Optimization of muscle activity for task-level goals predicts complex changes in limb forces across biomechanical contexts

Synergy quality assessment of muscle modules for determining learning performance using a realistic musculoskeletal model

How our central nervous system efficiently controls our complex musculoskeletal system is still debated. The muscle synergy hypothesis is proposed to simplify this complex system by assuming the existence of functional neural modules that coordinate several muscles. Modularity based on muscle synergies can facilitate motor learning without compromising task performance. However, the effectiveness of modularity in motor control remains debated. This ambiguity can, in part, stem from overlooking that the performance of modularity depends on the mechanical aspects of modules of interest, such as the torque the modules exert. To address this issue, this study introduces two criteria to evaluate the quality of module sets based on commonly used performance metrics in motor learning studies: the accuracy of torque production and learning speed. One evaluates the regularity in the direction of mechanical torque the modules exert, while the other evaluates the evenness of its magnitude. For verification of our criteria, we simulated motor learning of torque production tasks in a realistic musculoskeletal system of the upper arm using feed-forward neural networks while changing the control conditions. We found that the proposed criteria successfully explain the tendency of learning performance in various control conditions. These result suggest that regularity in the direction of and evenness in magnitude of mechanical torque of utilized modules are significant factor for determining learning performance. Although the criteria were originally conceived for an error-based learning scheme, the approach to pursue which set of modules is better for motor control can have significant implications in other studies of modularity in general.

Accuracy and effort costs together lead to temporal asynchrony of multiple motor commands

The timing of motor commands is critical for task performance. A well-known example is rapidly raising the arm while standing upright. Here, reaction forces from the arm movement to the body are countered by leg and trunk muscle activity starting before any sensory feedback from the perturbation and often before the onset of arm muscle activity. Despite decades of research on the patterns, modifiability, and neural basis of these "anticipatory postural adjustments," it remains unclear why asynchronous motor commands occur. Simple accuracy considerations appear unlikely since temporally advanced motor commands displace the body from its initial position. Effort is a credible and overlooked factor that has successfully explained coordination patterns of many behaviors including gait and reaching. We provide the first use of optimal control to address this question. Feedforward commands were applied to a body mass mechanically linked to a rapidly moving limb mass. We determined the feedforward actions with the lowest cost according to an explicit criterion, accuracy alone versus accuracy + effort. Accuracy costs alone led to synchronous activation of the body and limb controllers. Adding effort to the cost resulted in body commands preceding limb commands. This sequence takes advantage of the body's momentum in one direction to counter the limb's reaction force in the opposite direction, allowing a lower peak command and lower integral. With a combined accuracy + effort cost, temporal advancement was further impacted by various task goals and plant dynamics, replicating previous findings and suggesting further studies using optimal control principles.NEW & NOTEWORTHY An important goal in the fields of sensorimotor neuroscience and biomechanics is to explain the timing of different muscles during behavior. Here, we propose that energy and accuracy considerations underlie the asynchronous onset of postural and arm muscles during rapid movement. Our novel model-based framework replicates a broad range of observations across varying task demands and plant dynamics and offers a new perspective to study motor timing.

Kinematic trajectories in response to speed perturbations in walking suggest modular task-level control of leg angle and length

Navigating complex terrains requires dynamic interactions between the substrate, musculoskeletal and sensorimotor systems. Current perturbation studies have mostly used visible terrain height perturbations, which do not allow us to distinguish among the neuromechanical contributions of feedforward control, feedback-mediated and mechanical perturbation responses. Here, we use treadmill belt speed perturbations to induce a targeted perturbation to foot speed only, and without terrain-induced changes in joint posture and leg loading at stance onset. Based on previous studies suggesting a proximo-distal gradient in neuromechanical control, we hypothesized that distal joints would exhibit larger changes in joint kinematics, compared to proximal joints. Additionally, we expected birds to use feedforward strategies to increase the intrinsic stability of gait. To test these hypotheses, seven adult guinea fowl were video recorded while walking on a motorized treadmill, during both steady and perturbed trials. Perturbations consisted of repeated exposures to a deceleration and acceleration of the treadmill belt speed. Surprisingly, we found that joint angular trajectories and center of mass fluctuations remain very similar, despite substantial perturbation of foot velocity by the treadmill belt. Hip joint angular trajectories exhibit the largest changes, with the birds adopting a slightly more flexed position across all perturbed strides. Additionally, we observed increased stride duration across all strides, consistent with feedforward changes in the control strategy. The speed perturbations mainly influenced the timing of stance and swing, with the largest kinematic changes in the strides directly following a deceleration. Our findings do not support the general hypothesis of a proximo-distal gradient in joint control, as distal joint kinematics remain largely unchanged. Instead, we find that leg angular trajectory and the timing of stance and swing are most sensitive to this specific perturbation, and leg length actuation remains largely unchanged. Our results are consistent with modular task-level control of leg length and leg angle actuation, with different neuromechanical control and perturbation sensitivity in each actuation mode. Distal joints appear to be sensitive to changes in vertical loading but not foot fore-aft velocity. Future directions should include in vivo studies of muscle activation and force-length dynamics to provide more direct evidence of the sensorimotor control strategies for stability in response to belt speed perturbations.

