A muscle-reflex model that encodes principles of legged mechanics produces human walking dynamics and muscle activities

A model of task-level human stepping regulation yields semistable walking

A simple lateral dynamic walker, with swing leg dynamics and three adjustable input parameters, is used to study how motor regulation affects frontal-plane stepping. Motivated by experimental observations and phenomenological models, we imposed task-level multi-objective regulation targeting the walker's optimal lateral foot placement at each step. The regulator prioritizes achieving step width and lateral body position goals to varying degrees by choosing a mixture parameter. Our model thus integrates a lateral mechanical template, which captures the fundamental mechanics of frontal-plane walking, with a lateral motor regulation template, an empirically verified model of how humans manipulate lateral foot placements in a goal-directed manner. The model captures experimentally observed stepping fluctuation statistics and demonstrates how linear empirical models of stepping dynamics can emerge from first-principles nonlinear mechanics. We find that task-level regulation gives rise to a goal-equivalent manifold in the system's extended state space of mechanical states and inputs, a subset of which contains a continuum of period-1 gaits forming a semistable set: perturbations off of any of its gaits result in transients that return to the set, though typically to different gaits.

Myobolica: A Stochastic Approach to Estimate Physiological Muscle Control Variability

The inherent redundancy of the musculoskeletal systems is traditionally solved by optimizing a cost function. This approach may not be correct to model non-adult or pathological populations likely to adopt a "non-optimal" motor control strategy. Over the years, various methods have been developed to address this limitation, such as the stochastic approach. A well-known implementation of this approach, Metabolica, samples a wide number of plausible solutions instead of searching for a single one, leveraging Bayesian statistics and Markov Chain Monte Carlo algorithm, yet allowing muscles to abruptly change their activation levels. To overcome this and other limitations, we developed a new implementation of the stochastic approach (Myobolica), adding constraints and parameters to ensure the identification of physiological solutions. The aim of this study was to evaluate Myobolica, and quantify the differences in terms of width of the solution band (muscle control variability) compared to Metabolica. To this end, both muscle forces and knee joint force solutions bands estimated by the two approaches were compared to one another, and against (i) the solution identified by static optimization and (ii) experimentally measured knee joint forces. The use of Myobolica led to a marked narrowing of the solution band compared to Metabolica. Furthermore, the Myobolica solutions well correlated with the experimental data (R 2 = 0.92 , RMSE = 0.3 BW), but not as much with the optimal solution (R 2 = 0.82 , RMSE = 0.63 BW). Additional analyses are required to confirm the findings and further improve this implementation.

Kinematic patterns in performing trunk flexion tasks influenced by various mechanical optimization targets: A simulation study

BACKGROUND

Low back pain is the most prevalent and disabling condition worldwide, with a high recurrence rate in the general adult population.

METHODS

A set of open-sourced trunk musculoskeletal models was used to investigate trunk flexion kinematics under different motor control strategies, including minimizing shearing or compressive loads at the L4/L5 or L5/S1 level.

FINDINGS

A control strategy that minimizes the load on the lower lumbar intervertebral disc can result in two kinematic patterns-the "restricted lumbar spine" and the "overflexed lumbar spine"-in performing the trunk flexion task. The "restricted" pattern can reduce the overall load on the lower lumbar levels, whereas the "overflexed" pattern can reduce the shearing force only at the L4/L5 level and increase the compressive and shearing forces at the L5/S1 level and the compressive force at the L4/L5 level.

INTERPRETATION

This study investigated the relationships between specific trunk kinematics in patients with low back pain and lumbar intervertebral loading via musculoskeletal modelling and simulation. The results provide insight into individualized treatment for patients with low back pain.

On human-in-the-loop optimization of human-robot interaction

From industrial exoskeletons to implantable medical devices, robots that interact closely with people are poised to improve every aspect of our lives. Yet designing these systems is very challenging; humans are incredibly complex and, in many cases, we respond to robotic devices in ways that cannot be modelled or predicted with sufficient accuracy. A new approach, human-in-the-loop optimization, can overcome these challenges by systematically and empirically identifying the device characteristics that result in the best objective performance for a specific user and application. This approach has enabled substantial improvements in human-robot performance in research settings and has the potential to speed development and enhance products. In this Perspective, we describe methods for applying human-in-the-loop optimization to new human-robot interaction problems, addressing each key decision in a variety of contexts. We also identify opportunities to develop new optimization techniques and answer underlying scientific questions. We anticipate that our readers will advance human-in-the-loop optimization and use it to design robotic devices that truly enhance the human experience.

Sensory feedback and central neuronal interactions in mouse locomotion

Locomotion is a complex process involving specific interactions between the central neural controller and the mechanical components of the system. The basic rhythmic activity generated by locomotor circuits in the spinal cord defines rhythmic limb movements and their central coordination. The operation of these circuits is modulated by sensory feedback from the limbs providing information about the state of the limbs and the body. However, the specific role and contribution of central interactions and sensory feedback in the control of locomotor gait and posture remain poorly understood. We use biomechanical data on quadrupedal locomotion in mice and recent findings on the organization of neural interactions within the spinal locomotor circuitry to create and analyse a tractable mathematical model of mouse locomotion. The model includes a simplified mechanical model of the mouse body with four limbs and a central controller composed of four rhythm generators, each operating as a state machine controlling the state of one limb. Feedback signals characterize the load and extension of each limb as well as postural stability (balance). We systematically investigate and compare several model versions and compare their behaviour to existing experimental data on mouse locomotion. Our results highlight the specific roles of sensory feedback and some central propriospinal interactions between circuits controlling fore and hind limbs for speed-dependent gait expression. Our models suggest that postural imbalance feedback may be critically involved in the control of swing-to-stance transitions in each limb and the stabilization of walking direction.

Virtual muscles and reflex control generates human-like ankle torques during gait perturbations

A biologically-inspired actuation system, including muscles, spinal reflexes, and vestibular feedback, may be capable of achieving more natural gait mechanics in powered prostheses or exoskeletons. In this study, we developed a Virtual Muscle Reflex (VMR) system to control ankle torque and tuned it using data from human responses to anteroposterior mechanical perturbations at three walking speeds. The system consists of three Hill-Type muscles, simulated in real time, and uses feedback from ground reaction force and from stretch sensors on the virtual muscle fibers. Controller gains, muscle properties, and reflex/vestibular time delays were optimized using Covariance Matrix Adaptation (CMA) to minimize the difference between the VMR torque output and the torque measured from the experiment. We repeated the procedure using a conventional finite-state impedance controller. For both controllers, the coefficient of determination (R 2 ) and root-mean-square error (RMSE) was calculated as a function of time within the gait cycle. The VMR had lower RMSE than the impedance controller in 70%, and in 60% of the trials, the R 2 of the VMR controller was higher than for the impedance controller. We concluded that the VMR system can better reproduce the human responses to perturbations than the impedance controller.

The functions of leg muscles, structures and mechanisms in running

The actions of the major human leg muscles are well established; however, the functions of these muscle actions during steady running remain unclear. Here, leg structures and mechanisms are considered in terms of their functions in meeting the task of a vehicle acting as an effective machine, supporting body weight during translation with low mechanical work demand and in supplying mechanical work economically. Legs are modelled as a sequence of linkages that predict muscle actions and reveal the varying muscle functions within the integrated leg. Work avoidance is achieved with isometric muscles and linkages that promote a sliding of the hip over the ground contact, resulting in an approximately horizontal path of the centre of mass. Economical work supply requires, for muscle with constrained power, shortening over the entire stance duration; this function is achieved by the hamstrings without disrupting the linkages resulting in work avoidance. In late stance, the two functions occur through coactivation of antagonistic muscles, providing one answer to Lombard's paradox. Quadriceps and hamstring tensions result in opposing moments about both hip and knee joints, but by doing so perform the independent yet complementary roles of work avoidance during translating weight support and economical work supply.

