Biology subjects

Van Wouwe, T.

Publications and source records attributed to Van Wouwe, T..

3 recordsLinked to original sources

bioRxiv2026

Simulations predict that alterations in balance control contribute to the higher cost of walking with a prosthesis

The high metabolic cost of walking in individuals with a transtibial amputation is often attributed to reduced ankle propulsion. However, the cost of stabilizing walking may also increase after amputation, potentially explaining why restoring ankle propulsion does not always normalize metabolic cost. Because the contributions of propulsion and stabilization are difficult to dissociate experimentally, we used simulations to probe the effects of transtibial amputation and different prostheses on walking cost. We simulated gait using a conceptual planar torque-driven model. Leg joint torques were controlled by feedforward and linear, time-varying full state feedback. We computed feedforward torques associated with propulsion and feedback gains for stabilizing walking that minimized expected effort (sum of torques squared) in the presence of sensorimotor noise while imposing stability. We simulated walking for an intact model and three walkers with a prosthesis: a passive, a feedforward-controlled active, and a feedforward and local feedback-controlled active prosthesis. We solved a deterministic approximation of the resultant stochastic optimal control problems for different levels of sensorimotor noise. Feedforward and expected feedback torques were higher for the passive prosthesis walker than for the intact walker. An active prosthesis restored total biological effort (i.e., effort of all but the prosthesis joint) to the level of the intact walker, but this effort was generated by fewer biological joints. Biological feedback effort was higher in prosthesis walkers than in the intact walker. Adding local feedback to an active prosthesis reduced biological feedback effort, but only at low noise levels. Our results suggest that walking with a passive prosthesis increases effort related to both propulsion and stabilization. While active prostheses can support propulsion, local feedback control only marginally reduced stabilization-related effort, suggesting that controllers require sensory information from the rest of the body to fully restore the metabolic cost of walking.

systems biology↗

bioRxiv2023

Simulated musculoskeletal optimization for sprinting and marathon running

Musculoskeletal geometry and muscle volumes vary widely in the population and are intricately linked to the performance of tasks ranging from walking and running to jumping and sprinting. However, our ability to understand how these parameters affect task performance has been limited due to the high computational cost of modelling the necessary complexity of the musculoskeletal system and solving the requisite multi-dimensional optimization problem. For example, sprinting and running are fundamental to many forms of sport, but past research on the relationships between musculoskeletal geometry, muscle volumes, and running performance has been limited to observational studies, which have not established cause-effect relationships, and simulation studies with simplified representations of musculoskeletal geometry. In this study, we developed a novel musculoskeletal simulator that is differentiable with respect to musculoskeletal geometry and muscle volumes. This simulator enabled us to find the optimal body segment dimensions and optimal distribution of added muscle volume for sprinting and marathon running. Our simulation results replicate experimental observations, such as increased muscle mass in sprinters, as well a mass in the lower end of the healthy BMI range and a higher leg-length-to-height ratio in marathon runners. The simulations also reveal new relationships, for example showing that hip musculature is vital to both sprinting and marathon running. We found hip flexor and extensor moment arms were maximized to optimize sprint and marathon running performance, and hip muscles the main target when we simulated strength training for sprinters. Our simulation results can help sprint and marathon runners customize strength training, and our simulator can be extended to other athletic tasks, such as jumping, or to non-athletic applications, such as designing interventions to improve mobility in older adults or individuals with movement disorders. AUTHOR SUMMARYOur study addresses the challenge of determining optimal musculoskeletal parameters for tasks like sprinting and marathon running. Existing research has been limited to observational studies and simplified simulations. To overcome these limitations, we developed a differentiable musculoskeletal simulator to optimize running performance. We replicated past findings and uncovered new insights. We confirmed the benefits of increased muscle mass for sprinters and identified key factors for marathon runners, such a mass in the lower end of the healthy BMI range and an increased leg-length-to-height ratio. Hip musculature was found to be critical for both sprinting and marathon running. Our simulation results have practical implications. They can inform customized strength training for sprinters and marathon runners. Additionally, the simulator can be extended to other athletic tasks, benefiting various sporting events. Beyond athletics, our open-source simulator has broader applications. It can determine minimal strength requirements for daily activities, guide strength training in the elderly, and estimate the effects of simulated musculoskeletal surgery.

bioengineering↗

bioRxiv2021

An approximate stochastic optimal control framework to simulate nonlinear neuromusculoskeletal models in the presence of noise

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.

neuroscience↗