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.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.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.firstpage","228"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Physiotherapy Canada"],["dc.bibliographiccitation.lastpage","233"],["dc.bibliographiccitation.volume","66"],["dc.contributor.author","Horan, Sean A."],["dc.contributor.author","Watson, Steven L."],["dc.contributor.author","Carty, Christopher P."],["dc.contributor.author","Sartori, Massimo"],["dc.contributor.author","Weeks, Benjamin K."],["dc.date.accessioned","2018-11-07T09:45:47Z"],["dc.date.available","2018-11-07T09:45:47Z"],["dc.date.issued","2014"],["dc.description.abstract","Purpose: To determine the kinematic parameters that characterize good and poor single-leg squat (SLS) performance. Methods: A total of 22 healthy young adults free from musculoskeletal impairment were recruited for testing. For each SLS, both two-dimensional video and three-dimensional motion analysis data were collected. Pelvis, hip, and knee angles were calculated using a reliable and validated lower-limb (LL) biomechanical model. Two-dimensional video clips of SLSs were blindly assessed in random order by eight musculoskeletal physiotherapists using a 10-point ordinal scale. To facilitate between-group comparisons, SLS performances were stratified by tertiles corresponding to poor, intermediate, and good SLS performance. Results: Mean ratings of SLS performance assessed by physiotherapists were 8.3 (SD 0.5), 6.8 (SD 0.7), and 4.0 (SD 0.8) for good, intermediate, and poor squats, respectively. Three-dimensional analysis revealed that people whose SLS performance was assessed as poor exhibited increased hip adduction, reduced knee flexion, and increased medio-lateral displacement of the knee joint centre compared to those whose SLS performance was assessed as good (p<0.05). Conclusions: Overall, poor SLS performance is characterized by inadequate knee flexion and excessive frontal plane motion of the knee and hip. Future investigations of SLS performance should consider standardizing knee flexion angle to illuminate other influential kinematic parameters."],["dc.identifier.doi","10.3138/ptc.2013-09"],["dc.identifier.isi","000342508600002"],["dc.identifier.pmid","25125775"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/34707"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Univ Toronto Press Inc"],["dc.relation.issn","1708-8313"],["dc.relation.issn","0300-0508"],["dc.title","Lower-Limb Kinematics of Single-Leg Squat Performance in Young Adults"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["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.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"]]