Neuromusculoskeletal models based on the muscle synergy hypothesis for the investigation of adaptive motor control in locomotion via sensory-motor coordination

A spinal circuit model with asymmetric cervical-lumbar layout controls backward locomotion and scratching in quadrupeds

Locomotion and scratching are basic motor functions which are critically important for animal survival. Although the spinal circuits governing forward locomotion have been extensively investigated, the organization of spinal circuits and neural mechanisms regulating backward locomotion and scratching remain unclear. Here, we extend a model by Danner et al. to propose a spinal circuit model with asymmetrical cervical-lumbar layout to investigate these issues. In the model, the left-right alternation within the cervical and lumbar circuits is mediated by V 0D and V 0V commissural interneurons (CINs), respectively. With different control strategies, the model closely reproduces multiple experimental data of quadrupeds in different motor behaviors. Specifically, under the supraspinal drive, walk and trot are expressed in control condition, half-bound is expressed after deletion of V 0V CINs, and bound is expressed after deletion of V0 (V 0D and V 0V) CINs; in addition, unilateral hindlimb scratching occurs in control condition and synchronous bilateral hindlimb scratching appears after deletion of V 0V CINs. Under the combined drive of afferent feedback and perineal stimulation, different coordination patterns between hindlimbs during BBS (backward-biped-spinal) locomotion are generated. The results suggest that (1) the cervical and lumbar circuits in the spinal network are asymmetrically recruited during particular rhythmic limb movements. (2) Multiple motor behaviors share a single spinal network under the reconfiguration of the spinal network by supraspinal inputs or somatosensory feedback. Our model provides new insights into the organization of motor circuits and neural control of rhythmic limb movements.

Inhibitory circuits generate rhythms for leg movements during Drosophila grooming

Limbs execute diverse actions coordinated by the nervous system through multiple motor programs. The basic architecture of motor neurons that activate muscles that articulate joints for antagonistic flexion and extension movements is conserved from flies to vertebrates. While excitatory premotor circuits are expected to establish sets of leg motor neurons that work together, our study uncovered a new instructive role for inhibitory circuits: their ability to generate rhythmic leg movements. Using electron microscopy data for the Drosophila nerve cord, we categorized ~120 GABAergic inhibitory neurons from the 13A and 13B hemi-lineages into classes based on similarities in morphology and connectivity. By mapping their synaptic partners, we uncovered pathways for inhibiting specific groups of motor neurons, disinhibiting antagonistic counterparts, and inducing alternation between flexion and extension. We tested the function of specific inhibitory neurons through optogenetic activation and silencing, using an in-depth ethological analysis of leg movements during grooming. We combined anatomy and behavior analysis findings to construct a computational model that can reproduce major aspects of the observed behavior, confirming the sufficiency of these premotor inhibitory circuits to generate rhythms.

Neonatal resveratrol treatment in cerebral palsy model recovers neurodevelopment impairments by restoring the skeletal muscle morphology and decreases microglial activation in the cerebellum

Cerebral Palsy (CP) is the main motor disorder in childhood resulting from damage to the developing brain. Treatment perspectives are required to reverse the primary damage caused by the early insult and consequently to recover motor skills. Resveratrol has been shown to act as neuroprotection with benefits to skeletal muscle. This study aimed to investigate the effects of neonatal resveratrol treatment on neurodevelopment, skeletal muscle morphology, and cerebellar damage in CP model. Wistar rat pups were allocated to four experimental groups (n = 15/group) according CP model and treatment: Control+Saline (CS), Control+Resveratrol (CR), CP + Saline (CPS), and CP + Resveratrol (CPR). CP model associated anoxia and sensorimotor restriction. CP group showed delay in the disappearance of the palmar grasp reflex (p < 0.0001) and delay in the appearance of reflexes of negative geotaxis (p = 0.01), and free-fall righting (p < 0.0001), reduced locomotor activity and motor coordination (p < 0.05) than CS group. These motor skills impairments were associated with a reduction in muscle weight (p < 0.001) and area and perimeter of soleus end extensor digitorum longus muscle fibers (p < 0.0001), changes in muscle fibers typing pattern (p < 0.05), and the cerebellum showed signs of neuroinflammation due to elevated density and percentage of activated microglia in the CPS group compared to CS group (p < 0.05). CP animals treated with resveratrol showed anticipation of the appearance of negative geotaxis and free-fall righting reflexes (p < 0.01), increased locomotor activity (p < 0.05), recovery muscle fiber types pattern (p < 0.05), and reversal of the increase in density and the percentage of activated microglia in the cerebellum (p < 0.01). Thus, we conclude that neonatal treatment with resveratrol can contribute to the recovery of the delay neurodevelopment resulting from experimental CP due to its action in restoring the skeletal muscle morphology and reducing neuroinflammation from cerebellum.

