Biology subjects

Ton, V.

Publications and source records attributed to Ton, V..

2 recordsLinked to original sources

bioRxiv2026

Unified comparison of spinal locomotion control architectures in neuromechanical simulations

Neuromechanical simulations provide a powerful framework for investigating how neural control architectures generate and regulate human locomotion. Numerous biologically inspired locomotion controllers have been proposed, including reflex-based, central pattern generator (CPG)-based, and muscle synergy-based models. However, direct comparison across studies remains difficult because of differences in musculoskeletal models, optimization methods, and evaluation protocols. Here, we implemented four representative locomotion control architectures, reflex-based, CPG-reflex-based, muscle synergy-based, and CPG-reflex-synergy-based controllers, within a unified neuromechanical simulation framework to enable controlled comparisons under shared biomechanical and computational conditions. Performance was assessed in terms of (1) agreement with experimentally observed gait characteristics, including kinematics, kinetics, muscle activations, and biomechanical trends across speeds and slopes, and (2) locomotor versatility across speed-slope conditions. The reflex-based and CPG-reflex-synergy-based controllers most closely reproduced experimentally observed gait characteristics, while the CPG-reflex-synergy controller achieved the broadest range of stable walking behaviors across speeds and slopes, followed closely by the reflex-based controller. These findings should be interpreted as comparisons of specific model implementations rather than definitive evaluations of the underlying biological hypotheses. Moreover, because the investigated controllers primarily focused on spinal-level mechanisms for nominal steady-state locomotion, the limited versatility observed in some of the models across broader speed and slope conditions suggests the importance of integrating spinal locomotor mechanisms with supraspinal modulation when modeling locomotion beyond nominal steady gait. To facilitate further investigation, we publicly share the simulation framework and controller implementations. Key pointsO_LIExisting neuromechanical locomotion controllers have been difficult to compare directly because of differences in simulation frameworks. C_LIO_LIWe implemented four representative spinal locomotion control models (reflex-based, central pattern generator (CPG)-reflex-based, muscle synergy-based, and CPG-reflex-synergy-based) within a unified simulation framework and compared their human-likeness and versatility. C_LIO_LIThe reflex-based and CPG-reflex-synergy-based controllers best reproduced human-like gait characteristics, while the CPG-reflex-synergy-based controller demonstrated the greatest locomotor versatility across speed-slope conditions, followed closely by the reflex-based controller. C_LIO_LIBecause the investigated controllers primarily modeled spinal-level mechanisms associated with steady-state locomotion, their reduced adaptability across broader speed and slope conditions highlights the importance of incorporating supraspinal modulation when modeling locomotion beyond nominal gait. C_LIO_LIWe publicly share the simulation framework and controller implementations to support further investigation of human locomotion control. C_LI

physiology↗

bioRxiv2026

Predictive Neuromechanical Simulation Explains Gait Biomechanics in Obesity

Individuals with obesity exhibit gait adaptations including reduced early-stance knee flexion, altered muscle coordination, slower preferred walking speeds, and shorter step lengths. Although these features are well documented, the mechanisms by which obesity-related physiological changes produce these patterns and influence knee joint loading relevant to osteoarthritis (OA) remain unclear. This study used predictive neuromechanical simulation to examine how musculoskeletal changes and movement objectives interact to generate obesity-associated gait patterns and tibiofemoral loading. Predictive simulations were performed using a reflex-based neuromechanical walking model. A baseline non-obese model (1.8 m, 80 kg) was modified to represent obesity-related changes in segment mass distribution and muscle strength (1.8 m, 140 kg), including more apple-like and more pear-like body mass distributions. Control parameters were optimized to generate stable walking while minimizing muscle effort and tibiofemoral joint loading. Objective weightings were identified by matching simulated knee kinematics to experimental observations at a typical walking speed. Using the selected weightings, we compared joint kinematics, kinetics, and muscle activations, and simulations were performed across walking speeds to evaluate optimal walking speed, step length, muscle effort, and knee loading. The baseline model best matched reference knee kinematics using a muscle-effort objective alone, whereas the obese model required a combined objective penalizing both muscle effort and knee loading. This formulation reproduced key gait features, including reduced early-stance knee flexion, reduced vastii activation with increased plantarflexor activation, slower optimal walking speeds, and shorter step lengths. Variations in body mass distribution produced moderate but consistent effects on gait mechanics relative to larger effects of increased body mass. Obesity-related changes in body mass and muscle strength alone did not reproduce observed gait patterns, but incorporating an objective that penalizes knee loading generated multiple characteristic features. Predictive neuromechanical simulation provides a framework for identifying candidate mechanisms linking obesity, gait biomechanics, and knee joint loading. Author SummaryUnderstanding how and why obesity alters gait is a complex biomechanical problem involving multiple interacting factors including increased segmental mass, altered inertial properties, and reduced relative muscle strength. These factors interact in ways that are difficult to isolate through experimental observation alone. Here, we used computer simulations to examine how musculoskeletal changes and movement objectives interact to generate obesity-associated gait patterns and knee loading. We found that physiological changes alone did not reproduce observed gait features, whereas incorporating an objective that penalizes knee loading generated multiple characteristic features simultaneously, including reduced early-stance knee flexion, altered muscle coordination, slower optimal walking speeds, and shorter step lengths. These findings suggest that obesity-associated gait reflects coordination strategies that regulate knee loading under increased body mass.

systems biology↗