Literature DB >> 28387534

The influence of biophysical muscle properties on simulating fast human arm movements.

A Bayer1,2, S Schmitt1,2, M Günther1,3, D F B Haeufle2,4.   

Abstract

Computational modeling provides a framework to understand human movement control. For this approach, physiologically motivated and experimentally validated models are required to predict the dynamic interplay of the neuronal controller with the musculoskeletal biophysics. Previous studies show, that an adequate model of arm movements should consider muscle fiber contraction dynamics, parallel and serial elasticities, and activation dynamics. Numerous validated macroscopic model representations of these structures and processes exist. In this study, the influence of these structures and processes on maximum movement velocity of goal-directed arm movements was investigated by varying their mathematical model descriptions. It was found that the movement velocity strongly depends on the pre-activation of the muscles (differences up to 91.6%) and the model representing activation dynamics (differences up to 43.3%). Looking at the influence of the active muscle fibers (contractile element), the simulations reveal that velocities systematically differ depending on the width of the force-length relation (differences up to 17.4%). The series elasticity of the tendon influences the arm velocity up to 7.6%. In conclusion, in fast goal-directed arm movements from an equilibrium position, the modeling of the biophysical muscle properties influences the simulation results. To reliably distinguish between mathematical formulations by experimental validation, the initial muscular activity and the activation dynamics have to be modeled validly, as their influence excels. To this end, further experiments systematically varying the initial muscular activity would be needed.

Entities:  

Keywords:  Biomechanics; Hill-type muscle model; activation dynamics; neuromechanics

Mesh:

Year:  2017        PMID: 28387534     DOI: 10.1080/10255842.2017.1293663

Source DB:  PubMed          Journal:  Comput Methods Biomech Biomed Engin        ISSN: 1025-5842            Impact factor:   1.763


  6 in total

1.  A systems-theoretic analysis of low-level human motor control: application to a single-joint arm model.

Authors:  Stefanie Brändle; Syn Schmitt; Matthias A Müller
Journal:  J Math Biol       Date:  2019-11-26       Impact factor: 2.259

Review 2.  A geometry- and muscle-based control architecture for synthesising biological movement.

Authors:  Johannes R Walter; Michael Günther; Daniel F B Haeufle; Syn Schmitt
Journal:  Biol Cybern       Date:  2021-02-15       Impact factor: 2.086

3.  Morphological Computation Increases From Lower- to Higher-Level of Biological Motor Control Hierarchy.

Authors:  Daniel F B Haeufle; Katrin Stollenmaier; Isabelle Heinrich; Syn Schmitt; Keyan Ghazi-Zahedi
Journal:  Front Robot AI       Date:  2020-10-21

4.  Muscles Reduce Neuronal Information Load: Quantification of Control Effort in Biological vs. Robotic Pointing and Walking.

Authors:  Daniel F B Haeufle; Isabell Wochner; David Holzmüller; Danny Driess; Michael Günther; Syn Schmitt
Journal:  Front Robot AI       Date:  2020-06-24

5.  'Falling heads': investigating reflexive responses to head-neck perturbations.

Authors:  Isabell Wochner; Lennart V Nölle; Oleksandr V Martynenko; Syn Schmitt
Journal:  Biomed Eng Online       Date:  2022-04-16       Impact factor: 3.903

6.  Implementation and validation of the extended Hill-type muscle model with robust routing capabilities in LS-DYNA for active human body models.

Authors:  Christian Kleinbach; Oleksandr Martynenko; Janik Promies; Daniel F B Haeufle; Jörg Fehr; Syn Schmitt
Journal:  Biomed Eng Online       Date:  2017-09-02       Impact factor: 2.819

  6 in total

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