A model of task-level human stepping regulation yields semistable walking

A simple lateral dynamic walker, with swing leg dynamics and three adjustable input parameters, is used to study how motor regulation affects frontal plane stepping. Motivated by experimental observations and phenomenological models, we imposed task-level multiobjective regulation targeting the walker's optimal lateral foot placement at each step. The regulator prioritizes achieving step width and lateral body position goals to varying degrees by choosing a mixture parameter. Our model thus integrates a lateral mechanical template, which captures fundamental mechanics of frontal-plane walking, with a lateral motor regulation template, an empirically verified model of how humans manipulate lateral foot placements in a goal-directed manner. The model captures experimentally observed stepping fluctuation statistics and demonstrates how linear empirical models of stepping dynamics can emerge from first-principles nonlinear mechanics. We find that task-level regulation gives rise to a goal equivalent manifold in the system's extended state space of mechanical states and inputs, a subset of which contains a continuum of period-1 gaits forming a semistable set: perturbations off of any of its gaits result in transients that return to the set, though typically to different gaits.

The effect of including a mobile arch, toe joint, and joint coupling on predictive neuromuscular simulations of human walking

Predictive neuromuscular simulations are a powerful tool for studying the biomechanics of human walking, and deriving design criteria for technical devices like prostheses or biorobots. Good agreement between simulation and human data is essential for transferability to the real world. The human foot is often modeled with a single rigid element, but knowledge of how the foot model affects gait prediction is limited. Standardized procedures for selecting appropriate foot models are lacking. We performed 2D predictive neuromuscular simulations with six different foot models of increasing complexity to answer two questions: What is the effect of a mobile arch, a toe joint, and the coupling of toe and arch motion through the plantar fascia on gait prediction? and How much of the foot's anatomy do we need to model to predict sagittal plane walking kinematics and kinetics in good agreement with human data? We found that the foot model had a significant impact on ankle kinematics during terminal stance, push-off, and toe and arch kinematics. When focusing only on hip and knee kinematics, rigid foot models are sufficient. We hope our findings will help guide the community in modeling the human foot according to specific research goals and improve neuromuscular simulation accuracy.

Intuitive and versatile bionic legs: a perspective on volitional control

Active lower limb prostheses show large potential to offer energetic, balance, and versatility improvements to users when compared to passive and semi-active devices. Still, their control remains a major development challenge, with many different approaches existing. This perspective aims at illustrating a future leg prosthesis control approach to improve the everyday life of prosthesis users, while providing a research road map for getting there. Reviewing research on the needs and challenges faced by prosthesis users, we argue for the development of versatile control architectures for lower limb prosthetic devices that grant the wearer full volitional control at all times. To this end, existing control approaches for active lower limb prostheses are divided based on their consideration of volitional user input. The presented methods are discussed in regard to their suitability for universal everyday control involving user volition. Novel combinations of established methods are proposed. This involves the combination of feed-forward motor control signals with simulated feedback loops in prosthesis control, as well as online optimization techniques to individualize the system parameters. To provide more context, developments related to volitional control design are touched on.

A planar neuromuscular controller to simulate compensation strategies in the sit-to-walk movement

Standing up from a chair is a key daily life activity that is sensitive to functional limitations as we age and associated with falls, frailty, and institutional living. Predictive neuromusculoskeletal models can potentially shed light on the interconnectivity and interdependency of age-related changes in neuromuscular capacity, reinforcement schemes, sensory integration, and adaptation strategies during stand-up. Most stand-up movements transfer directly into walking (sit-to-walk). The aim of this study was to develop and validate a neuromusculoskeletal model with reflex-based muscle control that enables simulation of the sit-to-walk movement under various conditions (seat height, foot placement). We developed a planar sit-to-walk musculoskeletal model (11 degrees-of-freedom, 20 muscles) and neuromuscular controller, consisting of a two-phase stand-up controller and a reflex-based gait controller. The stand-up controller contains generic neural pathways of delayed proprioceptive feedback from muscle length, force, velocity, and upper-body orientation (vestibular feedback) and includes both monosynaptic an antagonistic feedback pathways. The control parameters where optimized using a shooting-based optimization method, based on a high-level optimization criterium. Simulations were compared to recorded kinematics, ground reaction forces, and muscle activation. The simulated kinematics resemble the measured kinematics and muscle activations. The adaptation strategies that resulted from alterations in seat height, are comparable to those observed in adults. The simulation framework and model are publicly available and allow to study age-related compensation strategies, including reduced muscular capacity, reduced neural capacity, external perturbations, and altered movement objectives.

Utilising redundancy in musculoskeletal systems for adaptive stiffness and muscle failure compensation: a model-free inverse statics approach

Vertebrates possess a biomechanical structure with redundant muscles, enabling adaptability in uncertain and complex environments. Harnessing this inspiration, musculoskeletal systems offer advantages like variable stiffness and resilience to actuator failure and fatigue. Despite their potential, the complex structure presents modelling challenges that are difficult to explicitly formulate and control. This difficulty arises from the need for comprehensive knowledge of the musculoskeletal system, including details such as muscle arrangement, and fully accessible muscle and joint states. Whilst existing model-free methods do not need explicit formulations, they also underutilise the benefits of muscle redundancy. Consequently, they necessitate retraining in the event of muscle failure and require manual tuning of parameters to control joint stiffness limiting their applications under unknown payloads. Presented here is a model-free local inverse statics controller for musculoskeletal systems, employing a feedforward neural network trained on motor babbling data. Experiments with a musculoskeletal leg model showcase the controller's adaptability to complex structures, including mono and bi-articulate muscles. The controller can compensate for changes such as weight variations, muscle failures, and environmental interactions, retaining reasonable accuracy without the need for any additional retraining.

Minimization of metabolic cost of transport predicts changes in gait mechanics over a range of ankle-foot orthosis stiffnesses in individuals with bilateral plantar flexor weakness

Neuromuscular disorders often lead to ankle plantar flexor muscle weakness, which impairs ankle push-off power and forward propulsion during gait. To improve walking speed and reduce metabolic cost of transport (mCoT), patients with plantar flexor weakness are provided dorsal-leaf spring ankle-foot orthoses (AFOs). It is widely believed that mCoT during gait depends on the AFO stiffness and an optimal AFO stiffness that minimizes mCoT exists. The biomechanics behind why and how an optimal stiffness exists and benefits individuals with plantar flexor weakness are not well understood. We hypothesized that the AFO would reduce the required support moment and, hence, metabolic cost contributions of the ankle plantar flexor and knee extensor muscles during stance, and reduce hip flexor metabolic cost to initiate swing. To test these hypotheses, we generated neuromusculoskeletal simulations to represent gait of an individual with bilateral plantar flexor weakness wearing an AFO with varying stiffness. Predictions were based on the objective of minimizing mCoT, loading rates at impact and head accelerations at each stiffness level, and the motor patterns were determined via dynamic optimization. The predictive gait simulation results were compared to experimental data from subjects with bilateral plantar flexor weakness walking with varying AFO stiffness. Our simulations demonstrated that reductions in mCoT with increasing stiffness were attributed to reductions in quadriceps metabolic cost during midstance. Increases in mCoT above optimum stiffness were attributed to the increasing metabolic cost of both hip flexor and hamstrings muscles. The insights gained from our predictive gait simulations could inform clinicians on the prescription of personalized AFOs. With further model individualization, simulations based on mCoT minimization may sufficiently predict adaptations to an AFO in individuals with plantar flexor weakness.

A comprehensive dataset on biomechanics and motor control during human walking with discrete mechanical perturbations

Background

Humans have a remarkable capability to maintain balance while walking. There is, however, a lack of publicly available research data on reactive responses to destabilizing perturbations during gait.

Methods

Here, we share a comprehensive dataset collected from 10 participants who experienced random perturbations while walking on an instrumented treadmill. Each participant performed six 5-min walking trials at a rate of 1.2 m/s, during which rapid belt speed perturbations could occur during the participant's stance phase. Each gait cycle had a 17% probability of being perturbed. The perturbations consisted of an increase of belt speed by 0.75 m/s, delivered with equal probability at 10%, 20%, 30%, 40%, 50%, 60%, 70%, or 80% of the stance phase. Data were recorded using motion capture with 25 markers, eight inertial measurement units (IMUs), and electromyography (EMG) from the tibialis anterior (TA), soleus (SOL), lateral gastrocnemius (LG), rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM), biceps femoris (BF), and gluteus maximus (GM). The full protocol is described in detail.