Quantitative evaluation of motion compensation in post-stroke rehabilitation training based on muscle synergy

Introduction: Stroke is the second leading cause of death globally and a primary factor contributing to disability. Unilateral limb motor impairment caused by stroke is the most common scenario. The bilateral movement pattern plays a crucial role in assisting stroke survivors on the affected side to relearn lost skills. However, motion compensation often lead to decreased coordination between the limbs on both sides. Furthermore, muscle fatigue resulting from imbalanced force exertion on both sides of the limbs can also impact the rehabilitation outcomes. Method: In this study, an assessment method based on muscle synergy indicators was proposed to objectively quantify the impact of motion compensation issues on rehabilitation outcomes. Muscle synergy describes the body's neuromuscular control mechanism, representing the coordinated activation of multiple muscles during movement. 8 post-stroke hemiplegia patients and 8 healthy subjects participated in this study. During hand-cycling tasks with different resistance levels, surface electromyography signals were synchronously collected from these participants before and after fatigue. Additionally, a simulated compensation experiment was set up for healthy participants to mimic various hemiparetic states observed in patients. Results and discussion: Synergy symmetry and synergy fusion were chosen as potential indicators for assessing motion compensation. The experimental results indicate significant differences in synergy symmetry and fusion levels between the healthy control group and the patient group (p ≤ 0.05), as well as between the healthy control group and the compensation group. Moreover, the analysis across different resistance levels showed no significant variations in the assessed indicators (p > 0.05), suggesting the utility of synergy symmetry and fusion indicators for the quantitative evaluation of compensation behaviors. Although muscle fatigue did not significantly alter the symmetry and fusion levels of bilateral synergies (p > 0.05), it did reduce the synergy repeatability across adjacent movement cycles, compromising movement stability and hindering patient recovery. Based on synergy symmetry and fusion indicators, the degree of bilateral motion compensation in patients can be quantitatively assessed, providing personalized recommendations for rehabilitation training and enhancing its effectiveness.

Spatiotemporal patterns of throwing muscle synergies in yips-affected baseball players

"Yips" are involuntary movements that interfere with the automatic execution of sports movements. However, how the coordination among the various muscles necessary for sports movements is impaired in athletes with yips remains to be fully understood. This study aimed to assess whether muscle synergy analysis through non-negative matrix factorization (NMF) could identify impaired spatiotemporal muscle coordination in baseball players with throwing yips. Twenty-two college baseball players, including 12 with and 10 without yips symptoms participated in the study. Electromyographic activity was recorded from 13 ipsilateral upper extremity muscles during full-effort throwing. Muscle synergies were extracted through NMF. Cluster analysis was conducted to identify any common spatiotemporal patterns of muscle synergies in players with yips. Whether individual players with yips showed deviations in spatiotemporal patterns of muscle synergies compared with control players was also investigated. Four muscle synergies were extracted for each player, but none were specific to the yips group. However, a more detailed analysis of individual players revealed that two of the three players who presented dystonic symptoms during the experiment exhibited specific patterns that differed from those in control players. By contrast, each player whose symptoms were not reproduced during the experiment presented spatiotemporal patterns of muscle synergies similar to those of the control group. The results of this study indicate no common spatiotemporal pattern of muscle synergies specific to the yips group. Furthermore, these results suggest that the spatiotemporal pattern of muscle synergies in baseball throwing motion is not impaired in situations where symptoms are not reproduced even if the players have yips symptoms. However, muscle synergy analysis can identify the characteristics of muscle coordination of players who exhibit dystonic movements. These findings can be useful in developing personalized therapeutic strategies based on individual characteristics of yips symptoms.

A long short-term memory modeling-based compensation method for muscle synergy

Muscle synergy containing temporal and spatial patterns of muscle activity has been frequently used in prediction of kinematic characteristics. However, there is often some discrepancy between the predicted results based on muscle synergy and the actual movement performance. This study aims to propose a new method for compensating muscle synergy that allows the compensated synergy signal to predict kinematic characteristics more accurately. The study used the change of direction in running as background. Non-negative matrix factorisation was used to extract the muscle synergy during the change of direction at different angles. A non-linear association between synergy and the height of pelvic mass centre was established using long and short-term memory neural networks. Based on this model, the height fluctuations of the pelvic centre of mass are used as input and predict the fluctuations of the synergy which were used to compensate for the original synergy in different ways. The accuracy of the synergies compensated in different ways in predicting pelvic centre of mass movement was then assessed by back propagation neural networks. It was found that the compensated synergy significantly improves accuracy in predicting pelvic centre of mass displacement (R2, p < 0.05). The predicted results of all-compensation are significantly different from actual performance in the end-swing (p < 0.05). The predicted results of half-compensation do not differ significantly from the actual performance, and its damage to the original synergy is smaller and does not increase with angle compared to all-compensation. The all-compensation may be affected by individual variability and lead to increased errors. The half-compensation can improve the predictive accuracy of the synergy while reducing the adjustment to the original synergy.

