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.
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.
authors.affiliation: Centre for Musculoskeletal Research, Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia.
authors.forename: Claudio
authors.initials: C
authors.lastname: Pizzolato
authors.affiliation: Centre for Musculoskeletal Research, Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia. Electronic address: david.lloyd@griffith.edu.au.
authors.forename: David G
authors.initials: DG
authors.lastname: Lloyd
authors.affiliation: Department of Neurorehabilitation Engineering, University Medical Center Göttingen, Georg-August University, Göttingen, Germany.
authors.forename: Massimo
authors.initials: M
authors.lastname: Sartori
authors.affiliation: Department of Management and Engineering, University of Padua, Vicenza, Italy.
authors.forename: Elena
authors.initials: E
authors.lastname: Ceseracciu
authors.affiliation: Auckland Bioengineering Institute & Dept of Engineering Science, University of Auckland, Auckland, New Zealand.
authors.forename: Thor F
authors.initials: TF
authors.lastname: Besier
authors.affiliation: Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, USA.
authors.forename: Benjamin J
authors.initials: BJ
authors.lastname: Fregly
authors.affiliation: Department of Management and Engineering, University of Padua, Vicenza, Italy. Electronic address: monica.reggiani@unipd.it.
authors.forename: Monica
authors.initials: M
authors.lastname: Reggiani
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004576:Electromyography; D056966:Excitation Contraction Coupling; D006801:Humans; D007596:Joints / Q000502:physiology*; D008954:Models, Biological; D009068:Movement / Q000502:physiology; D009119:Muscle Contraction / Q000502:physiology; D018482:Muscle, Skeletal / Q000502:physiology*
keywords: EMG-driven; EMG-informed; Neuromusculoskeletal modelling; Static optimisation