Results

We provide marker trajectories, force plate data, EMG data, and belt speed information for all trials and participants. IMU data is provided for most participants. This data can be useful for identifying neural feedback control in human gait, biologically inspired control systems for robots, and the development of clinical applications.

Is increased trunk flexion in standing up related to muscle weakness or pain avoidance in individuals with unilateral knee pain; a simulation study

The 'Timed Up and Go' test (TUG) is a widely used clinical tool for assessing gait and balance, relying primarily on timing as a measure. However, there are more observable biomechanical compensation strategies within TUG that are indicative of underlying neuromuscular issues and movement priorities. In individuals with unilateral knee osteoarthritis, an increased trunk flexion during TUG is a common phenomenon, often attributed to muscle weakness and/or pain avoidance. Unfortunately, it is difficult to differentiate between these underlying causes using experimental studies alone. This study aimed to distinguish between muscle weakness and pain avoidance as contributing factors, using predictive neuromuscular simulations of the sit-to-walk movement. Muscle weakness was simulated by reducing the maximum isometric force of the vasti muscles (ranging from 20% to 60%), while pain avoidance was integrated as a movement objective, ensuring that peak knee load did not exceed predefined thresholds (2-4 times body weight). The simulations demonstrate that a decrease in muscular capacity led to greater trunk flexion, while pain avoidance led to slower movement speeds and altered muscle recruitments, but not to greater trunk flexion. Our predictive simulations thus indicate that increased trunk flexion is more likely the result of lack of muscular reserve rather than pain avoidance. These findings align with reported differences in kinematics and muscle activations between moderate and severe knee osteoarthritis patients, emphasizing the impact of severe muscle weakness in those with advanced knee osteoarthritis. The simulations offer valuable insights into the mechanisms behind altered movement strategies, potentially guiding more targeted treatment.

Direct Current Stimulation over the Primary Motor Cortex, Cerebellum, and Spinal Cord to Modulate Balance Performance: A Randomized Placebo-Controlled Trial

OBJECTIVES

Existing applications of non-invasive brain stimulation in the modulation of balance ability are focused on the primary motor cortex (M1). It is conceivable that other brain and spinal cord areas may be comparable or more promising targets in this regard. This study compares transcranial direct current stimulation (tDCS) over (i) the M1, (ii) the cerebellum, and (iii) trans-spinal direct current stimulation (tsDCS) in the modulation of balance ability.

METHODS

Forty-two sports students were randomized in this placebo-controlled study. Twenty minutes of anodal 1.5 mA t/tsDCS over (i) the M1, (ii) the cerebellum, and (iii) the spinal cord, as well as (iv) sham tDCS were applied to each subject. The Y Balance Test, Single Leg Landing Test, and Single Leg Squat Test were performed prior to and after each intervention.

RESULTS

The Y Balance Test showed significant improvement after real stimulation of each region compared to sham stimulation. While tsDCS supported the balance ability of both legs, M1 and cerebellar tDCS supported right leg stand only. No significant differences were found in the Single Leg Landing Test and the Single Leg Squat Test.

CONCLUSIONS

Our data encourage the application of DCS over the cerebellum and spinal cord (in addition to the M1 region) in supporting balance control. Future research should investigate and compare the effects of different stimulation protocols (anodal or cathodal direct current stimulation (DCS), alternating current stimulation (ACS), high-definition DCS/ACS, closed-loop ACS) over these regions in healthy people and examine the potential of these approaches in the neurorehabilitation.

Evaluation of controllers for augmentative hip exoskeletons and their effects on metabolic cost of walking: explicit versus implicit synchronization

Background: Efficient gait assistance by augmentative exoskeletons depends on reliable control strategies. While numerous control methods and their effects on the metabolic cost of walking have been explored in the literature, the use of different exoskeletons and dissimilar protocols limit direct comparisons. In this article, we present and compare two controllers for hip exoskeletons with different synchronization paradigms. Methods: The implicit-synchronization-based approach, termed the Simple Reflex Controller (SRC), determines the assistance as a function of the relative loading of the feet, resulting in an emerging torque profile continuously assisting extension during stance and flexion during swing. On the other hand, the Hip-Phase-based Torque profile controller (HPT) uses explicit synchronization and estimates the gait cycle percentage based on the hip angle, applying a predefined torque profile consisting of two shorter bursts of assistance during stance and swing. We tested the controllers with 23 naïve healthy participants walking on a treadmill at 4 km ⋅ h-1, without any substantial familiarization. Results: Both controllers significantly reduced the metabolic rate compared to walking with the exoskeleton in passive mode, by 18.0% (SRC, p < 0.001) and 11.6% (HPT, p < 0.001). However, only the SRC led to a significant reduction compared to walking without the exoskeleton (8.8%, p = 0.004). The SRC also provided more mechanical power and led to bigger changes in the hip joint kinematics and walking cadence. Our analysis of mechanical powers based on a whole-body analysis suggested a reduce in ankle push-off under this controller. There was a strong correlation (Pearson's r = 0.778, p < 0.001) between the metabolic savings achieved by each participant with the two controllers. Conclusion: The extended assistance duration provided by the implicitly synchronized SRC enabled greater metabolic reductions compared to the more targeted assistance of the explicitly synchronized HPT. Despite the different assistance profiles and metabolic outcomes, the correlation between the metabolic reductions with the two controllers suggests a difference in individual responsiveness to assistance, prompting more investigations to explore the person-specific factors affecting assistance receptivity.

Design of Low-Cost Modular Bio-Inspired Electric-Pneumatic Actuator (EPA)-Driven Legged Robots

Exploring the fundamental mechanisms of locomotion extends beyond mere simulation and modeling. It necessitates the utilization of physical test benches to validate hypotheses regarding real-world applications of locomotion. This study introduces cost-effective modular robotic platforms designed specifically for investigating the intricacies of locomotion and control strategies. Expanding upon our prior research in electric-pneumatic actuation (EPA), we present the mechanical and electrical designs of the latest developments in the EPA robot series. These include EPA Jumper, a human-sized segmented monoped robot, and its extension EPA Walker, a human-sized bipedal robot. Both replicate the human weight and inertia distributions, featuring co-actuation through electrical motors and pneumatic artificial muscles. These low-cost modular platforms, with considerations for degrees of freedom and redundant actuation, (1) provide opportunities to study different locomotor subfunctions-stance, swing, and balance; (2) help investigate the role of actuation schemes in tasks such as hopping and walking; and (3) allow testing hypotheses regarding biological locomotors in real-world physical test benches.

FARMS: Framework for Animal and Robot Modeling and Simulation

The study of animal locomotion and neuromechanical control offers valuable insights for advancing research in neuroscience, biomechanics, and robotics. We have developed FARMS (Framework for Animal and Robot Modeling and Simulation), an open-source, interdisciplinary framework, designed to facilitate access to neuromechanical simulations for modeling, simulation, and analysis of animal locomotion and bio-inspired robotic systems. By providing an accessible and user-friendly platform, FARMS aims to lower the barriers for researchers to explore the complex interactions between the nervous system, musculoskeletal structures, and their environment. Integrating the MuJoCo physics engine in a modular manner, FARMS enables realistic simulations and fosters collaboration among neuroscientists, biologists, and roboticists. FARMS has already been extensively used to study locomotion in animals such as mice, drosophila, fish, salamanders, and centipedes, serving as a platform to investigate the role of central pattern generators and sensory feedback. This article provides an overview of the FARMS framework, discusses its interdisciplinary approach, showcases its versatility through specific case studies, and highlights its effectiveness in advancing our understanding of locomotion. In particular, we show how we used FARMS to study amphibious locomotion by presenting experimental demonstrations across morphologies and environments based on neural controllers with central pattern generators and sensory feedback circuits models. Overall, the goal of FARMS is to contribute to a deeper understanding of animal locomotion, the development of innovative bio-inspired robotic systems, and promote accessibility in neuromechanical research.