Synergy-Driven Musculoskeletal Modeling to Estimate Muscle Excitations and Joint Moments at Different Walking Speeds in Individuals with Transtibial Amputation

The main requirement for an amputee is to regain the function of the lost limb. In order to fully benefit from powered prosthetic legs, the user must rely on the dynamic control of the device. Progress in high-level control for powered prosthetic legs is currently challenged by the inability of current control schemes to generalize to large repertoires of movements as well as adapting to external mechanical demands. This ultimately leads the user to adopt compensatory movements, lack of comfort, higher energy requirements during walking and standing. This study uses a feedforward model of muscle activation and force generation that applies mathematical formulations of muscle synergies to generate synthetic activation profiles underlying walking across different speeds. Estimated activation profiles are used to drive forward subject-specific numerical models of the lower extremity musculoskeletal system. The model was validated on one individual with uni-lateral transtibial amputation and its predictions were compared to experimental torques from inverse dynamic calculations. Results showed that a generic muscle synergy driven personalized musculoskeletal model can fit the ankle torques of the intact limb of a person with transtibial amputation (RMSD = 0.1329±0.02). The estimated moments might be suitable as the control signal to drive powered prostheses to ultimately improve physical interaction between the user and a powered prostheses during dynamic motor tasks.

Gait asymmetry in children with Duchenne muscular dystrophy: evaluated through kinematic synergies and muscle synergies of lower limbs

BACKGROUND

Gait is a complex, whole-body movement that requires the coordinated action of multiple joints and muscles of our musculoskeletal system. In the context of Duchenne muscular dystrophy (DMD), a disease characterized by progressive muscle weakness and joint contractures, previous studies have generally assumed symmetrical behavior of the lower limbs during gait. However, such a symmetric gait pattern of DMD was controversial. One aspect of this is criticized, because most of these studies have primarily focused on univariate variables, rather than on the coordination of multiple body segments and even less investigate gait symmetry under a motor synergy of view.

METHODS

We investigated the gait pattern of 20 patients with DMD, compared to 18 typical developing children (TD) through 3D Gait Analysis. Kinematic and muscle synergies were extracted with principal component analysis (PCA) and non-negative matrix factorization (NNMF), respectively. The synergies extracted from the left and right sides were compared with each other to obtain a symmetry value. In addition, bilateral spatiotemporal variables of gait, such as stride length, percentage of stance and swing phase, step length, and percentage of double support phase, were used for calculating the symmetry index (SI) to evaluate gait symmetry as well.

RESULTS

Compared with the TD group, the DMD group walked with decreased gait velocity (both p < 0.01), stride length (both p < 0.01), and step length (both p < 0.001). No significant difference was found between groups in SI of all spatiotemporal parameters extracted between the left and right lower limbs. In addition, the DMD group exhibited lower kinematic synergy symmetry values compared to the TD group (p < 0.001), while no such significant group difference was observed in symmetry values of muscle synergy.

CONCLUSIONS

The findings of this study suggest that DMD influences, to some extent, the symmetry of synergistic movement of multiple segments of lower limbs, and thus kinematic synergy appears capable of discriminating gait asymmetry in children with DMD when conventional spatiotemporal parameters are unchanged.

Age-related differences in gait symmetry obtained from kinematic synergies and muscle synergies of lower limbs during childhood

The age-related changes of gait symmetry in healthy children concerning individual joint and muscle activation data have previously been widely studied. Extending beyond individual joints or muscles, identifying age-related changes in the coordination of multiple joints or muscles (i.e., muscle synergies and kinematic synergies) could capture more closely the underlying mechanisms responsible for gait symmetry development. To evaluate the effect of age on the symmetry of the coordination of multiple joints or muscles during childhood, we measured gait symmetry by kinematic and EMG data in 39 healthy children from 2 years old to 14 years old, divided into three equal age groups: preschool children (G1; 2.0-5.9 years), children (G2; 6.0-9.9 years), pubertal children (G3; 10.0-13.9 years). Participants walked barefoot at a self-selected walking speed during three-dimensional gait analysis (3DGA). Kinematic synergies and muscle synergies were extracted with principal component analysis (PCA) and non-negative matrix factorization (NNMF), respectively. The synergies extracted from the left and right sides were compared with each other to obtain a symmetry value. Statistical analysis was performed to examine intergroup differences. The results showed that the effect of age was significant on the symmetry values extracted by kinematic synergies, while older children exhibited higher kinematic synergy symmetry values compared to the younger group. However, no significant age-related changes in symmetry values of muscle synergy were observed. It is suggested that kinematic synergy of lower joints can be asymmetric at the onset of independent walking and showed improving symmetry with increasing age, whereas the age-related effect on the symmetry of muscle synergies was not demonstrated. These data provide an age-related framework and normative dataset to distinguish age-related differences from pathology in children with neuromotor disorders.

Contribution of Phase Resetting to Statistical Persistence in Stride Intervals: A Modeling Study

Stride intervals in human walking fluctuate from one stride to the next, exhibiting statistical persistence. This statistical property is changed by aging, neural disorders, and experimental interventions. It has been hypothesized that the central nervous system is responsible for the statistical persistence. Human walking is a complex phenomenon generated through the dynamic interactions between the central nervous system and the biomechanical system. It has also been hypothesized that the statistical persistence emerges through the dynamic interactions during walking. In particular, a previous study integrated a biomechanical model composed of seven rigid links with a central pattern generator (CPG) model, which incorporated a phase resetting mechanism as sensory feedback as well as feedforward, trajectory tracking, and intermittent feedback controllers, and suggested that phase resetting contributes to the statistical persistence in stride intervals. However, the essential mechanisms remain largely unclear due to the complexity of the neuromechanical model. In this study, we reproduced the statistical persistence in stride intervals using a simplified neuromechanical model composed of a simple compass-type biomechanical model and a simple CPG model that incorporates only phase resetting and a feedforward controller. A lack of phase resetting induced a loss of statistical persistence, as observed for aging, neural disorders, and experimental interventions. These mechanisms were clarified based on the phase response characteristics of our model. These findings provide useful insight into the mechanisms responsible for the statistical persistence of stride intervals in human walking.

