M
Massimo Sartori
Researcher at University of Twente
Publications - 114
Citations - 2968
Massimo Sartori is an academic researcher from University of Twente. The author has contributed to research in topics: Computer science & Exoskeleton. The author has an hindex of 23, co-authored 98 publications receiving 2083 citations. Previous affiliations of Massimo Sartori include National Research Council & University of Göttingen.
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Journal ArticleDOI
Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation
Dario Farina,Dario Farina,Ivan Vujaklija,Ivan Vujaklija,Massimo Sartori,Tamas Kapelner,Francesco Negro,Francesco Negro,Ning Jiang,Konstantin D. Bergmeister,Arash Andalib,Jose C. Principe,Oskar C. Aszmann +12 more
TL;DR: An offline man/machine interface that takes advantage of the discharge timings of spinal motor neurons to create an interface with the output layers of the spinal cord circuitry that allows for the intuitive control of multiple degrees of freedom.
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EMG-driven forward-dynamic estimation of muscle force and joint moment about multiple degrees of freedom in the human lower extremity.
TL;DR: This work 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.
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Subject-specific knee joint geometry improves predictions of medial tibiofemoral contact forces
Pauline Gerus,Massimo Sartori,Thor F. Besier,Benjamin J. Fregly,Scott L. Delp,Scott A. Banks,Marcus G. Pandy,Darryl D. D'Lima,David Lloyd +8 more
TL;DR: This study investigated 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 to improve the accuracy of medial contact forces and lateral contact forces by 47% and 7%, respectively.
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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.
Claudio Pizzolato,David Lloyd,Massimo Sartori,E. Ceseracciu,Thor F. Besier,Benjamin J. Fregly,Monica Reggiani +6 more
TL;DR: 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, is created and made freely available for the research community.
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Hybrid neuromusculoskeletal modeling to best track joint moments using a balance between muscle excitations derived from electromyograms and optimization.
TL;DR: The proposed hybrid model enables muscle-driven simulations of human movement while enforcing physiological constraints on muscle excitation patterns, which might have important implications for studying pathological movement for which EMG recordings are limited.