Dynamic spinal reflex adaptation during locomotor adaptation.

The dynamics and interaction of spinal and supraspinal centers during locomotor adaptation remain vaguely understood. In this work, we use Hoffmann reflex measurements to investigate changes in spinal reflex gains during split-belt locomotor adaptation. We show that spinal reflex gains are dynamically modulated during split-belt locomotor adaptation. During first exposure to split-belt transitions, modulation occurs mostly on the leg ipsilateral to the speed change and constitutes rapid suppression or facilitation of the reflex gains, followed by slow recovery to baseline. Over repeated exposure, the modulation pattern washes out. We further show that reflex gain modulation strongly correlates with correction of leg asymmetry, and cannot be explained by speed modulation solely. We argue that reflex modulation is likely of supraspinal origins and constitutes an integral part of the neural substrate underlying split-belt locomotor adaptation.NEW & NOTEWORTHY This work presents direct evidence for spinal reflex modulation during locomotor adaptation. In particular, we show that reflexes can be modulated on-demand unilaterally during split-belt locomotor adaptation and speculate about reflex modulation as an underlying mechanism for adaptation of gait asymmetry in healthy adults.

A model for the transfer of control from the brain to the spinal cord through synaptic learning.

The spinal cord is essential to the control of locomotion in legged animals and humans. However, the actual circuitry of the spinal controller remains only vaguely understood. Here we approach this problem from the viewpoint of learning. More precisely, we assume the circuitry evolves through the transfer of control from the brain to the spinal cord, propose a specific learning mechanism for this transfer based on the error between the cord and brain contributions to muscle control, and study the resulting structure of the spinal controller in a simplified neuromuscular model of human locomotion. The model focuses on the leg rebound behavior in stance and represents the spinal circuitry with 150 muscle reflexes. We find that after learning a spinal controller has evolved that produces leg rebound motions in the absence of a central brain input with only three structural reflex groups. These groups contain individual reflexes well known from physiological experiments but thought to serve separate purposes in the control of human locomotion. Our results suggest a more holistic interpretation of the role of individual sensory projections in spinal networks than is common. In addition, we discuss potential neural correlates for the proposed learning mechanism that may be probed in experiments. Together with such experiments, neuromuscular models of spinal learning likely will become effective tools for uncovering the structure and development of the spinal control circuitry.

Predictive neuromechanical simulations indicate why walking performance declines with ageing.

KEY POINTS: Although the natural decline in walking performance with ageing affects the quality of life of a growing elderly population, its physiological origins remain unknown. By using predictive neuromechanical simulations of human walking with age-related neuro-musculo-skeletal changes, we find evidence that the loss of muscle strength and muscle contraction speed dominantly contribute to the reduced walking economy and speed. The findings imply that focusing on recovering these muscular changes may be the only effective way to improve performance in elderly walking. More generally, the work is of interest for investigating the physiological causes of altered gait due to age, injury and disorders. ABSTRACT: Healthy elderly people walk slower and energetically less efficiently than young adults. This decline in walking performance lowers the quality of life for a growing ageing population, and understanding its physiological origin is critical for devising interventions that can delay or revert it. However, the origin of the decline in walking performance remains unknown, as ageing produces a range of physiological changes whose individual effects on gait are difficult to separate in experiments with human subjects. Here we use a predictive neuromechanical model to separately address the effects of common age-related changes to the skeletal, muscular and nervous systems. We find in computer simulations of this model that the combined changes produce gait consistent with elderly walking and that mainly the loss of muscle strength and mass reduces energy efficiency. In addition, we find that the slower preferred walking speed of elderly people emerges in the simulations when adapting to muscle fatigue, again mainly caused by muscle-related changes. The results suggest that a focus on recovering these muscular changes may be the only effective way to improve performance in elderly walking.

A neural circuitry that emphasizes spinal feedback generates diverse behaviours of human locomotion.