Functional reorganization of locomotor kinematic synergies reflects the neuropathology in a mouse model of spinal cord injury

Spinal cord injury (SCI) disrupts motor commands to modular structures of the spinal cord, limiting the ability to walk. Evidence suggests that these modules are conserved across species from rodent to human and subserve adaptive walking by controlling coordinated joint movements (kinematic synergies). Since SCI causes uncoordinated joint movements of the lower limbs during walking, there may be a disorder of the modular structures that control them. To gain insights into this complex process, we recorded the kinematics of intact and SCI mice when walking on a treadmill and applied principal component analysis to extract kinematic synergies. Most SCI mice walked stably on the treadmill, but their kinematic synergies were generally different from those of intact mice. We classified the kinematic synergies of SCI mice into three groups based on the similarity of the extracted first three synergy components. We found that these three groups had different degrees of spinal cord damage. This suggests that differences in kinematic synergies reflect underlying SCI neuropathology. These results may help guide the development of different rehabilitation approaches and future physiological experiments to understand the mechanisms of motor control and recovery.

Recording activity in proximal muscle networks with surface EMG in assessing infant motor development

OBJECTIVE

To develop methods for recording and analysing infant's proximal muscle activations.

METHODS

Surface electromyography (sEMG) of truncal muscles was recorded in three months old infants (N = 18) during spontaneous movement and controlled postural changes. The infants were also divided into two groups according to motor performance. We developed an efficient method for removing dynamic cardiac artefacts to allow i) accurate estimation of individual muscle activations, as well as ii) quantitative characterization of muscle networks.

RESULTS

The automated removal of cardiac artefacts allowed quantitation of truncal muscle activity, which showed predictable effects during postural changes, and there were differences between high and low performing infants.The muscle networks showed consistent change in network density during spontaneous movements between supine and prone position. Moreover, activity correlations in individual pairs of back muscles linked to infant́s motor performance.

CONCLUSIONS

The hereby developed sEMG analysis methodology is feasible and may disclose differences between high and low performing infants. Analysis of the muscle networks may provide novel insight to central control of motility.

SIGNIFICANCE

Quantitative analysis of infant's muscle activity and muscle networks holds promise for an objective neurodevelopmental assessment of motor system.

Computational Modeling of Spinal Locomotor Circuitry in the Age of Molecular Genetics

Neuronal circuits in the spinal cord are essential for the control of locomotion. They integrate supraspinal commands and afferent feedback signals to produce coordinated rhythmic muscle activations necessary for stable locomotion. For several decades, computational modeling has complemented experimental studies by providing a mechanistic rationale for experimental observations and by deriving experimentally testable predictions. This symbiotic relationship between experimental and computational approaches has resulted in numerous fundamental insights. With recent advances in molecular and genetic methods, it has become possible to manipulate specific constituent elements of the spinal circuitry and relate them to locomotor behavior. This has led to computational modeling studies investigating mechanisms at the level of genetically defined neuronal populations and their interactions. We review literature on the spinal locomotor circuitry from a computational perspective. By reviewing examples leading up to and in the age of molecular genetics, we demonstrate the importance of computational modeling and its interactions with experiments. Moving forward, neuromechanical models with neuronal circuitry modeled at the level of genetically defined neuronal populations will be required to further unravel the mechanisms by which neuronal interactions lead to locomotor behavior.

Locomotion is impacted differently according to the perinatal brain injury model: Meta-analysis of preclinical studies with implications for cerebral palsy

BACKGROUND

Different approaches to reproduce cerebral palsy (CP) in animals, contribute to the knowledge of the pathophysiological mechanism of this disease and provide a basis for the development of intervention strategies. Locomotion and coordination are the main cause of disability in CP, however, few studies highlight the quantitative differences of CP models, on locomotion parameters, considering the methodologies to cause brain lesions in the perinatal period.

METHODS

Studies with cerebral palsy animal models that assess locomotion parameters were systematically retrieved from Medline/PubMed, SCOPUS, LILACS, and Web of Science. Methodological evaluation of included studies and quantitative assessment of locomotion parameters were performed after eligibility screening.

