Biology subjects

De Groote, F.

Publications and source records attributed to De Groote, F..

At least 19 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↗

bioRxiv2026

The effects of muscle fibre type distribution on gait biomechanics: A predictive simulation study

Muscle fibre type distribution influences both metabolic and contractile properties of muscles and its influence on gait biomechanics is difficult to investigate \textit{in vivo}. This study aimed to predict the influence of muscle fibre type distribution on the metabolic cost and biomechanics of simulated gait. A muscle model that independently represented slow and fast twitch fibres was implemented in a predictive musculoskeletal simulation framework and used to investigate how cost of transport, stride length, stride frequency, and mechanical work performed by slow and fast twitch fibres were influenced by fibre type distribution across locomotion speeds (1.0 - 4.5 m {middle dot} s-1). Cost of transport decreased as proportion of slow twitch fibres increased, while stride length and frequency were minimally affected by fibre type distribution during walking. During running, cost of transport decreased and increased with increasing proportions of slow twitch fibres above and below a threshold speed, respectively. Stride frequency decreased and stride length increased with increasing proportions of slow twitch fibres above this speed. These shifts in spatiotemporal characteristics may allow key muscles to operate close to their peak mechanical efficiency. In conclusion, fibre type distribution may influence both the energetics and biomechanics of gait in a locomotion speed dependent manner.

bioengineering↗

bioRxiv2026

Experiment-free learning of exoskeleton assistance remains an unsolved problem

In "Experiment-free exoskeleton assistance via learning in simulation", Luo et al. [1] present an ambitious framework for developing exoskeleton controllers through reinforcement learning exclusively in computer simulation. The authors report that a control policy trained on a small dataset from one subject was directly transferred to physical hardware, reducing human metabolic cost during walking, running, and stair climbing by more than any prior device. If confirmed, this would represent a major breakthrough for the field of wearable robotics and their clinical applications. However, a close examination of the published materials casts doubt on these claims. The reported experimental results violate physiological limits on the relationship between mechanical power and muscle energy use during gait2,3,4. The algorithmic claims are surprising and cannot be verified; in contrast with established replicability standards in machine learning5,6, executable code has not been made available. We conclude that the goals of this study have not yet been verifiably achieved and make recommendations for avoiding publication errors of this type in the future.

physiology↗

bioRxiv2026

Stochastic optimal control simulations of walking: potential and perspective

Human walking is intrinsically variable. For example, there is considerable stride to stride variability even when walking speed is constant. This variability is due to uncertainty in the sensorimotor system and the environment, and is shaped by both musculoskeletal dynamics (e.g. joint stiffness and damping originating from muscles) and the control strategy used to mitigate the effects of uncertainty. Yet, insight into how sensorimotor noise shapes walking variability is limited due to a lack of experimental methods to assess sensorimotor noise and control strategies during walking. Simulations that account for uncertainty can elucidate how sensorimotor noise affects movement variability but due to numerical challenges, accounting for sensorimotor noise is not common in simulations of walking. Existing simulations have hugely simplified musculoskeletal dynamics (e.g. no muscles), the control policy (e.g. pre-defined feedback loops), or sensorimotor noise sources (e.g. only motor noise). Here, we performed stochastic optimal control simulations of walking based on a model with 9 degrees of freedom and 18 muscles to study how the level of sensory and motor noise influences walking. We solved for feedforward muscle excitations and full-state time-varying feedback gains that minimised expected effort while generating periodic, and hence stable, gait patterns. To enable these simulations, we approximated the state distribution with a Gaussian and used an unscented transform to propagate the state covariance. Resulting optimisation problems were solved with direct collocation. Sensorimotor noise level had a small effect on the mean kinematics but shaped kinematic and muscle activity variability as well as expected effort. Although simulations underestimated the magnitude of experimental positional variability, they captured its structure. In agreement with experimental results, the control policy prioritised limiting variability of centre of mass kinematics and minimal swing foot clearance over limiting joint angle variability. Hence, our simulations suggest that effort minimisation underlies these observations. Author summaryWhen performing a movement multiple times, each repetition will be slightly different due to random disturbances in the neural signals used to control movement, i.e. sensorimotor noise. Because it is difficult to measure inside the nervous system of a moving person, computer simulations are used to study movement control. They found that both sensorimotor noise and musculoskeletal mechanics determine how people control arm movements and standing. However, there are no simulations of walking that systematically evaluated how sensorimotor noise level influences walking kinematics because they pose computational challenges. Here, we proposed and used an approach for minimal effort simulations of walking in the presence of uncertainty. We imposed forward speed and stability but not kinematics. We found that the level of sensorimotor noise had little effect on the mean movement but a strong effect on the variability and the expected effort. The control strategy prioritised reducing the variability of the centre of mass position and swing foot clearance over reducing the variability of individual joint angles, which is also observed in experiments. Interestingly, strict control of centre of mass position and foot clearance in our simulations emerged from minimising effort.