KEY POINTS: It is often assumed that central pattern generators, which generate rhythmic patterns without rhythmic inputs, play a key role in the spinal control of human locomotion. We propose a neural control model in which the spinal control generates muscle stimulations mainly through integrated reflex pathways with no central pattern generator. Using a physics-based neuromuscular human model, we show that this control network is sufficient to compose steady and transitional 3-D locomotion behaviours, including walking and running, acceleration and deceleration, slope and stair negotiation, turning, and deliberate obstacle avoidance. The results suggest feedback integration to be functionally more important than central pattern generation in human locomotion across behaviours. In addition, the proposed control architecture may serve as a guide in the search for the neurophysiological origin and circuitry of spinal control in humans. ABSTRACT: Neural networks along the spinal cord contribute substantially to generating locomotion behaviours in humans and other legged animals. However, the neural circuitry involved in this spinal control remains unclear. We here propose a specific circuitry that emphasizes feedback integration over central pattern generation. The circuitry is based on neurophysiologically plausible muscle-reflex pathways that are organized in 10 spinal modules realizing limb functions essential to legged systems in stance and swing. These modules are combined with a supraspinal control layer that adjusts the desired foot placements and selects the leg that is to transition into swing control during double support. Using physics-based simulation, we test the proposed circuitry in a neuromuscular human model that includes neural transmission delays, musculotendon dynamics and compliant foot-ground contacts. We find that the control network is sufficient to compose steady and transitional 3-D locomotion behaviours including walking and running, acceleration and deceleration, slope and stair negotiation, turning, and deliberate obstacle avoidance. The results suggest feedback integration to be functionally more important than central pattern generation in human locomotion across behaviours. In addition, the proposed control architecture may serve as a guide in the search for the neurophysiological origin and circuitry of spinal control in humans.

A neuromuscular model of human locomotion combines spinal reflex circuits with voluntary movements.

Existing models of human walking use low-level reflexes or neural oscillators to generate movement. While appropriate to generate the stable, rhythmic movement patterns of steady-state walking, these models lack the ability to change their movement patterns or spontaneously generate new movements in the specific, goal-directed way characteristic of voluntary movements. Here we present a neuromuscular model of human locomotion that bridges this gap and combines the ability to execute goal directed movements with the generation of stable, rhythmic movement patterns that are required for robust locomotion. The model represents goals for voluntary movements of the swing leg on the task level of swing leg joint kinematics. Smooth movements plans towards the goal configuration are generated on the task level and transformed into descending motor commands that execute the planned movements, using internal models. The movement goals and plans are updated in real time based on sensory feedback and task constraints. On the spinal level, the descending commands during the swing phase are integrated with a generic stretch reflex for each muscle. Stance leg control solely relies on dedicated spinal reflex pathways. Spinal reflexes stimulate Hill-type muscles that actuate a biomechanical model with eight internal joints and six free-body degrees of freedom. The model is able to generate voluntary, goal-directed reaching movements with the swing leg and combine multiple movements in a rhythmic sequence. During walking, the swing leg is moved in a goal-directed manner to a target that is updated in real-time based on sensory feedback to maintain upright balance, while the stance leg is stabilized by low-level reflexes and a behavioral organization switching between swing and stance control for each leg. With this combination of reflex-based stance leg and voluntary, goal-directed control of the swing leg, the model controller generates rhythmic, stable walking patterns in which the swing leg movement can be flexibly updated in real-time to step over or around obstacles.

Interactions Between Different Age-Related Factors Affecting Balance Control in Walking.

Maintaining balance during walking is a continuous sensorimotor control problem. Throughout the movement, the central nervous system has to collect sensory data about the current state of the body in space, use this information to detect possible threats to balance and adapt the movement pattern to ensure stability. Failure of this sensorimotor loop can lead to dire consequences in the form of falls, injury and death. Such failures tend to become more prevalent as people get older. While research has established a number of factors associated with higher risk of falls, we know relatively little about age-related changes of the underlying sensorimotor control loop and how such changes are related to empirically established risk factors. This paper approaches the problem of age-related fall risk from a neural control perspective. We begin by summarizing recent empirical findings about the neural control laws mapping sensory input to motor output for balance control during walking. These findings were established in young, neurotypical study populations and establish a baseline of sensorimotor control of balance. We then review correlates for deteriorating balance control in older adults, of muscle weakness, slow walking, cognitive decline, and increased visual dependency. While empirical associations between these factors and fall risk have been established reasonably well, we know relatively little about the underlying causal relationships. Establishing such causal relationships is hard, because the different factors all co-vary with age and are difficult to isolate empirically. One option to analyze the role of an individual factor for balance control is to use computational models of walking comprising all levels of the sensorimotor control loop. We introduce one such model that generates walking movement patterns from a short list of spinal reflex modules with limited supraspinal modulation for balance. We show how this model can be used to simulate empirical studies, and how comparison between the model and empirical results can indicate gaps in our current understanding of balance control. We also show how different aspects of aging can be added to this model to study their effect on balance control in isolation.

A Neuro-Musculo-Skeletal Model of Human Standing Combining Muscle-Reflex Control and Virtual Model Control.