Does crouch alter the effects of neuromuscular impairments on gait? A simulation study

Cerebral palsy (CP) is a neurologic injury that impacts control of movement. Individuals with CP also often develop secondary impairments like weakness and contracture. Both altered motor control and secondary impairments influence how an individual walks after neurologic injury. However, understanding the complex interactions between and relative effects of these impairments makes analyzing and improving walking capacity in CP challenging. We used a sagittal-plane musculoskeletal model and neuromuscular control framework to simulate crouch and nondisabled gait. We perturbed each simulation by varying the number of synergies controlling each leg (altered control), and imposed weakness and contracture. A Bayesian Additive Regression Trees (BART) model was also used to parse the relative effects of each impairment on the muscle activations required for each gait pattern. By using these simulations to evaluate gait-pattern specific effects of neuromuscular impairments, we identified some advantages of crouch gait. For example, crouch tolerated 13 % and 22 % more plantarflexor weakness than nondisabled gait without and with altered control, respectively. Furthermore, BART demonstrated that plantarflexor weakness had twice the effect on total muscle activity required during nondisabled gait than crouch gait. However, crouch gait was also disadvantageous in the presence of vasti weakness: crouch gait increased the effects of vasti weakness on gait without and with altered control. These simulations highlight gait-pattern specific effects and interactions between neuromuscular impairments. Utilizing computational techniques to understand these effects can elicit advantages of gait deviations, providing insight into why individuals may select their gait pattern and possible interventions to improve energetics.

NSF DARE-transforming modeling in neurorehabilitation: a patient-in-the-loop framework

In 2023, the National Science Foundation (NSF) and the National Institute of Health (NIH) brought together engineers, scientists, and clinicians by sponsoring a conference on computational modelling in neurorehabiilitation. To facilitate multidisciplinary collaborations and improve patient care, in this perspective piece we identify where and how computational modelling can support neurorehabilitation. To address the where, we developed a patient-in-the-loop framework that uses multiple and/or continual measurements to update diagnostic and treatment model parameters, treatment type, and treatment prescription, with the goal of maximizing clinically-relevant functional outcomes. This patient-in-the-loop framework has several key features: (i) it includes diagnostic and treatment models, (ii) it is clinically-grounded with the International Classification of Functioning, Disability and Health (ICF) and patient involvement, (iii) it uses multiple or continual data measurements over time, and (iv) it is applicable to a range of neurological and neurodevelopmental conditions. To address the how, we identify state-of-the-art and highlight promising avenues of future research across the realms of sensorimotor adaptation, neuroplasticity, musculoskeletal, and sensory & pain computational modelling. We also discuss both the importance of and how to perform model validation, as well as challenges to overcome when implementing computational models within a clinical setting. The patient-in-the-loop approach offers a unifying framework to guide multidisciplinary collaboration between computational and clinical stakeholders in the field of neurorehabilitation.

Predictive simulations identify potential neuromuscular contributors to idiopathic toe walking

BACKGROUND

Most cases of toe walking in children are idiopathic. We used pathology-specific neuromusculoskeletal predictive simulations to identify potential underlying neural and muscular mechanisms contributing to idiopathic toe walking.

METHODS

A musculotendon contracture was added to the ankle plantarflexors of a generic musculoskeletal model to represent a pathology-specific contracture model, matching the reduced ankle dorsiflexion range-of-motion in a cohort of children with idiopathic toe walking. This model was employed in a forward dynamic simulation controlled by reflexes and supraspinal drive, governed by a multi-objective cost function to predict gait patterns with the contracture model. We validated the predicted gait using experimental gait data from children with idiopathic toe walking with ankle contracture, by calculating the root mean square errors averaged over all biomechanical variables.

FINDINGS

A predictive simulation with the pathology-specific model with contracture approached experimental ITW data (root mean square error = 1.37SD). Gastrocnemius activation was doubled from typical gait simulations, but lacked a peak in early stance as present in electromyography. This synthesised idiopathic toe walking was more costly for all cost function criteria than typical gait simulation. Also, it employed a different neural control strategy, with increased length- and velocity-based reflex gains to the plantarflexors in early stance and swing than typical gait simulations.

INTERPRETATION

The simulations provide insights into how a musculotendon contracture combined with altered neural control could contribute to idiopathic toe walking. Insights into these neuromuscular mechanisms could guide future computational and experimental studies to gain improved insight into the cause of idiopathic toe walking.

Identifying essential factors for energy-efficient walking control across a wide range of velocities in reflex-based musculoskeletal systems

Humans can generate and sustain a wide range of walking velocities while optimizing their energy efficiency. Understanding the intricate mechanisms governing human walking will contribute to the engineering applications such as energy-efficient biped robots and walking assistive devices. Reflex-based control mechanisms, which generate motor patterns in response to sensory feedback, have shown promise in generating human-like walking in musculoskeletal models. However, the precise regulation of velocity remains a major challenge. This limitation makes it difficult to identify the essential reflex circuits for energy-efficient walking. To explore the reflex control mechanism and gain a better understanding of its energy-efficient maintenance mechanism, we extend the reflex-based control system to enable controlled walking velocities based on target speeds. We developed a novel performance-weighted least squares (PWLS) method to design a parameter modulator that optimizes walking efficiency while maintaining target velocity for the reflex-based bipedal system. We have successfully generated walking gaits from 0.7 to 1.6 m/s in a two-dimensional musculoskeletal model based on an input target velocity in the simulation environment. Our detailed analysis of the parameter modulator in a reflex-based system revealed two key reflex circuits that have a significant impact on energy efficiency. Furthermore, this finding was confirmed to be not influenced by setting parameters, i.e., leg length, sensory time delay, and weight coefficients in the objective cost function. These findings provide a powerful tool for exploring the neural bases of locomotion control while shedding light on the intricate mechanisms underlying human walking and hold significant potential for practical engineering applications.

Investigating the roles of reflexes and central pattern generators in the control and modulation of human locomotion using a physiologically plausible neuromechanical model

Objective.Studying the neural components regulating movement in human locomotion is obstructed by the inability to perform invasive experimental recording in the human neural circuits. Neuromechanical simulations can provide insights by modeling the locomotor circuits. Past neuromechanical models proposed control of locomotion either driven by central pattern generators (CPGs) with simple sensory commands or by a purely reflex-based network regulated by state-machine mechanisms, which activate and deactivate reflexes depending on the detected gait cycle phases. However, the physiological interpretation of these state machines remains unclear. Here, we present a physiologically plausible model to investigate spinal control and modulation of human locomotion.Approach.We propose a bio-inspired controller composed of two coupled CPGs that produce the rhythm and pattern, and a reflex-based network simulating low-level reflex pathways and Renshaw cells. This reflex network is based on leaky-integration neurons, and the whole system does not rely on changing reflex gains according to the gait cycle state. The musculoskeletal model is composed of a skeletal structure and nine muscles per leg generating movement in sagittal plane.Main results.Optimizing the open parameters for effort minimization and stability, human kinematics and muscle activation naturally emerged. Furthermore, when CPGs were not activated, periodic motion could not be achieved through optimization, suggesting the necessity of this component to generate rhythmic behavior without a state machine mechanism regulating reflex activation. The controller could reproduce ranges of speeds from 0.3 to 1.9 m s-1. The results showed that the net influence of feedback on motoneurons (MNs) during perturbed locomotion is predominantly inhibitory and that the CPGs provide the timing of MNs' activation by exciting or inhibiting muscles in specific gait phases.Significance.The proposed bio-inspired controller could contribute to our understanding of locomotor circuits of the intact spinal cord and could be used to study neuromotor disorders.

