Exploring the Contribution of Proprioceptive Reflexes to Balance Control in Perturbed Standing

Koelewijn, Anne D.; Ijspeert, Auke Jan

doi:10.3389/fbioe.2020.00866

Cited by 22 publications

(25 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, we approximated the non-Gaussian experimental platform movements by zero-mean Gaussian platform movements with a standard deviation of half the amplitude of the experimental perturbations. The variance of the translational accelerations were determined such that the healthy model for the maximal accelerations, under optimal control, reached a standard deviation of the ankle angle of 4°, a value that is typically not exceeded in continuous translation perturbation experiments [ 82 , 83 ].…”

Section: Methodsmentioning

confidence: 99%

An approximate stochastic optimal control framework to simulate nonlinear neuro-musculoskeletal models in the presence of noise

2022

View full text Add to dashboard Cite

Optimal control simulations have shown that both musculoskeletal dynamics and physiological noise are important determinants of movement. However, due to the limited efficiency of available computational tools, deterministic simulations of movement focus on accurately modelling the musculoskeletal system while neglecting physiological noise, and stochastic simulations account for noise while simplifying the dynamics. We took advantage of recent approaches where stochastic optimal control problems are approximated using deterministic optimal control problems, which can be solved efficiently using direct collocation. We were thus able to extend predictions of stochastic optimal control as a theory of motor coordination to include muscle coordination and movement patterns emerging from non-linear musculoskeletal dynamics. In stochastic optimal control simulations of human standing balance, we demonstrated that the inclusion of muscle dynamics can predict muscle co-contraction as minimal effort strategy that complements sensorimotor feedback control in the presence of sensory noise. In simulations of reaching, we demonstrated that nonlinear multi-segment musculoskeletal dynamics enables complex perturbed and unperturbed reach trajectories under a variety of task conditions to be predicted. In both behaviors, we demonstrated how interactions between task constraint, sensory noise, and the intrinsic properties of muscle influence optimal muscle coordination patterns, including muscle co-contraction, and the resulting movement trajectories. Our approach enables a true minimum effort solution to be identified as task constraints, such as movement accuracy, can be explicitly imposed, rather than being approximated using penalty terms in the cost function. Our approximate stochastic optimal control framework predicts complex features, not captured by previous simulation approaches, providing a generalizable and valuable tool to study how musculoskeletal dynamics and physiological noise may alter neural control of movement in both healthy and pathological movements.

show abstract

Section: Methodsmentioning

confidence: 99%

An approximate stochastic optimal control framework to simulate nonlinear neuro-musculoskeletal models in the presence of noise

2022

View full text Add to dashboard Cite

show abstract

“…Computational modeling studies have also been conducted using musculoskeletal models, which have more DOF than the inverted pendulum model (Jiang et al, 2016;Kaminishi et al, 2019Kaminishi et al, , 2020Koelewijn and Ijspeert, 2020). These models better represent the human anatomical structure, and they include muscle control.…”

Section: Introductionmentioning

confidence: 99%

A Neural Controller Model Considering the Vestibulospinal Tract in Human Postural Control

Omura

Kaminishi

Chiba

et al. 2022

Front. Comput. Neurosci.

View full text Add to dashboard Cite

Humans are able to control their posture in their daily lives. It is important to understand how this is achieved in order to understand the mechanisms that lead to impaired postural control in various diseases. The descending tracts play an important role in controlling posture, particularly the reticulospinal and the vestibulospinal tracts (VST), and there is evidence that the latter is impaired in various diseases. However, the contribution of the VST to human postural control remains unclear, despite extensive research using neuroscientific methods. One reason for this is that the neuroscientific approach limits our understanding of the relationship between an array of sensory information and the muscle outputs. This limitation can be addressed by carrying out studies using computational models, where it is possible to make and validate hypotheses about postural control. However, previous computational models have not considered the VST. In this study, we present a neural controller model that mimics the VST, which was constructed on the basis of physiological data. The computational model is composed of a musculoskeletal model and a neural controller model. The musculoskeletal model had 18 degrees of freedom and 94 muscles, including those of the neck related to the function of the VST. We used an optimization method to adjust the control parameters for different conditions of muscle tone and with/without the VST. We examined the postural sway for each condition. The validity of the neural controller model was evaluated by comparing the modeled postural control with (1) experimental results in human subjects, and (2) the results of a previous study that used a computational model. It was found that the pattern of results was similar for both. This therefore validated the neural controller model, and we could present the neural controller model that mimics the VST.

show abstract

“…Direct collocation reduced the optimization time to less than 1 h for three-dimensional gait simulations ( Falisse et al, 2019 ; Nitschke et al, 2020 ) and thereby greatly enhanced the possibilities for gait simulations. Recently though, advances have also been achieved using forward shooting, especially in combination with reflex models ( Ong et al, 2019 ; Koelewijn & Ijspeert, 2020 ), and nowadays this method, e.g . using the software SCONE ( Geijtenbeek, 2019 ), can also efficiently solve optimal control problems with a predefined controller structure and a relatively smaller number of optimization variables.…”

Section: Introductionmentioning

confidence: 99%

Antagonistic co-contraction can minimize muscular effort in systems with uncertainty

Koelewijn

Bogert

2022

PeerJ

Self Cite

View full text Add to dashboard Cite

Muscular co-contraction of antagonistic muscle pairs is often observed in human movement, but it is considered inefficient and it can currently not be predicted in simulations where muscular effort or metabolic energy are minimized. Here, we investigated the relationship between minimizing effort and muscular co-contraction in systems with random uncertainty to see if muscular co-contraction can minimize effort in such system. We also investigated the effect of time delay in the muscle, by varying the time delay in the neural control as well as the activation time constant. We solved optimal control problems for a one-degree-of-freedom pendulum actuated by two identical antagonistic muscles, using forward shooting, to find controller parameters that minimized muscular effort while the pendulum remained upright in the presence of noise added to the moment at the base of the pendulum. We compared a controller with and without feedforward control. Task precision was defined by bounding the root mean square deviation from the upright position, while different perturbation levels defined task difficulty. We found that effort was minimized when the feedforward control was nonzero, even when feedforward control was not necessary to perform the task, which indicates that co-contraction can minimize effort in systems with uncertainty. We also found that the optimal level of co-contraction increased with time delay, both when the activation time constant was increased and when neural time delay was added. Furthermore, we found that for controllers with a neural time delay, a different trajectory was optimal for a controller with feedforward control than for one without, which indicates that simulation trajectories are dependent on the controller architecture. Future movement predictions should therefore account for uncertainty in dynamics and control, and carefully choose the controller architecture. The ability of models to predict co-contraction from effort or energy minimization has important clinical and sports applications. If co-contraction is undesirable, one should aim to remove the cause of co-contraction rather than the co-contraction itself.

show abstract

Exploring the Contribution of Proprioceptive Reflexes to Balance Control in Perturbed Standing

Cited by 22 publications

References 49 publications

An approximate stochastic optimal control framework to simulate nonlinear neuro-musculoskeletal models in the presence of noise

An approximate stochastic optimal control framework to simulate nonlinear neuro-musculoskeletal models in the presence of noise

A Neural Controller Model Considering the Vestibulospinal Tract in Human Postural Control

Antagonistic co-contraction can minimize muscular effort in systems with uncertainty

Contact Info

Product

Resources

About