systems biology↗

bioRxiv2026

A comparative analysis of co-contraction indices using synthetic EMG data: Implications for selection and interpretation

Co-contraction--the simultaneous activation of opposing muscles--influences movement efficiency, joint stability, and motor learning. While consensus exists for EMG acquisition and processing, comparable guidance for quantifying co-contraction is lacking. This study evaluated the behavior and interrelationships of six commonly used co-contraction indices (CCIs) to develop practical recommendations for their selection, calculation, and interpretation. Synthetic EMG-like signals were generated and used to evaluate CCI behavior across a range of conditions that would be difficult to achieve experimentally. Based on their formulas and observed behavior, CCIs were sorted into three categories: shape-based, amplitude-driven, and temporal indices. Shape-based CCIs increase when two EMG signals have similar shapes, regardless of amplitudes. Amplitude-driven CCIs increase when activation is high in both muscles. Temporal CCIs increase as the duration of EMG overlap increases, regardless of signal shape or amplitude. Correlation analyses showed stronger associations within-category than between-category, supporting the proposed classification scheme. CCI behavior yielded three principal findings, each paired with a practical recommendation. First, EMG amplitude normalization techniques altered co-contraction estimates, and the effect varied by index. Researchers should therefore test whether their conclusions hold across normalization methods. Second, because CCIs differ in scale and theoretical maxima, their values are not directly comparable across indices. Comparisons should instead focus on relative trends interpreted within each indexs bounds. Third, each CCI category was sensitive to different EMG features (e.g., amplitude versus shape). The choice of CCI should therefore align with hypothesized differences in EMG signals - use shape-based CCIs when waveform similarity is of interest, and amplitude-driven CCIs when differences in activation magnitude are expected. These results provide initial guidance for selecting, calculating, and interpretating CCIs, and they establish a framework for testing the robustness of these theoretical findings using experimental EMG from diverse tasks, muscle pairs, and populations.

bioengineering↗

bioRxiv2025

Biophysical model for predicting muscle short-range stiffness during movement

Musculoskeletal simulations can offer valuable insight into how the properties of our musculoskeletal system influence the biomechanics of our daily movements. One such property is muscles history-dependent initial resistance to stretch, also known as short-range stiffness, which is key to stabilizing movements in response to external perturbations. Short-range stiffness is poorly captured by existing musculoskeletal simulations since they employ phenomenological Hill-type muscle models that lack the mechanisms underlying short-range stiffness. While it has been previously shown that biophysical cross-bridge models can reproduce muscle short-range-stiffness, it is unclear which specific biophysical properties are necessary to capture history-dependent muscle force responses in behaviorally relevant conditions. Here, we tested the ability of various biophysical cross-bridge models to reproduce empirical short-range stiffness and its history-dependent changes across a broad range of behaviorally relevant length changes and activation levels, using an existing dataset on permeabilized rat soleus muscle fibers (N = 11). We found that a biophysical cross-bridge model with cooperative myofilament activation reproduced the effects of muscle activation (R2 = 0.86), stretch amplitude (R2 = 0.71) and isometric recovery time (R2 = 0.79) on history-dependent changes in short-range stiffness after shortening. Similar results were obtained when the cross-bridge distribution of the biophysical model was approximated by a Gaussian (R2 = 0.73 - 0.88), but at a 20 times lower computational cost. These effects could not be reproduced by either a biophysical cross-bridge model without cooperative myofilament activation or a Hill-type model (R2 < 0.5). The reduced computational demand of the Gaussian-approximated models facilitates implementing biophysical cross-bridge models with cooperative myofilament activation in musculoskeletal simulations to improve the prediction of short-range stiffness during movements.