RESULTS

CP models were induced by hypoxia-ischemia (HI), Prenatal ischemia (PI), lipopolysaccharide inflammation (LPS), intraventricular haemorrhage (IVH), anoxia (A), sensorimotor restriction (SR), and a combination of different models. Overall, 63 studies included in qualitative synthesis showed a moderate quality of evidence. 16 studies were included in the quantitative meta-analysis. Significant reduction was observed in models that combined LPS with HI related to distance traveled (SMD -7.24 95 % CI [-8.98, -5.51], Z = 1.18, p < 0.00001) and LPS with HI or anoxia with sensory-motor restriction (SMD -6.01, 95 % CI [-7.67, -4.35], Z = 7.11), or IVH (SMD -4.91, 95 % CI [-5.84, -3.98], Z = 10.31, p < 0.00001) related to motor coordination.

CONCLUSION

The combination of different approaches to reproduce CP in animals causes greater deficits in locomotion and motor coordination from the early stages of life to adulthood. These findings contribute to methodological refinement, reduction, and replacement in animal experimentation, favoring translational purposes.

Contribution of Phase Resetting to Adaptive Rhythm Control in Human Walking Based on the Phase Response Curves of a Neuromusculoskeletal Model

Humans walk adaptively in varying environments by manipulating their complicated and redundant musculoskeletal system. Although the central pattern generators in the spinal cord are largely responsible for adaptive walking through sensory-motor coordination, it remains unclear what neural mechanisms determine walking adaptability. It has been reported that locomotor rhythm and phase are regulated by the production of phase shift and rhythm resetting (phase resetting) for periodic motor commands in response to sensory feedback and perturbation. While the phase resetting has been suggested to make a large contribution to adaptive walking, it has only been investigated based on fictive locomotion in decerebrate cats, and thus it remains unclear if human motor control has such a rhythm regulation mechanism during walking. In our previous work, we incorporated a phase resetting mechanism into a motor control model and demonstrated that it improves the stability and robustness of walking through forward dynamic simulations of a human musculoskeletal model. However, this did not necessarily verify that phase resetting plays a role in human motor control. In our other previous work, we used kinematic measurements of human walking to identify the phase response curve (PRC), which explains phase-dependent responses of a limit cycle oscillator to a perturbation. This revealed how human walking rhythm is regulated by perturbations. In this study, we integrated these two approaches using a physical model and identification of the PRC to examine the hypothesis that phase resetting plays a role in the control of walking rhythm in humans. More specifically, we calculated the PRC using our neuromusculoskeletal model in the same way as our previous human experiment. In particular, we compared the PRCs calculated from two different models with and without phase resetting while referring to the PRC for humans. As a result, although the PRC for the model without phase resetting did not show any characteristic shape, the PRC for the model with phase resetting showed a characteristic phase-dependent shape with trends similar to those of the PRC for humans. These results support our hypothesis and will improve our understanding of adaptive rhythm control in human walking.

Gait Generation and Its Energy Efficiency Based on Rat Neuromusculoskeletal Model

Changing gait is crucial for adaptive and smooth animal locomotion. Although it remains unclear what makes animals decide on a specific gait, energy efficiency is an important factor. It has been reported that the relationship of oxygen consumption with speed is U-shaped for each horse gait and that different gaits have different speeds at which oxygen consumption is minimized. This allows the horse to produce energy-efficient locomotion in a wide speed range by changing gait. However, the underlying mechanisms causing oxygen consumption to be U-shaped and the speeds for the minimum consumption to be different between different gaits are unclear. In the present study, we used a neuromusculoskeletal model of the rat to examine the mechanism from a dynamic viewpoint. Specifically, we constructed the musculoskeletal part of the model based on empirical anatomical data on rats and the motor control model based on the physiological concepts of the spinal central pattern generator and muscle synergy. We also incorporated the posture and speed regulation models at the levels of the brainstem and cerebellum. Our model achieved walking through forward dynamic simulation, and the simulated joint kinematics and muscle activities were compared with animal data. Our model also achieved trotting by changing only the phase difference of the muscle-synergy-based motor commands between the forelimb and hindlimb. Furthermore, the speed of each gait varied by changing only the extension phase duration and amplitude of the muscle synergy-based motor commands and the reference values for the regulation models. The relationship between cost of transport (CoT) and speed was U-shaped for both the generated walking and trotting, and the speeds for the minimum CoT were different for the two gaits, as observed in the oxygen consumption of horses. We found that the resonance property and the posture and speed regulations contributed to the CoT shape and difference in speeds for the minimum CoT. We further discussed the energy efficiency of gait based on the simulation results.

Variant and Invariant Spatiotemporal Structures in Kinematic Coordination to Regulate Speed During Walking and Running

Humans walk, run, and change their speed in accordance with circumstances. These gaits are rhythmic motions generated by multi-articulated movements, which have specific spatiotemporal patterns. The kinematic characteristics depend on the gait and speed. In this study, we focused on the kinematic coordination of locomotor behavior to clarify the underlying mechanism for the effect of speed on the spatiotemporal kinematic patterns for each gait. In particular, we used seven elevation angles for the whole-body motion and separated the measured data into different phases depending on the foot-contact condition, that is, single-stance phase, double-stance phase, and flight phase, which have different physical constraints during locomotion. We extracted the spatiotemporal kinematic coordination patterns with singular value decomposition and investigated the effect of speed on the coordination patterns. Our results showed that most of the whole-body motion could be explained by only two sets of temporal and spatial coordination patterns in each phase. Furthermore, the temporal coordination patterns were invariant for different speeds, while the spatial coordination patterns varied. These findings will improve our understanding of human adaptation mechanisms to tune locomotor behavior for changing speed.