While neuro-musculo-skeletal models are a common tool in theoretical studies on human gait, they are rarely used for studying human motor control of standing balance. As a result, it is difficult to assess whether proposed control strategies of standing balance can be realized by the human neuromuscular structure. Nor is it clear how the human control of standing balance interacts with that of walking. Motivated by these two shortcomings, we here develop a neuro-musculo-skeletal model of human bipedal standing whose control combines spinal muscle reflexes suggested to be important in walking with a virtual model control mimicking the supraspinal regulation of balance. We show in computer simulations that the model can reproduce several aspects of human standing balance observed in experiments on postural sway. Although control improvements are necessary to capture more aspects, the model may serve as a starting for studying the combined control of standing and walking.

A simple bipedal model for studying control of gait termination.

We study the control of human gait termination with a simple bipedal locomotion model. Several control strategies have been proposed for gait termination. However, the relative importance of these strategies has not been evaluated in models of human gait. Here we extend the bipedal spring mass walking model in a least parameter fashion and study three explicit control strategies for gait termination, including the shortening of the final step, braking at the ankle, and extending the knee. Applying the strategies separately, we find that only braking at the ankle reduces the propulsive force enough to transition into quiet standing. In combination with the other two strategies, we observe that the range of control parameters suitable for gait termination increases, especially when the ankle control is applied intermittently by taking advantage of passive stabilizing dynamics. We further show the resulting model behavior is compatible with several experimental observations about the human center of mass dynamics and leg forces during gait termination, and discuss model improvements to correct mismatches. The proposed model may serve as a starting point for more advanced models that can provide a deeper understanding of human control strategies during gait termination.

The Benefit of Combining Neuronal Feedback and Feed-Forward Control for Robustness in Step Down Perturbations of Simulated Human Walking Depends on the Muscle Function.

It is often assumed that the spinal control of human locomotion combines feed-forward central pattern generation with sensory feedback via muscle reflexes. However, the actual contribution of each component to the generation and stabilization of gait is not well understood, as direct experimental evidence for either is difficult to obtain. We here investigate the relative contribution of the two components to gait stability in a simulation model of human walking. Specifically, we hypothesize that a simple linear combination of feedback and feed-forward control at the level of the spinal cord improves the reaction to unexpected step down perturbations. In previous work, we found preliminary evidence supporting this hypothesis when studying a very reduced model of rebounding behaviors. In the present work, we investigate if the evidence extends to a more realistic model of human walking. We revisit a model that has previously been published and relies on spinal feedback control to generate walking. We extend the control of this model with a feed-forward muscle activation pattern. The feed-forward pattern is recorded from the unperturbed feedback control output. We find that the improvement in the robustness of the walking model with respect to step down perturbations depends on the ratio between the two strategies and on the muscle to which they are applied. The results suggest that combining feed-forward and feedback control is not guaranteed to improve locomotion, as the beneficial effects are dependent on the muscle and its function during walking.

Objective Assessment of Spasticity With a Method Based on a Human Upper Limb Model.

This paper presents a method based on a human upper limb model that assesses the severity of spasticity in patients with stroke objectively. The kinematic model consists of four moving segments connected by four joints. The joint torques are computed using inverse dynamics with measurements from three inertial measurement units (IMUs) attached to the participant's upper limb. The muscle activations are estimated using the joint torques via a musculoskeletal model which consists of 22 muscles. The severity of spasticity is then quantified by measuring the tonic stretch reflex threshold (TSRT) of the participant. 15 patient participants participated in the experiments where they were assessed by two qualified therapists using modified Ashworth scale (MAS), and their motions and EMG signals were captured at the same time. Using the upper limb model, the TSRT of each patient was measured and ranked. The estimated muscle activation profiles have a high correlation (0.707) to the EMG signal profiles. The null hypothesis that the rankings of the severity using the model and the MAS assessment have no correlation has been tested, and was rejected convincingly ( ). These findings suggest that the model has the potential to complement the existing practices by providing an alternative evaluation method.

An Overview on Principles for Energy Efficient Robot Locomotion.

Despite enhancements in the development of robotic systems, the energy economy of today's robots lags far behind that of biological systems. This is in particular critical for untethered legged robot locomotion. To elucidate the current stage of energy efficiency in legged robotic systems, this paper provides an overview on recent advancements in development of such platforms. The covered different perspectives include actuation, leg structure, control and locomotion principles. We review various robotic actuators exploiting compliance in series and in parallel with the drive-train to permit energy recycling during locomotion. We discuss the importance of limb segmentation under efficiency aspects and with respect to design, dynamics analysis and control of legged robots. This paper also reviews a number of control approaches allowing for energy efficient locomotion of robots by exploiting the natural dynamics of the system, and by utilizing optimal control approaches targeting locomotion expenditure. To this end, a set of locomotion principles elaborating on models for energetics, dynamics, and of the systems is studied.

