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

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

Affiliations

¹Centre for Musculoskeletal Research, Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia

²Department of Neurorehabilitation Engineering, University Medical Center Göttingen, Georg-August University, Göttingen, Germany

³Department of Management and Engineering, University of Padua, Vicenza, Italy

⁴Auckland Bioengineering Institute & Dept of Engineering Science, University of Auckland, Auckland, New Zealand

⁵Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, USA

Abstract and keywords

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.

Keywords: EMG-driven; EMG-informed; Neuromusculoskeletal modelling; Static optimisation

Links

DOI: 10.1016/j.jbiomech.2015.09.021

PubMed: 26522621

PubMedCentral: PMC4655131

WoS: 000366064000027

History

Accepted: 2015-09-24

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Cited By (6)

Year	Entry
2023	Savage TN, Saxby DJ, Lloyd DG, Pizzolato C. Neuromusculoskeletal model calibration accounts for differences in electromechanical delay and maximum isometric muscle force. J Biomech. March 2023;149:111503.
2023	Bu A, MacLean MK, Ferris DP. EMG-informed neuromuscular model assesses the effects of varied bodyweight support on muscles during overground walking. J Biomech. April 2023;151:111532.
2023	Kaneda JM, Seagers KA, Uhlrich SD, Kolesar JA, Thomas KA, Delp SL. Can static optimization detect changes in peak medial knee contact forces induced by gait modifications? J Biomech. May 2023;152:111569.
2023	Uhlrich SD, Uchida TK, Lee MR, Delp SL. Ten steps to becoming a musculoskeletal simulation expert: a half-century of progress and outlook for the future. J Biomech. June 2023;154:111623.
2023	Moya-Esteban A, Durandau G, van der Kooij H, Sartori M. Real-time lumbosacral joint loading estimation in exoskeleton-assisted lifting conditions via electromyography-driven musculoskeletal models. J Biomech. August 2023;157:111727.
2020	Durandau GV. Towards the Next Generation in Human-Machine-Interfacing: Controlling Wearable Robots Via Neuromusculoskeletal Modelling [PhD thesis]. University of Twente; 2020.