physiology↗

bioRxiv2025

Estimating the contribution of musculoskeletal impairments to altered gait kinematics in children with cerebral palsy using predictive simulations

1.BackgroundCerebral palsy (CP) is caused by a brain lesion around birth leading to impaired motor control, bony deformities, muscle contractures, and weakness resulting in altered gait. Since the brain lesion cannot be cured, treatment aims at improving mobility. Multilevel surgery targets muscle and bony impairments but the outcome of multilevel surgery is variable and unpredictable due to our limited insight in the contribution of musculoskeletal impairments to gait alterations. MethodsHere, we used predictive simulations based on personalized musculoskeletal models to identify the contribution of musculoskeletal impairments to altered gait in eight individuals with CP scheduled for multilevel surgery. For each individual, we generated gait patterns based on eight models with different levels of personalization. We modeled muscle weakness, muscle contractures, and/or bony deformities of hip and knee and evaluated the contribution of these impairments to deviations in kinematics by comparing simulated and experimental kinematics. ResultsThe contribution of musculoskeletal impairments varied largely between participants. All modeled musculoskeletal impairments explained little to 39% of the kinematic deficits, in line with the limited and variable effect of multilevel surgery. Muscle contractures had the largest effect on the predicted kinematics. ConclusionOur results suggest an important contribution of motor control and unmodeled musculoskeletal impairments (e.g. shank and foot deformities) to alterations in the gait pattern. Model-based simulations are a promising tool to determine the contribution of musculoskeletal impairments to alterations in gait kinematics in individuals with CP.

bioengineering↗

bioRxiv2025

Smaller stepping thresholds in older adults might be related to reduced ability to suppress conflicting sensory information

Aging leads to alterations in the sensorimotor system and balance control but it is not well understood how changes in sensorimotor function affect how people respond to postural disturbances. Elucidating the relationships between balance control and sensorimotor function is crucial for developing effective rehabilitations. Here, we compared the kinematic responses to platform translations and rotations during standing in 10 young and 30 older adults and explored relationships between sensorimotor function and balance responses. We found that older adults were less able to withstand perturbations without stepping, not because their non-stepping strategies were less effective but because they chose to step at smaller deviations of the extrapolated center of mass. Older adults performed worse than young adults on measures of sensory and motor function but lower stepping thresholds were associated with susceptibility to unreliable visual information and not with reduced sensory acuity or reduced strength. Poor sensory reweighting may contribute to and combine with age-related cognitive rigidity, leading to a higher priority on safer strategies. Older adults may resort to stepping, even if a step is not necessary, rather than rely on potentially inaccurate sensory signals to inform a corrective response. Our results provide initial evidence that sensory reweighting could be a potential target for fall prevention methods. NEW & NOTEWORTHYThe relationship between age-related changes in sensorimotor function and postural control is poorly understood. Here, we did a comprehensive assessment of sensorimotor function and reactive standing balance. We found that healthy older adults chose safer strategies, i.e. they step at smaller disturbances, than young adults. Although we found many differences in sensorimotor function, only a reduced ability to suppress conflicting sensory information was related to the use of a safer strategy.

neuroscience↗

bioRxiv2025

Speeding up, not slowing down, decreases the energy needed to control walking balance

There is a metabolic cost associated with controlling walking balance but it remains unclear to what extent sagittal plane balance control contributes to this cost. Furthermore, sagittal plane balance control strategies vary with speed but it is unclear whether this also leads to a speed-dependent balance-related metabolic cost. Here, we explored the metabolic cost of stabilizing walking in the sagittal plane across speeds and its relationship with balance control strategies. To this aim, we applied continuous treadmill belt speed perturbations (standard deviation of 0.13 ms-1) to 22 healthy individuals walking at 0.8, 1.2, and 1.6 m/s. We evaluated changes in metabolic energy consumption and balance control strategies between perturbed and unperturbed walking and explored relationships between both. Perturbations induced larger increases in metabolic rate and changes in balance control strategies at slower than faster walking speeds, suggesting that walking is more robust against perturbations at faster speeds. Perturbations increased the metabolic rate by 16.7% at the slowest versus 4.6% at the fastest walking speed. When perturbed, subjects took shorter, wider, and more variable steps, and variability in ankle muscle activation increased but most changes were larger at slower speeds. Metabolic rate increased more due to perturbations in individuals who reduced step length more, i.e. relied more on anticipatory adjustments of the walking pattern. Our findings are especially relevant for explaining the increased metabolic cost of individuals with mobility impairments, who often walk slower and have altered balance control.

physiology↗

bioRxiv2024

Benchmarking the predictive capability of human gait simulations.