Sensory Feedback and Central Neuronal Interactions in Mouse Locomotion

Locomotion is a complex process involving specific interactions between the central neural controller and the mechanical components of the system. The basic rhythmic activity generated by locomotor circuits in the spinal cord defines rhythmic limb movements and their central coordination. The operation of these circuits is modulated by sensory feedback from the limbs providing information about the state of the limbs and the body. However, the specific role and contribution of central interactions and sensory feedback in the control of locomotor gait and posture remain poorly understood. We use biomechanical data on quadrupedal locomotion in mice and recent findings on the organization of neural interactions within the spinal locomotor circuitry to create and analyze a tractable mathematical model of mouse locomotion. The model includes a simplified mechanical model of the mouse body with four limbs and a central controller composed of four rhythm generators, each operating as a state machine controlling the state of one limb. Feedback signals characterize the load and extension of each limb as well as postural stability (balance). We systematically investigate and compare several model versions and compare their behavior to existing experimental data on mouse locomotion. Our results highlight the specific roles of sensory feedback and some central propriospinal interactions between circuits controlling fore and hind limbs for speed-dependent gait expression. Our models suggest that postural imbalance feedback may be critically involved in the control of swing-to-stance transitions in each limb and the stabilization of walking direction.

A spinal cord neuroprosthesis for locomotor deficits due to Parkinson's disease

People with late-stage Parkinson's disease (PD) often suffer from debilitating locomotor deficits that are resistant to currently available therapies. To alleviate these deficits, we developed a neuroprosthesis operating in closed loop that targets the dorsal root entry zones innervating lumbosacral segments to reproduce the natural spatiotemporal activation of the lumbosacral spinal cord during walking. We first developed this neuroprosthesis in a non-human primate model that replicates locomotor deficits due to PD. This neuroprosthesis not only alleviated locomotor deficits but also restored skilled walking in this model. We then implanted the neuroprosthesis in a 62-year-old male with a 30-year history of PD who presented with severe gait impairments and frequent falls that were medically refractory to currently available therapies. We found that the neuroprosthesis interacted synergistically with deep brain stimulation of the subthalamic nucleus and dopaminergic replacement therapies to alleviate asymmetry and promote longer steps, improve balance and reduce freezing of gait. This neuroprosthesis opens new perspectives to reduce the severity of locomotor deficits in people with PD.

Virtual pivot point: Always experimentally observed in human walking?

A main challenge in human walking is maintaining stability. One strategy to balance the whole body dynamically is to direct the ground reaction forces toward a point above the center of mass, called virtual pivot point (VPP). This strategy could be observed in various experimental studies for human and animal gait. A VPP was also observed when VPP input variables like center of mass or ground reaction forces were perturbed. In this study, the kinetic and kinematic consequences of a center of pressure manipulation and the influence on the VPP are investigated. Thus, eleven participants walked with manipulated center of pressure (i.e. barefoot, backwards, with a rigid sole, with stilts, and in handstand compared to shoe walking). In all conditions a VPP could be observed, only one participant showed no VPP in handstand walking. The vertical VPP position only differs between shoe walking and rigid sole walking, there are no significant differences between the conditions in the horizontal VPP position and the spread around the VPP. However, it is conceivable that for more severe gait changes, walking without VPP could be observed. To further analyze this issue, the authors provide a VPP calculation tool for testing data regarding the existence of the VPP.

Simulating human walking: a model-based reinforcement learning approach with musculoskeletal modeling

Introduction

Recent advancements in reinforcement learning algorithms have accelerated the development of control models with high-dimensional inputs and outputs that can reproduce human movement. However, the produced motion tends to be less human-like if algorithms do not involve a biomechanical human model that accounts for skeletal and muscle-tendon properties and geometry. In this study, we have integrated a reinforcement learning algorithm and a musculoskeletal model including trunk, pelvis, and leg segments to develop control modes that drive the model to walk.

Methods

We simulated human walking first without imposing target walking speed, in which the model was allowed to settle on a stable walking speed itself, which was 1.45 m/s. A range of other speeds were imposed for the simulation based on the previous self-developed walking speed. All simulations were generated by solving the Markov decision process problem with covariance matrix adaptation evolution strategy, without any reference motion data.

Results

Simulated hip and knee kinematics agreed well with those in experimental observations, but ankle kinematics were less well-predicted.

Discussion

We finally demonstrated that our reinforcement learning framework also has the potential to model and predict pathological gait that can result from muscle weakness.

Modeling Human Suboptimal Control: A Review

This review paper provides an overview of the approaches to model neuromuscular control, focusing on methods to identify nonoptimal control strategies typical of populations with neuromuscular disorders or children. Where possible, the authors tightened the description of the methods to the mechanisms behind the underlying biomechanical and physiological rationale. They start by describing the first and most simplified approach, the reductionist approach, which splits the role of the nervous and musculoskeletal systems. Static optimization and dynamic optimization methods and electromyography-based approaches are summarized to highlight their limitations and understand (the need for) their developments over time. Then, the authors look at the more recent stochastic approach, introduced to explore the space of plausible neural solutions, thus implementing the uncontrolled manifold theory, according to which the central nervous system only controls specific motions and tasks to limit energy consumption while allowing for some degree of adaptability to perturbations. Finally, they explore the literature covering the explicit modeling of the coupling between the nervous system (acting as controller) and the musculoskeletal system (the actuator), which may be employed to overcome the split characterizing the reductionist approach.

The History and Future of Neuromusculoskeletal Biomechanics

The Executive Council of the International Society of Biomechanics has initiated and overseen the commemorations of the Society's 50th Anniversary in 2023. This included multiple series of lectures at the ninth World Congress of Biomechanics in 2022 and XXIXth Congress of the International Society of Biomechanics in 2023, all linked to special issues of International Society of Biomechanics' affiliated journals. This special issue of the Journal of Applied Biomechanics is dedicated to the biomechanics of the neuromusculoskeletal system. The reader is encouraged to explore this special issue which comprises 6 papers exploring the current state-of the-art, and future directions and roles for neuromusculoskeletal biomechanics. This editorial presents a very brief history of the science of the neuromusculoskeletal system's 4 main components: the central nervous system, musculotendon units, the musculoskeletal system, and joints, and how they biomechanically integrate to enable an understanding of the generation and control of human movement. This also entails a quick exploration of contemporary neuromusculoskeletal biomechanics and its future with new fields of application.

Discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics

Locomotion results from the interactions of highly nonlinear neural and biomechanical dynamics. Accordingly, understanding gait dynamics across behavioral conditions and individuals based on detailed modeling of the underlying neuromechanical system has proven difficult. Here, we develop a data-driven and generative modeling approach that recapitulates the dynamical features of gait behaviors to enable more holistic and interpretable characterizations and comparisons of gait dynamics. Specifically, gait dynamics of multiple individuals are predicted by a dynamical model that defines a common, low-dimensional, latent space to compare group and individual differences. We find that highly individualized dynamics-i.e., gait signatures-for healthy older adults and stroke survivors during treadmill walking are conserved across gait speed. Gait signatures further reveal individual differences in gait dynamics, even in individuals with similar functional deficits. Moreover, components of gait signatures can be biomechanically interpreted and manipulated to reveal their relationships to observed spatiotemporal joint coordination patterns. Lastly, the gait dynamics model can predict the time evolution of joint coordination based on an initial static posture. Our gait signatures framework thus provides a generalizable, holistic method for characterizing and predicting cyclic, dynamical motor behavior that may generalize across species, pathologies, and gait perturbations.

The Bcm rule allows a spinal cord model to learn rhythmic movements

Currently, it is accepted that animal locomotion is controlled by a central pattern generator in the spinal cord. Experiments and models show that rhythm generating neurons and genetically determined network properties could sustain oscillatory output activity suitable for locomotion. However, current central pattern generator models do not explain how a spinal cord circuitry, which has the same basic genetic plan across species, can adapt to control the different biomechanical properties and locomotion patterns existing in these species. Here we demonstrate that rhythmic and alternating movements in pendulum models can be learned by a monolayer spinal cord circuitry model using the Bienenstock-Cooper-Munro learning rule, which has been previously proposed to explain learning in the visual cortex. These results provide an alternative theory to central pattern generator models, because rhythm generating neurons and genetically defined connectivity are not required in our model. Though our results are not in contradiction to current models, as existing neural mechanism and structures, not used in our model, can be expected to facilitate the kind of learning demonstrated here. Therefore, our model could be used to augment existing models.