Computational analysis of knee joint stability following total knee arthroplasty

The overall objective of this study was to introduce knee joint power as a potential measure to investigate knee joint stability following total knee arthroplasty (TKA). Specific aims were to investigate whether weakened knee joint stabilizers cause abnormal kinematics and how it influences the knee joint kinetic (i.e., power) in response to perturbation. Patient-specific musculoskeletal models were simulated with experimental gait data from six TKA patients (baseline models). Muscle strength and ligament force parameter were reduced by up to 30% to simulate weak knee joint stabilizers (weak models). Two different muscle recruitment criteria were tested to examine whether altered muscle recruitment pattern can mask the influence of weakened stabilizers on the knee joint kinematics and kinetics. Level-walking knee joint kinematics and kinetics were calculated though force-dependent kinematic and inverse dynamic analyses. Bode analysis was then recruited to estimate the knee joint power in response to a simulated perturbation. Weak models resulted in larger anterior-posterior (A-P) displacement and internal-external (I-E) rotation compared to baseline (I-E: 18.4 ± 8.5 vs. 11.6 ± 5.7 (deg), A-P: 9.7 ± 5.6 vs. 5.5 ± 4.1 (mm)). Changes in muscle recruitment criterion however altered the results such that A-P and I-E were not notably different from baseline models. In response to the simulated perturbation, weak models versus baseline models generated a delayed power response with unbounded magnitudes. Perturbed power behavior of the knee remained unaltered regardless of the muscle recruitment criteria. In conclusion, impairment at the knee joint stabilizers may or may not lead to excessive joint motions but it notably affects the knee joint power in response to a perturbation. Whether perturbed knee joint power is associated with the patient-reported outcome requires further investigation.

Neuromusculoskeletal model that walks and runs across a speed range with a few motor control parameter changes based on the muscle synergy hypothesis

Humans walk and run, as well as change their gait speed, through the control of their complicated and redundant musculoskeletal system. These gaits exhibit different locomotor behaviors, such as a double-stance phase in walking and flight phase in running. The complex and redundant nature of the musculoskeletal system and the wide variation in locomotion characteristics lead us to imagine that the motor control strategies for these gaits, which remain unclear, are extremely complex and differ from one another. It has been previously proposed that muscle activations may be generated by linearly combining a small set of basic pulses produced by central pattern generators (muscle synergy hypothesis). This control scheme is simple and thought to be shared between walking and running at different speeds. Demonstrating that this control scheme can generate walking and running and change the speed is critical, as bipedal locomotion is dynamically challenging. Here, we provide such a demonstration by using a motor control model with 69 parameters developed based on the muscle synergy hypothesis. Specifically, we show that it produces both walking and running of a human musculoskeletal model by changing only seven key motor control parameters. Furthermore, we show that the model can walk and run at different speeds by changing only the same seven parameters based on the desired speed. These findings will improve our understanding of human motor control in locomotion and provide guiding principles for the control design of wearable exoskeletons and prostheses.

The effects of coordinative locomotor training on coordination and gait in chronic stroke patients: a randomized controlled pilot trial

The purpose of this study was to investigate the effects of coordinative locomotor training (CLT) on coordination and gait in chronic stroke patients. Thirteen stroke patients were randomly assigned to one of two groups: The experimental group (EG, n=7) and control group (CG, n=6). After balanced random assignment, interventions were provided to all patients in twelve 30-min sessions during a 4-week period (3 sessions a week). The EG group underwent CLT after performing the sprinter and skater patterns in four postures. The CG group was provided with conventional neurodevelopment treatment. The primary outcomes were measured using the gait assessment and intervention tool (GAIT) as well as G-WALK. The secondary outcomes were measured using a 10-m walk test (10 MWT) and Timed Up and Go Test. There was no statistically significant difference in the mean values between the two groups. Within both groups there were significant differences in GAIT and 10MWT. There were significant differences in gait speed and stride length in the experimental group, but not in the control group. CLT showed a more positive improvement in gait and coordination after stroke than conventional rehabilitation exercise.

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 Linear Approach to Optimize an EMG-Driven Neuromusculoskeletal Model for Movement Intention Detection in Myo-Control: A Case Study on Shoulder and Elbow Joints