Physics-based simulation generate movement patterns based on a neuro-musculoskeletal model without relying on experimental movement data, offering a powerful approach to study how neuro-musculoskeletal properties shape locomotion. Yet, simulated gait patterns and metabolic powers do not always agree with experiments, pointing to modeling errors reflecting gaps in our understanding. Here, we systematically evaluated the predictive capability of simulations based on a 3D musculoskeletal model to predict gait mechanics, muscle activity and metabolic power across gait conditions. We simulated the effect of adding mass to body segments, variations in walking speed, incline walking, crouched walking. We chose tasks that are straightforward to model, ensuring that prediction errors stem from shortcomings in the neuro-musculoskeletal model. The simulations predicted stride frequency and walking kinematic with reasonable accuracy but underestimated variation in metabolic power across conditions. In particular, they underestimated changes in metabolic power with respect to level walking in tasks requiring substantial positive mechanical work, such as incline walking (27% underestimation). We identified two possible errors in simulated metabolic power. First, the Hill-type muscle model and phenomenological metabolic power model produced high maximal mechanical efficiency (average 0.58) during concentric contractions, compared to the observed 0.2-0.3 in laboratory experiments. Second, when we multiplied the mechanical work with more realistic estimates of mechanical efficiency (i.e. 0.25), simulations overestimated the metabolic power by 84%. This suggests that positive work by muscle fibers was overestimated in the simulations. This overestimation may be caused by several assumptions and errors in the musculoskeletal model including its interacting with the environment and/or its many parameters. This study highlights the need for more accurate models of muscle mechanics, energetics, and passive elastic structures to improve the realism of human movement simulations. Validating simulations across a broad range of conditions is important to pinpoint shortcomings in neuro-musculoskeletal modeling. Author summary: (non-technical summary of the work)Our research focuses on understanding how humans walk by using computer simulations. These simulations are based on detailed models, i.e. mathematical descriptions, of skeleton, muscles, joints, and control system. By comparing our simulations to actual experiments where people walked under different conditions--such as carrying extra weight, walking faster or slower, or moving uphill or downhill--we evaluated how well the simulations could predict real-life movement and energy use. We found that while the simulations performed well in predicting the walking pattern, they underestimated metabolic energy used by the body, especially in tasks like walking uphill. Errors in simulated metabolic power likely stem from two issues. First, the metabolic power model resulted in unrealistically high mechanical efficiency compared to experiment. Second, positive work (and as a result also net negative work) by muscle fibers was overestimated in the simulations. These findings highlight the need to improve the models so they can more accurately reflect the complexity of human movement and energy use. Ultimately, better models will help us design devices like exoskeletons and prosthetics and improve treatments for people with movement difficulties.

biophysics↗

bioRxiv2023

Identification of neural and non-neural origins of joint hyper-resistance based on a novel neuromechanical model.

Joint hyper-resistance is a common symptom in neurological disorders. It has both neural and nonneural origins, but it has been challenging to distinguish different origins based on clinical tests alone. Combining instrumented tests with parameter identification based on a neuromechanical model may allow us to dissociate the different origins of joint hyper-resistance in individual patients. However, this requires that the model captures the underlying mechanisms. Here, we propose a neuromechanical model that, in contrast to previously proposed models, accounts for muscle shortrange stiffness and its interaction with muscle tone and reflex activity. We collected knee angle trajectories during the pendulum test in 15 children with cerebral palsy (CP) and 5 typically developing children. We did the test in two conditions - hold and pre-movement - that have been shown to alter knee movement. We modeled the lower leg as an inverted pendulum actuated by two antagonistic Hill-type muscles extended with SRS. Reflex activity was modeled as delayed, linear feedback from muscle force. We estimated neural and non-neural parameters by optimizing the fit between simulated and measured knee angle trajectories during the hold condition. The model could fit a wide range of knee angle trajectories in the hold condition. The model with personalized parameters predicted the effect of pre-movement demonstrating that the model captured the underlying mechanism and subject-specific deficits. Our model thus allows us to determine subject-specific origins of joint hyper-resistance and thereby opens perspectives for improved diagnosis and consequently treatment selection in children with spastic CP.