Predicting Gait Patterns of Children With Spasticity by Simulating Hyperreflexia

Spasticity is a common impairment within pediatric neuromusculoskeletal disorders. How spasticity contributes to gait deviations is important for treatment selection. Our aim was to evaluate the pathophysiological mechanisms underlying gait deviations seen in children with spasticity, using predictive simulations. A cluster analysis was performed to extract distinct gait patterns from experimental gait data of 17 children with spasticity to be used as comparative validation data. A forward dynamic simulation framework was employed to predict gait with either velocity- or force-based hyperreflexia. This framework entailed a generic musculoskeletal model controlled by reflexes and supraspinal drive, governed by a multiobjective cost function. Hyperreflexia values were optimized to enable the simulated gait to best match experimental gait patterns. Three experimental gait patterns were extracted: (1) increased knee flexion, (2) increased ankle plantar flexion, and (3) increased knee flexion and ankle plantar flexion when compared with typical gait. Overall, velocity-based hyperreflexia outperformed force-based hyperreflexia. The first gait pattern could mostly be explained by rectus femoris and hamstrings velocity-based hyperreflexia, the second by gastrocnemius velocity-based hyperreflexia, and the third by gastrocnemius, soleus, and hamstrings velocity-based hyperreflexia. This study shows how velocity-based hyperreflexia from specific muscles contributes to different spastic gait patterns, which may help in providing targeted treatment.

The interaction between muscle pathophysiology, body mass, walking speed and ankle foot orthosis stiffness on walking energy cost: a predictive simulation study

BACKGROUND

The stiffness of a dorsal leaf AFO that minimizes walking energy cost in people with plantarflexor weakness varies between individuals. Using predictive simulations, we studied the effects of plantarflexor weakness, passive plantarflexor stiffness, body mass, and walking speed on the optimal AFO stiffness for energy cost reduction.

METHODS

We employed a planar, nine degrees-of-freedom musculoskeletal model, in which for validation maximal strength of the plantar flexors was reduced by 80%. Walking simulations, driven by minimizing a comprehensive cost function of which energy cost was the main contributor, were generated using a reflex-based controller. Simulations of walking without and with an AFO with stiffnesses between 0.9 and 8.7 Nm/degree were generated. After validation against experimental data of 11 people with plantarflexor weakness using the Root-mean-square error (RMSE), we systematically changed plantarflexor weakness (range 40-90% weakness), passive plantarflexor stiffness (range: 20-200% of normal), body mass (+ 30%) and walking speed (range: 0.8-1.2 m/s) in our baseline model to evaluate their effect on the optimal AFO stiffness for energy cost minimization.

RESULTS

Our simulations had a RMSE < 2 for all lower limb joint kinetics and kinematics except the knee and hip power for walking without AFO. When systematically varying model parameters, more severe plantarflexor weakness, lower passive plantarflexor stiffness, higher body mass and walking speed increased the optimal AFO stiffness for energy cost minimization, with the largest effects for severity of plantarflexor weakness.

CONCLUSIONS

Our forward simulations demonstrate that in individuals with bilateral plantarflexor the necessary AFO stiffness for walking energy cost minimization is largely affected by severity of plantarflexor weakness, while variation in walking speed, passive muscle stiffness and body mass influence the optimal stiffness to a lesser extent. That gait deviations without AFO are overestimated may have exaggerated the required support of the AFO to minimize walking energy cost. Future research should focus on improving predictive simulations in order to implement personalized predictions in usual care. Trial Registration Nederlands Trial Register 5170. Registration date: May 7th 2015.  http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=5170.

Novel Design and Implementation of a Neuromuscular Controller on a Hip Exoskeleton for Partial Gait Assistance

Exoskeletons intended for partial assistance of walking should be able to follow the gait pattern of their users, via online adaptive control strategies rather than imposing predefined kinetic or kinematic profiles. NeuroMuscular Controllers (NMCs) are adaptive strategies inspired by the neuromuscular modeling methods that seek to mimic and replicate the behavior of the human nervous system and skeletal muscles during gait. This study presents a novel design of a NMC, applied for the first time to partial assistance hip exoskeletons. Rather than the two-phase (stance/swing) division used in previous designs for the modulation of reflexes, a 5-state finite state machines is designed for gait phase synchronisation. The common virtual muscle model is also modified by assuming a stiff tendon, allowing for a more analytical computation approach for the muscle state resolution. As a first validation, the performance of the controller was tested with 9 healthy subjects walking at different speeds and slopes on a treadmill. The generated torque profiles show similarity to biological torques and optimal assistance profiles in the literature. Power output profiles of the exoskeleton indicate good synchronization with the users' intended movements, reflected in predominantly positive work by the assistance. The results also highlight the adaptability of the controller to different users and walking conditions, without the need for extensive parameter tuning.

Subject-Specific and COM Acceleration-Enhanced Reflex Neuromuscular Model to Predict Ankle Responses in Perturbed Gait

Subject-specific musculoskeletal models generate more accurate joint torque estimates from electromyography (EMG) inputs in relation to experimentally obtained torques. Similarly, reflex Neuromuscular Models (NMMs) that employ COM states in addition to musculotendon information generate muscle activations to musculoskeletal models that better predict ankle torques during perturbed gait. In this study, the reflex NMM of locomotion of one subject is identified by employing an EMG-calibrated musculoskeletal model in unperturbed and perturbed gait. A COM acceleration-enhanced reflex NMM is identified. Subject-specific musculoskeletal models improve torque tracking of the ankle joint in unperturbed and perturbed conditions. COM acceleration-enhanced reflex NMM improves ankle torque tracking especially in early stance and during backward perturbation. Results found herein can guide the implementation of reflex controllers in active prosthetic and orthotic devices.

Methods for integrating postural control into biomechanical human simulations: a systematic review

Understanding of the human body's internal processes to maintain balance is fundamental to simulate postural control behaviour. The body uses multiple sensory systems' information to obtain a reliable estimate about the current body state. This information is used to control the reactive behaviour to maintain balance. To predict a certain motion behaviour with knowledge of the muscle forces, forward dynamic simulations of biomechanical human models can be utilized. We aim to use predictive postural control simulations to give therapy recommendations to patients suffering from postural disorders in the future. It is important to know which types of modelling approaches already exist to apply such predictive forward dynamic simulations. Current literature provides different models that aim to simulate human postural control. We conducted a systematic literature research to identify the different approaches of postural control models. The different approaches are discussed regarding their applied biomechanical models, sensory representation, sensory integration, and control methods in standing and gait simulations. We searched on Scopus, Web of Science and PubMed using a search string, scanned 1253 records, and found 102 studies to be eligible for inclusion. The included studies use different ways for sensory representation and integration, although underlying neural processes still remain unclear. We found that for postural control optimal control methods like linear quadratic regulators and model predictive control methods are used less, when models' level of details is increasing, and nonlinearities become more important. Considering musculoskeletal models, reflex-based and PD controllers are mainly applied and show promising results, as they aim to create human-like motion behaviour considering physiological processes.

