cortical hand-grasping areas coding, with implications in the development of prosthetics hands."],["dc.identifier.doi","10.1109/tnsre.2014.2364776"],["dc.identifier.gro","3151429"],["dc.identifier.pmid","25350935"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/11890"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/91988"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.relation.euproject","DEMOVE"],["dc.relation.issn","1558-0210"],["dc.relation.issn","1534-4320"],["dc.relation.orgunit","Bernstein Center for Computational Neuroscience Göttingen"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Musculoskeletal representation of a large repertoire of hand grasping actions in primates."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]