pathology↗

bioRxiv2023

A dynamic foot model for predictive simulations of gait reveals causal relations between foot structure and whole body mechanics

The unique structure of the human foot is seen as a crucial adaptation for bipedalism. Its arched shape makes it possible to stiffen the foot to withstand high loads when pushing off, without compromising the range of motion. Experimental studies demonstrated that manipulating foot stiffness had considerable effects on gait. In clinical practise, altered foot structure is associated with pathological gait. Yet our understanding of how foot structure influences gait mechanics is still poor. Here we used predictive simulations to explore causal relations between foot properties and whole-body gait. Our dynamic three-segment foot model with longitudinal arch improved gait predictions compared to one- and two-segment foot models and can explain measured ankle-foot kinematics and energetics. We identified three properties of the ankle-foot complex that are crucial for healthy walking: (1) compliant Achilles tendon, (2) stiff heel pad, (3) the ability to stiffen the foot. The latter requires sufficient arch height and contributions of plantar fascia, intrinsic and extrinsic foot muscles. Insufficient foot stiffness results in walking patterns with reduced push-off power. During terminal stance plantar fascia and intrinsic foot muscles transfer energy from the metatarsophalangeal to midtarsal joint, which further increases push-off power.

bioengineering↗

bioRxiv2022

Accuracy-speed-stability trade-offs in a targeted stepping task are similar in young and older adults

BackgroundStepping accuracy, speed, and stability are lower in older compared to young adults. Lower stepping performance in older adults may be due to larger accuracy-speed-stability trade-offs because of reduced ability to simultaneously fulfill these task-level goals. Our goal was to evaluate whether trade-offs are larger in older compared to young adults in a targeted stepping task. Since sensorimotor function declines with age, our secondary goal was to evaluate whether poorer sensorimotor function was associated with larger trade-offs. Methods25 young (median 22 years old) and 25 older (median 70 years old) adults stepped into projected targets in conditions with various levels of accuracy, speed, and stability requirements. We determined trade-offs as the change in performance, i.e. foot placement error, step duration, and mediolateral center of pressure path length, between each of these conditions and a control condition. To assess age-related differences in the magnitude of trade-offs, we compared the change in performance between age groups. Associations between trade-offs and measures of sensorimotor function were tested using correlations. ResultsWe found an accuracy-speed and an accuracy-stability trade-off in both young and older adults, but trade-offs were not different between young and older adults. Inter-subject differences in sensorimotor function could not explain inter-subject differences in trade-offs. ConclusionAge-related differences in the ability to combine task-level goals do not explain why older adults stepped less accurate and less stable than young adults. However, lower stability combined with an age-independent accuracy-stability trade-off could explain lower accuracy in older adults.

neuroscience↗

bioRxiv2022

Assisting walking balance using a bio-inspired exoskeleton controller

Balance control is important for mobility, yet exoskeleton research has mainly focused on improving metabolic energy efficiency. Here we present a biomimetic exoskeleton controller that supports walking balance and reduces muscle activity. Humans restore balance after a perturbation by adjusting activity of the muscles actuating the ankle in proportion to deviations from steady-state center of mass kinematics. We designed a controller that mimics the neural control of steady-state walking and the balance recovery responses to perturbations. This controller uses both feedback from ankle kinematics in accordance with an existing model and feedback from the center of mass velocity. Control parameters were estimated by fitting the experimental relation between kinematics and ankle moments observed in humans that were walking while being perturbed by push and pull perturbations. This identified model was implemented on a bilateral ankle exoskeleton. The exoskeleton provided 30% of the estimated ankle moment during steady-state and perturbed walking. Across twelve subjects, exoskeleton support reduced calf muscle activity in steady-state walking by 19 % with respect to a minimal impedance controller. Proportional feedback of the center of mass velocity improved balance support after perturbation. Muscle activity is reduced in response to push and pull perturbations by 10 and 16 % and center of mass deviations by 9 and 18% with respect to the same controller without center of mass feedback. Our control approach implemented on bilateral ankle exoskeletons can thus effectively support steady-state walking and balance control and therefore has the potential to improve mobility in balance-impaired individuals.