Bioinspired preactivation reflex increases robustness of walking on rough terrain

Walking on unknown and rough terrain is challenging for (bipedal) robots, while humans naturally cope with perturbations. Therefore, human strategies serve as an excellent inspiration to improve the robustness of robotic systems. Neuromusculoskeletal (NMS) models provide the necessary interface for the validation and transfer of human control strategies. Reflexes play a crucial part during normal locomotion and especially in the face of perturbations, and provide a simple, transferable, and bio-inspired control scheme. Current reflex-based NMS models are not robust to unexpected perturbations. Therefore, in this work, we propose a bio-inspired improvement of a widely used NMS walking model. In humans, different muscles show an increase in activation in anticipation of the landing at the end of the swing phase. This preactivation is not integrated in the used reflex-based walking model. We integrate this activation by adding an additional feedback loop and show that the landing is adapted and the robustness to unexpected step-down perturbations is markedly improved (from 3 to 10 cm). Scrutinizing the effect, we find that the stabilizing effect is caused by changed knee kinematics. Preactivation, therefore, acts as an accommodation strategy to cope with unexpected step-down perturbations, not requiring any detection of the perturbation. Our results indicate that such preactivation can potentially enable a bipedal system to react adequately to upcoming unexpected perturbations and is hence an effective adaptation of reflexes to cope with rough terrain. Preactivation can be ported to robots by leveraging the reflex-control scheme and improves the robustness to step-down perturbation without the need to detect the perturbation. Alternatively, the stabilizing mechanism can also be added in an anticipatory fashion by applying an additional knee torque to the contralateral knee.

Simulating the effect of ankle plantarflexion and inversion-eversion exoskeleton torques on center of mass kinematics during walking

Walking balance is central to independent mobility, and falls due to loss of balance are a leading cause of death for people 65 years of age and older. Bipedal gait is typically unstable, but healthy humans use corrective torques to counteract perturbations and stabilize gait. Exoskeleton assistance could benefit people with neuromuscular deficits by providing stabilizing torques at lower-limb joints to replace lost muscle strength and sensorimotor control. However, it is unclear how applied exoskeleton torques translate to changes in walking kinematics. This study used musculoskeletal simulation to investigate how exoskeleton torques applied to the ankle and subtalar joints alter center of mass kinematics during walking. We first created muscle-driven walking simulations using OpenSim Moco by tracking experimental kinematics and ground reaction forces recorded from five healthy adults. We then used forward integration to simulate the effect of exoskeleton torques applied to the ankle and subtalar joints while keeping muscle excitations fixed based on our previous tracking simulation results. Exoskeleton torque lasted for 15% of the gait cycle and was applied between foot-flat and toe-off during the stance phase, and changes in center of mass kinematics were recorded when the torque application ended. We found that changes in center of mass kinematics were dependent on both the type and timing of exoskeleton torques. Plantarflexion torques produced upward and backward changes in velocity of the center of mass in mid-stance and upward and smaller forward velocity changes near toe-off. Eversion and inversion torques primarily produced lateral and medial changes in velocity in mid-stance, respectively. Intrinsic muscle properties reduced kinematic changes from exoskeleton torques. Our results provide mappings between ankle plantarflexion and inversion-eversion torques and changes in center of mass kinematics which can inform designers building exoskeletons aimed at stabilizing balance during walking. Our simulations and software are freely available and allow researchers to explore the effects of applied torques on balance and gait.

Interacting effects of AFO stiffness, neutral angle and footplate stiffness on gait in case of plantarflexor weakness: A predictive simulation study

To maximize effects of dorsal leaf ankle foot orthoses (AFOs) on gait in people with bilateral plantarflexor weakness, the AFO properties should be matched to the individual. However, how AFO properties interact regarding their effect on gait function is unknown. We studied the interaction of AFO bending stiffness with neutral angle and footplate stiffness on the effect of bending stiffness on walking energy cost, gait kinematics and kinetics in people with plantarflexor weakness by employing predictive simulations. Our simulation framework consisted of a planar 11 degrees of freedom model, containing 11 muscles activated by a reflex-based neuromuscular controller. The controller was optimized by a comprehensive cost function, predominantly minimizing walking energy cost. The AFO bending and footplate stiffness were modelled as torsional springs around the ankle and metatarsal joint. The neutral angle of the AFO was defined as the angle in the sagittal plane at which the moment of the ankle torsional spring was zero. Simulations without AFO and with AFO for 9 bending stiffnesses (0-14 Nm/degree), 3 neutral angles (0-3-6 degrees dorsiflexion) and 3 footplate stiffnesses (0-0.5-2.0 Nm/degree) were performed. When changing neutral angle towards dorsiflexion, a higher AFO bending stiffness minimized energy cost of walking and normalized joint kinematics and kinetics. Footplate stiffness mainly affected MTP joint kinematics and kinetics, while no systematic and only marginal effects on energy cost were found. In conclusion, the interaction of the AFO bending stiffness and neutral angle in bilateral plantarflexor weakness, suggests that these should both be considered together when matching AFO properties to the individual patient.

Integration of feedforward and feedback control in the neuromechanics of vertebrate locomotion: a review of experimental, simulation and robotic studies

Animal locomotion is the result of complex and multi-layered interactions between the nervous system, the musculo-skeletal system and the environment. Decoding the underlying mechanisms requires an integrative approach. Comparative experimental biology has allowed researchers to study the underlying components and some of their interactions across diverse animals. These studies have shown that locomotor neural circuits are distributed in the spinal cord, the midbrain and higher brain regions in vertebrates. The spinal cord plays a key role in locomotor control because it contains central pattern generators (CPGs) - systems of coupled neuronal oscillators that provide coordinated rhythmic control of muscle activation that can be viewed as feedforward controllers - and multiple reflex loops that provide feedback mechanisms. These circuits are activated and modulated by descending pathways from the brain. The relative contributions of CPGs, feedback loops and descending modulation, and how these vary between species and locomotor conditions, remain poorly understood. Robots and neuromechanical simulations can complement experimental approaches by testing specific hypotheses and performing what-if scenarios. This Review will give an overview of key knowledge gained from comparative vertebrate experiments, and insights obtained from neuromechanical simulations and robotic approaches. We suggest that the roles of CPGs, feedback loops and descending modulation vary among animals depending on body size, intrinsic mechanical stability, time required to reach locomotor maturity and speed effects. We also hypothesize that distal joints rely more on feedback control compared with proximal joints. Finally, we highlight important opportunities to address fundamental biological questions through continued collaboration between experimentalists and engineers.

A machine learning approach to quantify individual gait responses to ankle exoskeletons

Predicting an individual's response to an exoskeleton and understanding what data are needed to characterize responses remains challenging. Specifically, we lack a theoretical framework capable of quantifying heterogeneous responses to exoskeleton interventions. We leverage a neural network-based discrepancy modeling framework to quantify complex changes in gait in response to passive ankle exoskeletons in nondisabled adults. Discrepancy modeling aims to resolve dynamical inconsistencies between model predictions and real-world measurements. Neural networks identified models of (i) Nominal gait, (ii) Exoskeleton (Exo) gait, and (iii) the Discrepancy (i.e., response) between them. If an Augmented (Nominal+Discrepancy) model captured exoskeleton responses, its predictions should account for comparable amounts of variance in Exo gait data as the Exo model. Discrepancy modeling successfully quantified individuals' exoskeleton responses without requiring knowledge about physiological structure or motor control: a model of Nominal gait augmented with a Discrepancy model of response accounted for significantly more variance in Exo gait (median R2 for kinematics (0.928-0.963) and electromyography (0.665-0.788), (p<0.042)) than the Nominal model (median R2 for kinematics (0.863-0.939) and electromyography (0.516-0.664)). However, additional measurement modalities and/or improved resolution are needed to characterize Exo gait, as the discrepancy may not comprehensively capture response due to unexplained variance in Exo gait (median R2 for kinematics (0.954-0.977) and electromyography (0.724-0.815)). These techniques can be used to accelerate the discovery of individual-specific mechanisms driving exoskeleton responses, thus enabling personalized rehabilitation.

Dysfunctional neuro-muscular mechanisms explain gradual gait changes in prodromal spastic paraplegia

BACKGROUND

In Hereditary Spastic Paraplegia (HSP) type 4 (SPG4) a length-dependent axonal degeneration in the cortico-spinal tract leads to progressing symptoms of hyperreflexia, muscle weakness, and spasticity of lower extremities. Even before the manifestation of spastic gait, in the prodromal phase, axonal degeneration leads to subtle gait changes. These gait changes - depicted by digital gait recording - are related to disease severity in prodromal and early-to-moderate manifest SPG4 participants.