The growing interest of the industry production in wearable robots for assistance and rehabilitation purposes opens the challenge for developing intuitive and natural control strategies. Myoelectric control, or myo-control, which consists in decoding the human motor intent from muscular activity and its mapping into control outputs, represents a natural way to establish an intimate human-machine connection. In this field, model based myo-control schemes (e.g., EMG-driven neuromusculoskeletal models, NMS) represent a valid solution for estimating the moments of the human joints. However, a model optimization is needed to adjust the model's parameters to a specific subject and most of the optimization approaches presented in literature consider complex NMS models that are unsuitable for being used in a control paradigm since they suffer from long-lasting setup and optimization phases. In this work we present a minimal NMS model for predicting the elbow and shoulder torques and we compare two optimization approaches: a linear optimization method (LO) and a non-linear method based on a genetic algorithm (GA). The LO optimizes only one parameter per muscle, whereas the GA-based approach performs a deep customization of the muscle model, adjusting 12 parameters per muscle. EMG and force data have been collected from 7 healthy subjects performing a set of exercises with an arm exoskeleton. Although both optimization methods substantially improved the performance of the raw model, the findings of the study suggest that the LO might be beneficial with respect to GA as the latter is much more computationally heavy and leads to minimal improvements with respect to the former. From the comparison between the two considered joints, it emerged also that the more accurate the NMS model is, the more effective a complex optimization procedure could be. Overall, the two optimized NMS models were able to predict the shoulder and elbow moments with a low error, thus demonstrating the potentiality for being used in an admittance-based myo-control scheme. Thanks to the low computational cost and to the short setup phase required for wearing and calibrating the system, obtained results are promising for being introduced in industrial or rehabilitation real time scenarios.

Walking with perturbations: a guide for biped humans and robots

This paper provides an update on the neural control of bipedal walking in relation to bioinspired models and robots. It is argued that most current models or robots are based on the construct of a symmetrical central pattern generator (CPG). However, new evidence suggests that CPG functioning is basically asymmetrical with its flexor half linked more tightly to the rhythm generator. The stability of bipedal gait, which is an important problem for robots and biological systems, is also addressed. While it is not possible to determine how biological biped systems guarantee stability, robot solutions can be useful to propose new hypotheses for biology. In the second part of this review, the focus is on gait perturbations, which is an important topic in robotics in view of the frequent falls of robots when faced with perturbations. From the human physiology it is known that the initial reaction often consists of a brief interruption followed by an adequate response. For instance, the successful recovery from a trip is achieved using some basic reactions (termed elevating and lowering strategies), that depend on the phase of the step cycle of the trip occurrence. Reactions to stepping unexpectedly in a hole depend on comparing expected and real feedback. Implementation of these ideas in models and robotics starts to emerge, with the most advanced robots being able to learn how to fall safely and how to deal with complicated disturbances such as provided by walking on a split-belt.

Evaluation of matrix factorisation approaches for muscle synergy extraction

The muscle synergy concept provides a widely-accepted paradigm to break down the complexity of motor control. In order to identify the synergies, different matrix factorisation techniques have been used in a repertoire of fields such as prosthesis control and biomechanical and clinical studies. However, the relevance of these matrix factorisation techniques is still open for discussion since there is no ground truth for the underlying synergies. Here, we evaluate factorisation techniques and investigate the factors that affect the quality of estimated synergies. We compared commonly used matrix factorisation methods: Principal component analysis (PCA), Independent component analysis (ICA), Non-negative matrix factorization (NMF) and second-order blind identification (SOBI). Publicly available real data were used to assess the synergies extracted by each factorisation method in the classification of wrist movements. Synthetic datasets were utilised to explore the effect of muscle synergy sparsity, level of noise and number of channels on the extracted synergies. Results suggest that the sparse synergy model and a higher number of channels would result in better estimated synergies. Without dimensionality reduction, SOBI showed better results than other factorisation methods. This suggests that SOBI would be an alternative when a limited number of electrodes is available but its performance was still poor in that case. Otherwise, NMF had the best performance when the number of channels was higher than the number of synergies. Therefore, NMF would be the best method for muscle synergy extraction.

A Systematic Review on Muscle Synergies: From Building Blocks of Motor Behavior to a Neurorehabilitation Tool

The central nervous system (CNS) is believed to utilize specific predefined modules, called muscle synergies (MS), to accomplish a motor task. Yet questions persist about how the CNS combines these primitives in different ways to suit the task conditions. The MS hypothesis has been a subject of debate as to whether they originate from neural origins or nonneural constraints. In this review article, we present three aspects related to the MS hypothesis: (1) the experimental and computational evidence in support of the existence of MS, (2) algorithmic approaches for extracting them from surface electromyography (EMG) signals, and (3) the possible role of MS as a neurorehabilitation tool. We note that recent advances in computational neuroscience have utilized the MS hypothesis in motor control and learning. Prospective advances in clinical, medical, and engineering sciences and in fields such as robotics and rehabilitation stand to benefit from a more thorough understanding of MS.

Muscle synergy extraction during arm reaching movements at different speeds

OBJECTIVE

Muscle synergy is the activation of a group of muscles that contribute to a particular movement. The goal of the present study is to examine the hypothesis that human reaching movements at different speeds share similar muscle synergies and to investigate the kinesiology basis and innervation of muscles.

METHODS

Electromyographic activity from six muscles of the upper limb and shoulder girdle were recorded during three movements at different speeds, i.e. slow, moderate and fast. The effect of window length on the RMS signal of the EMG was analyzed and then EMG envelope signals were decomposed using non-negative matrix factorization. For each of the ten subjects, three synergies were extracted which accounted for at least 99% of the VAF. For each movement, the muscle synergies and muscle activation coefficients of all participants were clustered in to three partitions. Investigation showed a high similarity and dependency of cluster members due to the cosine similarity and mutual information in muscle synergy clustering. For further verification, the EMG envelope signals for all subjects were reconstructed.