biophysics↗

bioRxiv2022

Turning the head while biking makes older people lose cycling direction and balance

Older adults are more often involved in bike accidents than other age groups, especially in single-sided accidents (crashes without another road user). This suggests that older adults have more difficulty maintaining balance while cycling, likely due to age-related sensorimotor decline. A particularly challenging maneuver in traffic is the shoulder check (turning the upper body to look behind while maintaining balance and direction). This study investigates how age-related sensorimotor deficits impact this skill. 40 young (22.86 {+/-} 1.53 years) and 41 older participants (62.73 {+/-} 1.57 years) cycled in a straight lane while performing a shoulder check to identify the color of an object behind them. We recorded the task-errors (interrupted cycling, incorrect color identification, cycling outside the lane) and computed the steering angle, rotation of the frame, pelvis and torso, and task duration from inertial measurement unit data. Older adults made more task-errors than young participants; one-third failed the task due to mistakes such as misidentifying the color, losing balance, or leaving the lane. In the successful trials, older adults showed greater steering variability, increased pelvic rotation relative to the frame, and took longer to complete the task than the young participants. These findings suggest that age-related difficulties in maintaining balance while head-turning may contribute to the higher rate of single-sided bike accidents among older cyclists. Infrastructure adaptations (e.g., wider bike lanes) and individual-level interventions (e.g., mirrors) that alleviate the difficulties older adults experience when performing a shoulder check could improve safety for people from this age group.

neuroscience↗

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↗

bioRxiv2021

Modeling toes contributes to realistic stance knee mechanics in three-dimensional predictive simulations of walking

Physics-based predictive simulations have been shown to capture many salient features of human walking. Yet they often fail to produce realistic stance knee mechanics and terminal stance ankle plantarflexion. While the influence of the performance criterion on the predicted walking pattern has been previously studied, the influence of the mechanics has been less explored. Here, we investigated the influence of two mechanical assumptions on the predicted walking pattern: the complexity of the foot segment and the stiffness of the Achilles tendon. We found, through three-dimensional muscle-driven predictive simulations of walking, that modeling the toes and metatarsophalangeal joints, and thus using two-segment instead of single-segment foot models, contributed to robustly eliciting physiological stance knee flexion angles, knee extension torques, and knee extensor activity. Yet modeling toe joints did not improve ankle kinematics, nor did decreasing the Achilles tendon stiffness. The lack of predicted terminal stance ankle plantarflexion thereby remains an open question. Overall, this simulation study shows that not only the performance criterion but also mechanical assumptions affect predictive simulations of walking. Improving the realism of predictive simulations is required for their application in clinical contexts. Here, we suggest that using complex models is needed to yield such realism.

bioengineering↗

bioRxiv2020

Similar sensorimotor transformations control balance during standing and walking

Standing and walking balance control in humans relies on the transformation of sensory information to motor commands that drive muscles. Here, we evaluated whether sensorimotor transformations underlying walking balance control can be described by task-level center of mass kinematics feedback similar to standing balance control. We found that delayed feedback of center of mass position and velocity, but not local feedback of joint positions and velocities, can explain reactive ankle muscle activity and joint moments in response to perturbations of walking across protocols (discrete and continuous platform translations and discrete pelvis pushes). Feedback gains were modulated during the gait cycle and decreased with walking speed. Our results thus suggest that similar task-level variables, i.e. center of mass position and velocity, are controlled across standing and walking but that feedback gains are modulated during gait to accommodate changes in body configuration during the gait cycle and in stability with walking speed. These findings have important implications for modelling the neuromechanics of human balance control and for biomimetic control of wearable robotic devices. The feedback mechanisms we identified can be used to extend the current neuromechanical models that lack balance control mechanisms for the ankle joint. When using these models in the control of wearable robotic devices, we believe that this will facilitate shared control of balance between the user and the robotic device.

neuroscience↗