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 G Lloyd, Massimo Sartori, Elena Ceseracciu, Thor F. Besier, Benjamin J. Fregly, Monica Reggiani

Research output: Contribution to journalArticleAcademicpeer-review

82 Citations (Scopus)
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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.

Original languageEnglish
Pages (from-to)3929-3936
Number of pages8
JournalJournal of biomechanics
Volume48
Issue number14
DOIs
Publication statusPublished - 5 Nov 2015
Externally publishedYes

Keywords

  • EMG-driven
  • EMG-informed
  • Neuromusculoskeletal modelling
  • Static optimisation

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