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.

The dynamics of the skeletal muscle: A systems biophysics perspective on muscle modeling with the focus on Hill‐type muscle models

“…According to literature, the closed‐loop reflex model may take as input a desired fiber length , the actual fiber length , and the fiber contraction velocity …”

“…According to literature, [17][18][19][20][21] the closed-loop reflex model may take as input a desired fiber length , the actual fiber length , and the fiber contraction velocitẏ=…”

Towards overcoming the bottleneck of optimizing control parameters in finite element active human body models

“…In this way, an optimization problem is defined with a cost function minimizing the sum of all muscle stimulations and the constraint of an equilibrium [2]. The open-loop stimulation u open i and the desired lengths and velocities of the muscle contractile elements (λ i andλ i ) define the equilibrium positions and need to be determined by optimization.…”

“…In previous works [1][2][3], we presented a muscle control algorithm for aHBMs based on intermittent equilibrium point control implemented in the FE software code LS-DYNA. To make such a model biofidelic, one needs to implement musculoskeletal systems and their control schemes in commercial software codes used in the automotive industry.…”

“…To make such a model biofidelic, one needs to implement musculoskeletal systems and their control schemes in commercial software codes used in the automotive industry. In previous works [1][2][3], we presented a muscle control algorithm for aHBMs based on intermittent equilibrium point control implemented in the FE software code LS-DYNA. Due to the fact that the mapping between muscle stimulations and the body motion itself is highly non-linear and complex -because of actuation and kinematics redundancy -it is necessary to optimise the parameters for the open-loop central nervous system command and the closed-loop reflex signal before running the simulation.…”