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

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.

Keywords:

• Biomechanics
• neuromechanics
• activation dynamics
• Hill-type muscle model

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

Alexandra Bayer received funding from the “Institut für Arbeitsschutz” (IFA, St. Augustin, Bonn, Germany) for a three year PhD scholarship. The research of Daniel Haeufle was supported by the Ministry of Science, Research and the Arts Baden-Württemberg [Az: 33-7533.-30-20/7/2]. Michael Günther was supported by “Berufsgenossenschaft Nahrungsmittel und Gastgewerbe, Geschäftsbereich Prävention, Mannheim” (BGN), “Cluster of Excellence Simulation Technology” (EXC310 SimTech), and Deutsche Forschungsgemeinschaft (DFG: SCHM2392/5-1), all granted to Syn Schmitt.