Evaluation of a Neuromechanical Walking Control Model Using Disturbance Experiments.

Neuromechanical simulations have been used to study the spinal control of human locomotion which involves complex mechanical dynamics. So far, most neuromechanical simulation studies have focused on demonstrating the capability of a proposed control model in generating normal walking. As many of these models with competing control hypotheses can generate human-like normal walking behaviors, a more in-depth evaluation is required. Here, we conduct the more in-depth evaluation on a spinal-reflex-based control model using five representative gait disturbances, ranging from electrical stimulation to mechanical perturbation at individual leg joints and at the whole body. The immediate changes in muscle activations of the model are compared to those of humans across different gait phases and disturbance magnitudes. Remarkably similar response trends for the majority of investigated muscles and experimental conditions reinforce the plausibility of the reflex circuits of the model. However, the model's responses lack in amplitude for two experiments with whole body disturbances suggesting that in these cases the proposed reflex circuits need to be amplified by additional control structures such as location-specific cutaneous reflexes. A model that captures these selective amplifications would be able to explain both steady and reactive spinal control of human locomotion. Neuromechanical simulations that investigate hypothesized control models are complementary to gait experiments in better understanding the control of human locomotion.

Compliant leg behaviour explains basic dynamics of walking and running.

The basic mechanics of human locomotion are associated with vaulting over stiff legs in walking and rebounding on compliant legs in running. However, while rebounding legs well explain the stance dynamics of running, stiff legs cannot reproduce that of walking. With a simple bipedal spring-mass model, we show that not stiff but compliant legs are essential to obtain the basic walking mechanics; incorporating the double support as an essential part of the walking motion, the model reproduces the characteristic stance dynamics that result in the observed small vertical oscillation of the body and the observed out-of-phase changes in forward kinetic and gravitational potential energies. Exploring the parameter space of this model, we further show that it not only combines the basic dynamics of walking and running in one mechanical system, but also reveals these gaits to be just two out of the many solutions to legged locomotion offered by compliant leg behaviour and accessed by energy or speed.

Toward Balance Recovery With Leg Prostheses Using Neuromuscular Model Control.

OBJECTIVE: Lower limb amputees are at high risk of falling as current prosthetic legs provide only limited functionality for recovering balance after unexpected disturbances. For instance, the most established control method used on powered leg prostheses tracks local joint impedance functions without taking the global function of the leg in balance recovery into account. Here, we explore an alternative control policy for powered transfemoral prostheses that considers the global leg function and is based on a neuromuscular model of human locomotion. METHODS: We adapt this model to describe and simulate an amputee walking with a powered prosthesis using the proposed control, and evaluate the gait robustness when confronted with rough ground and swing leg disturbances. We then implement and partially evaluate the resulting controller on a leg prosthesis prototype worn by a nonamputee user. RESULTS: In simulation, the proposed prosthesis control leads to gaits that are more robust than those obtained by the impedance control method. The initial hardware experiments with the prosthesis prototype show that the proposed control reproduces normal walking patterns qualitatively and effectively responds to disturbances in early and late swing. However, the response to midswing disturbances neither replicates human responses nor averts falls. CONCLUSIONS: The neuromuscular model control is a promising alternative to existing prosthesis controls, although further research will need to improve on the initial implementation and determine how well these results transfer to amputee gait. SIGNIFICANCE: This paper provides a potential avenue for future development of control policies that help to improve amputee balance recovery.

Influence of swing leg movement on running stability.

The aim of this study was to investigate the role of the swing leg movement on running stability. A simple model was used describing a forward hopping motion. The model consisted of two sub-models, namely a spring-mass system for the stance phase and a functional control model for the swing phase (represented by a passive or actively driven pendulum). To verify the main simulation results, an experimental study on treadmill running was performed. The results of the model indicated that for certain running speeds and pendulum lengths, the behavior of the mechanical system was stable. The following characteristic dependencies between the model parameters were observed. (1) Pendulum length and hip muscle activity determined running height and therefore swing duration. (2) Horizontal velocity was inversely related to leg angle of attack. Increased speed corresponded to flatter leg angles at touch-down, which is in agreement with experimental studies and previous predictions of spring-mass running. It was shown that a biologically motivated control approach with oscillating leg movements is well capable of generating stable hopping movements. Due to its simplicity, however, the monopedal model failed to explain more detailed mechanisms like the swing-leg to stance-leg interaction or the functional role of the leg segmentation. This simple model is therefore considered as a functional mechanical template for legged locomotion, which could help to build more elaborate models in the future.