METHODS

We hypothesize that dysfunctional neuro-muscular mechanisms such as hyperreflexia and muscle weakness explain these disease severity-related gait changes of prodromal and early-to-moderate manifest SPG4 participants. We test our hypothesis in computer simulation with a neuro-muscular model of human walking. We introduce neuro-muscular dysfunction by gradually increasing sensory-motor reflex sensitivity based on increased velocity feedback and gradually increasing muscle weakness by reducing maximum isometric force.

RESULTS

By increasing hyperreflexia of plantarflexor and dorsiflexor muscles, we found gradual muscular and kinematic changes in neuro-musculoskeletal simulations that are comparable to subtle gait changes found in prodromal SPG4 participants.

CONCLUSIONS

Predicting kinematic changes of prodromal and early-to-moderate manifest SPG4 participants by gradual alterations of sensory-motor reflex sensitivity allows us to link gait as a directly accessible performance marker to emerging neuro-muscular changes for early therapeutic interventions.

Assisting walking balance using a bio-inspired exoskeleton controller

BACKGROUND

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.

METHODS

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.

RESULTS

Across twelve subjects, exoskeleton support reduced calf muscle activity in steady-state walking by 19% with respect to a minimal impedance controller (p < 0.001). 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% (p = 0.006) and 16% (p < 0.001) and center of mass deviations by 9% (p = 0.026) and 18% (p = 0.002) with respect to the same controller without center of mass feedback.

CONCLUSION

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.

The neuromechanics of animal locomotion: From biology to robotics and back

Robotics and neuroscience are sister disciplines that both aim to understand how agile, efficient, and robust locomotion can be achieved in autonomous agents. Robotics has already benefitted from neuromechanical principles discovered by investigating animals. These include the use of high-level commands to control low-level central pattern generator-like controllers, which, in turn, are informed by sensory feedback. Reciprocally, neuroscience has benefited from tools and intuitions in robotics to reveal how embodiment, physical interactions with the environment, and sensory feedback help sculpt animal behavior. We illustrate and discuss exemplar studies of this dialog between robotics and neuroscience. We also reveal how the increasing biorealism of simulations and robots is driving these two disciplines together, forging an integrative science of autonomous behavioral control with many exciting future opportunities.

'Virtual pivot point' in human walking: Always experimentally observed but simulations suggest it may not be necessary for stability

The intersection of ground reaction forces near a point above the center of mass has been observed in computer simulation models and human walking experiments. Observed so ubiquitously, the intersection point (IP) is commonly assumed to provide postural stability for bipedal walking. In this study, we challenge this assumption by questioning if walking without an IP is possible. Deriving gaits with a neuromuscular reflex model through multi-stage optimization, we found stable walking patterns that show no signs of the IP-typical intersection of ground reaction forces. The non-IP gaits found are stable and successfully rejected step-down perturbations, which indicates that an IP is not necessary for locomotion robustness or postural stability. A collision-based analysis shows that non-IP gaits feature center of mass (CoM) dynamics with vectors of the CoM velocity and ground reaction force increasingly opposing each other, indicating an increased mechanical cost of transport. Although our computer simulation results have yet to be confirmed through experimental studies, they already indicate that the role of the IP in postural stability should be further investigated. Moreover, our observations on the CoM dynamics and gait efficiency suggest that the IP may have an alternative or additional function that should be considered.

A reduced-order closed-loop hybrid dynamic model for design and development of lower limb prostheses

This manuscript presents a simplified dynamic human-prosthesis model and simulation framework for the purpose of designing and developing lower limb prosthesis hardware and controllers. The objective was to provide an offline design tool to verify the closed-loop behavior of the prosthesis with the human, in order to avoid relying solely on limiting kinematic and kinetic reference trajectories of (able-bodied) subjects and associated static or inverse dynamic analyses, while not having to resort to complete neuromusculoskeletal models of the human that require extensive optimizations to run. The presented approach employs a reduced-order model that includes only the prosthetic limb and trunk in a multi-body dynamic model. External forces are applied to the trunk during stance phase of the intact leg to represent its presence. Walking is realized by employing the well-known spring-loaded inverted pendulum model, which is shown to generate realistic dynamics on the prosthesis while maintaining a stable and modifiable gait. This simple approach is inspired from the rationale that the human is adaptive, and from the desire to facilitate modifications or inclusions of additional user actions. The presented framework is validated with two use cases, featuring a commercial and research knee prosthesis in combination with a passive ankle prosthesis, performing a continuous sequence of standing still, walking at different velocities and stopping.

A Review of Current State-of-the-Art Control Methods for Lower-Limb Powered Prostheses

Lower-limb prostheses aim to restore ambulatory function for individuals with lower-limb amputations. While the design of lower-limb prostheses is important, this paper focuses on the complementary challenge - the control of lower-limb prostheses. Specifically, we focus on powered prostheses, a subset of lower-limb prostheses, which utilize actuators to inject mechanical power into the walking gait of a human user. In this paper, we present a review of existing control strategies for lower-limb powered prostheses, including the control objectives, sensing capabilities, and control methodologies. We separate the various control methods into three main tiers of prosthesis control: high-level control for task and gait phase estimation, mid-level control for desired torque computation (both with and without the use of reference trajectories), and low-level control for enforcing the computed torque commands on the prosthesis. In particular, we focus on the high- and mid-level control approaches in this review. Additionally, we outline existing methods for customizing the prosthetic behavior for individual human users. Finally, we conclude with a discussion on future research directions for powered lower-limb prostheses based on the potential of current control methods and open problems in the field.

Muscle prestimulation tunes velocity preflex in simulated perturbed hopping

Muscle fibres possess unique visco-elastic properties, which generate a stabilising zero-delay response to unexpected perturbations. This instantaneous response-termed "preflex"-mitigates neuro-transmission delays, which are hazardous during fast locomotion due to the short stance duration. While the elastic contribution to preflexes has been studied extensively, the function of fibre viscosity due to the force-velocity relation remains unknown. In this study, we present a novel approach to isolate and quantify the preflex force produced by the force-velocity relation in musculo-skeletal computer simulations. We used our approach to analyse the muscle response to ground-level perturbations in simulated vertical hopping. Our analysis focused on the preflex-phase-the first 30 ms after impact-where neuronal delays render a controlled response impossible. We found that muscle force at impact and dissipated energy increase with perturbation height, helping reject the perturbations. However, the muscle fibres reject only 15% of step-down perturbation energy with constant stimulation. An open-loop rising stimulation, observed in locomotion experiments, amplified the regulatory effects of the muscle fibre's force-velocity relation, resulting in 68% perturbation energy rejection. We conclude that open-loop neuronal tuning of muscle activity around impact allows for adequate feed-forward tuning of muscle fibre viscous capacity, facilitating energy adjustment to unexpected ground-level perturbations.

Inertia-Constrained Reinforcement Learning to Enhance Human Motor Control Modeling

Locomotor impairment is a highly prevalent and significant source of disability and significantly impacts the quality of life of a large portion of the population. Despite decades of research on human locomotion, challenges remain in simulating human movement to study the features of musculoskeletal drivers and clinical conditions. Most recent efforts to utilize reinforcement learning (RL) techniques are promising in the simulation of human locomotion and reveal musculoskeletal drives. However, these simulations often fail to mimic natural human locomotion because most reinforcement strategies have yet to consider any reference data regarding human movement. To address these challenges, in this study, we designed a reward function based on the trajectory optimization rewards (TOR) and bio-inspired rewards, which includes the rewards obtained from reference motion data captured by a single Inertial Moment Unit (IMU) sensor. The sensor was equipped on the participants' pelvis to capture reference motion data. We also adapted the reward function by leveraging previous research on walking simulations for TOR. The experimental results showed that the simulated agents with the modified reward function performed better in mimicking the collected IMU data from participants, which means that the simulated human locomotion was more realistic. As a bio-inspired defined cost, IMU data enhanced the agent's capacity to converge during the training process. As a result, the models' convergence was faster than those developed without reference motion data. Consequently, human locomotion can be simulated more quickly and in a broader range of environments, with a better simulation performance.