RESULTS

The results indicated a lower reconstruction error using the center of the muscle synergy clusters in comparison with the average of the activation coefficients, which confirms the current research's hypothesis.

Adaptive Control Strategies for Interlimb Coordination in Legged Robots: A Review

Walking animals produce adaptive interlimb coordination during locomotion in accordance with their situation. Interlimb coordination is generated through the dynamic interactions of the neural system, the musculoskeletal system, and the environment, although the underlying mechanisms remain unclear. Recently, investigations of the adaptation mechanisms of living beings have attracted attention, and bio-inspired control systems based on neurophysiological findings regarding sensorimotor interactions are being developed for legged robots. In this review, we introduce adaptive interlimb coordination for legged robots induced by various factors (locomotion speed, environmental situation, body properties, and task). In addition, we show characteristic properties of adaptive interlimb coordination, such as gait hysteresis and different time-scale adaptations. We also discuss the underlying mechanisms and control strategies to achieve adaptive interlimb coordination and the design principle for the control system of legged robots.

Modular organization of muscle activity patterns in the leading and trailing limbs during obstacle clearance in healthy adults

Human locomotor patterns require precise adjustments to successfully navigate complex environments. Studies suggest that the central nervous system may control such adjustments through supraspinal signals modifying a basic locomotor pattern at the spinal level. To explore this proposed control mechanism in the leading and trailing limbs during obstructed walking, healthy young adults stepped over obstacles measuring 0.1 and 0.2 m in height. Unobstructed walking with no obstacle present was also performed as a baseline. Full body three-dimensional kinematic data were recorded and electromyography (EMG) was collected from 14 lower limb muscles on each side of the body. EMG data were analyzed using two techniques: by mapping the EMG data to the approximate location of the motor neuron pools on the lumbosacral enlargement of the spinal cord and by applying a nonnegative matrix factorization algorithm to unilateral and bilateral muscle activations separately. Results showed that obstacle clearance may be achieved not only with the addition of a new activation pattern in the leading limb, but with a temporal shift of a pattern present during unobstructed walking in both the leading and trailing limbs. An investigation of the inter-limb coordination of these patterns suggested a strong bilateral linkage between lower limbs. These results highlight the modular organization of muscle activation in the leading and trailing limbs, as well as provide a mechanism of control when implementing a locomotor adjustment when stepping over an obstacle.

Evaluation of the Phase-Dependent Rhythm Control of Human Walking Using Phase Response Curves

Humans and animals control their walking rhythms to maintain motion in a variable environment. The neural mechanism for controlling rhythm has been investigated in many studies using mechanical and electrical stimulation. However, quantitative evaluation of rhythm variation in response to perturbation at various timings has rarely been investigated. Such a characteristic of rhythm is described by the phase response curve (PRC). Dynamical simulations of human skeletal models with changing walking rhythms (phase reset) described a relation between the effective phase reset on stability and PRC, and phase reset around touch-down was shown to improve stability. A PRC of human walking was estimated by pulling the swing leg, but such perturbations hardly influenced the stance leg, so the relation between the PRC and walking events was difficult to discuss. This research thus examines human response to variations in floor velocity. Such perturbation yields another problem, in that the swing leg is indirectly (and weakly) perturbed, so the precision of PRC decreases. To solve this problem, this research adopts the weighted spike-triggered average (WSTA) method. In the WSTA method, a sequential pulsed perturbation is used for stimulation. This is in contrast with the conventional impulse method, which applies an intermittent impulsive perturbation. The WSTA method can be used to analyze responses to a large number of perturbations for each sequence. In the experiment, perturbations are applied to walking subjects by rapidly accelerating and decelerating a treadmill belt, and measured data are analyzed by the WSTA and impulse methods. The PRC obtained by the WSTA method had clear and stable waveforms with a higher temporal resolution than those obtained by the impulse method. By investigation of the rhythm transition for each phase of walking using the obtained PRC, a rhythm change that extends the touch-down and mid-single support phases is found to occur.

Humans and animals tune their walking rhythms when motion is disturbed, such that they hesitate before making the transition from stance to swing phase. The effectiveness of rhythm control for stability has also been shown, and thus the elucidation of rhythm responses is important to understanding human strategies for walking control. In this research, how and when humans change their walking rhythm in response to disturbance is analyzed over the complete walking cycle. Phase response of human walking has previously been estimated by pulling the swing leg. The problem with this perturbation is that it hardly disturbs the stance leg, so here we apply the perturbation by changing floor velocity. However, perturbation from the floor yields another problem in that it weakly influences the swing leg, decreasing the precision of the PRC. The present research tackles this problem by introducing a new method for identifying rhythm characteristics by use of high-frequency perturbation, which allows us to obtain results with clear temporal resolution. We found that the human walking rhythm changes by lengthening the touch-down and mid-single support phases. These phase responses are compared with neural mechanisms for rhythm control, and relevance to the cutaneous and proprioceptive originated responses is shown.