Positive force feedback in bouncing gaits?

During bouncing gaits (running, hopping, trotting), passive compliant structures (e.g. tendons, ligaments) store and release part of the stride energy. Here, active muscles must provide the required force to withstand the developing tendon strain and to compensate for the inevitable energy losses. This requires an appropriate control of muscle activation. In this study, for hopping, the potential involvement of afferent information from muscle receptors (muscle spindles, Golgi tendon organs) is investigated using a two-segment leg model with one extensor muscle. It is found that: (i) positive feedbacks of muscle-fibre length and muscle force can result in periodic bouncing; (ii) positive force feedback (F+) stabilizes bouncing patterns within a large range of stride energies (maximum hopping height of 16.3 cm, almost twofold higher than the length feedback); and (iii) when employing this reflex scheme, for moderate hopping heights (up to 8.8 cm), an overall elastic leg behaviour is predicted (hopping frequency of 1.4-3 Hz, leg stiffness of 9-27 kN m(-1)). Furthermore, F+ could stabilize running. It is suggested that, during the stance phase of bouncing tasks, the reflex-generated motor control based on feedbacks might be an efficient and reliable alternative to central motor commands.

A movement criterion for running.

The adjustment of the leg during running was addressed using a spring-mass model with a fixed landing angle of attack. The objective was to obtain periodic movement patterns. Spring-like running was monitored by a one-dimensional stride-to-stride mapping of the apex height to identify mechanically stable fixed points. We found that for certain angles of attack, the system becomes self-stabilized if the leg stiffness was properly adjusted and a minimum running speed was exceeded. At a given speed, running techniques fulfilling a stable movement pattern are characterized by an almost constant maximum leg force. With increasing speed, the leg adjustment becomes less critical. The techniques predicted for stable running are in agreement with experimental studies. Mechanically self-stabilized running requires a spring-like leg operation, a minimum running speed and a proper adjustment of leg stiffness and angle of attack. These conditions can be considered as a movement criterion for running.

Generalization of a muscle-reflex control model to 3D walking.

The neural controller that generates human locomotion can currently not be measured directly, and researchers often resort to forward dynamic simulations of the human neuromuscular system to propose and test different controller architectures. However, most of these models are restricted to locomotion in the sagittal plane, which limits the ability to study and compare proposed neural controls for 3D-related motions. Here we generalize a previously identified reflex control model for sagittal plane walking to 3D locomotion. The generalization includes additional degrees of freedom at the hips in the lateral plane, their actuation and control by hip abductor and adductor muscles, and 3D compliant ground contact dynamics. The resulting 3D model of human locomotion generates normal walking while producing human-like ground reaction forces and moments, indicating that the proposed neural controller based on muscle reflexes generalizes well to 3D locomotion.

Integration of an adaptive swing control into a neuromuscular human walking model.

Understanding the neuromuscular control underlying human locomotion has the potential to deliver practical controllers for humanoid and prosthetic robots. However, neurocontrollers developed in forward dynamic simulations are seldom applied as practical controllers due to their lack of robustness and adaptability. A key element for robust and adaptive locomotion is swing leg placement. Here we integrate a previously identified robust swing leg controller into a full neuromuscular human walking model and demonstrate that the integrated model has largely improved behaviors including walking on very rough terrain (±10 cm) and stair climbing (15 cm stairs). These initial results highlight the potential of the identified robust swing control. We plan to generalize it to a range of human locomotion behaviors critical in rehabilitation robotics.

The effect of foot compliance encoded in the windlass mechanism on the energetics of human walking.

The human foot, which is the part of the body that interacts with the environment during locomotion, consists of rich biomechanical design. One of the unique designs of human feet is the windlass mechanism. In a previous simulation study, we found that the windlass mechanism seems to improve the energy efficiency of walking. To better understand the origin of this efficiency, we here conduct both simulation and experimental studies exploring the influence of foot compliance, which is one of the functionalities that the windlass mechanism embeds, on the energetics of walking. The studies show that walking with compliant feet incurs more energetic costs than walking with stiff feet. The preliminary results suggest that the energy saved by introducing the windlass mechanism does not originate from the compliance it embeds. We speculate that the energy savings of the windlass mechanism are related more to its contribution to reducing the effective foot length in swing than to providing compliance in stance.