A Conceptual Blueprint for Making Neuromusculoskeletal Models Clinically Useful

The ultimate goal of most neuromusculoskeletal modeling research is to improve the treatment of movement impairments. However, even though neuromusculoskeletal models have become more realistic anatomically, physiologically, and neurologically over the past 25 years, they have yet to make a positive impact on the design of clinical treatments for movement impairments. Such impairments are caused by common conditions such as stroke, osteoarthritis, Parkinson’s disease, spinal cord injury, cerebral palsy, limb amputation, and even cancer. The lack of clinical impact is somewhat surprising given that comparable computational technology has transformed the design of airplanes, automobiles, and other commercial products over the same time period. This paper provides the author’s personal perspective for how neuromusculoskeletal models can become clinically useful. First, the paper motivates the potential value of neuromusculoskeletal models for clinical treatment design. Next, it highlights five challenges to achieving clinical utility and provides suggestions for how to overcome them. After that, it describes clinical, technical, collaboration, and practical needs that must be addressed for neuromusculoskeletal models to fulfill their clinical potential, along with recommendations for meeting them. Finally, it discusses how more complex modeling and experimental methods could enhance neuromusculoskeletal model fidelity, personalization, and utilization. The author hopes that these ideas will provide a conceptual blueprint that will help the neuromusculoskeletal modeling research community work toward clinical utility.

Older adults reduce the complexity and efficiency of neuromuscular control to preserve walking balance

Healthy aging modifies neuromuscular control of dynamic balance. Challenging tasks could amplify such modifications, providing clinical insights. We examined the effects of age and walking condition difficulty on neuromuscular control of walking balance. We analyzed whole-body kinematics and activity of 13 right leg and trunk muscles in 17 young (11 males and 6 females; age 24 ± 3 years) and 14 older adults (3 males and 11 females; age 69 ± 4 years) while walking on a taped line on the floor and a 6-cm wide beam. Spatiotemporal parameters of gait, margin of stability, motor performance, and muscle synergies were estimated. Regardless of age, maintaining walking balance was more difficult on the beam compared to the taped line as evidenced by a shorter distance walked (17.3%), a reduction in step length (5.8%) and speed (10.3%), as well as a 40.0% smaller margin of stability during beam vs. tape walking. The number of muscle synergies was also higher during beam vs. tape walking. Compared to younger adults, older adults had larger margin of stability during beam walking. Older adults also had higher muscle co-activity within each muscle synergy and greater variance accounted for by the first muscle synergy regardless of condition. Such age-effects may be interpreted as a safer, less efficient, and less complex neuromuscular modular control strategy. In conclusion, beam walking increased the difficulty of maintaining walking balance and induced adaptations in modular control. It seems that healthy older adults reduce the complexity and efficiency of neuromuscular control of walking to preserve walking balance.

Optimality Principles in Human Point-to-Manifold Reaching Accounting for Muscle Dynamics

Human arm movements are highly stereotypical under a large variety of experimental conditions. This is striking due to the high redundancy of the human musculoskeletal system, which in principle allows many possible trajectories toward a goal. Many researchers hypothesize that through evolution, learning, and adaption, the human system has developed optimal control strategies to select between these possibilities. Various optimality principles were proposed in the literature that reproduce human-like trajectories in certain conditions. However, these studies often focus on a single cost function and use simple torque-driven models of motion generation, which are not consistent with human muscle-actuated motion. The underlying structure of our human system, with the use of muscle dynamics in interaction with the control principles, might have a significant influence on what optimality principles best model human motion. To investigate this hypothesis, we consider a point-to-manifold reaching task that leaves the target underdetermined. Given hypothesized motion objectives, the control input is generated using Bayesian optimization, which is a machine learning based method that trades-off exploitation and exploration. Using numerical simulations with Hill-type muscles, we show that a combination of optimality principles best predicts human point-to-manifold reaching when accounting for the muscle dynamics.

Muscle Synergy Constraints Do Not Improve Estimates of Muscle Activity From Static Optimization During Gait for Unimpaired Children or Children With Cerebral Palsy

Neuromusculoskeletal simulation provides a promising platform to inform the design of assistive devices or inform rehabilitation. For these applications, a simulation must be able to accurately represent the person of interest, such as an individual with a neurologic injury. If a simulation fails to predict how an individual recruits and coordinates their muscles during movement, it will have limited utility for informing design or rehabilitation. While inverse dynamic simulations have previously been used to evaluate anticipated responses from interventions, like orthopedic surgery or orthoses, they frequently struggle to accurately estimate muscle activations, even for tasks like walking. The simulated muscle activity often fails to represent experimentally measured muscle activity from electromyographic (EMG) recordings. Research has theorized that the nervous system may simplify the range of possible activations used during dynamic tasks, by constraining activations to weighted groups of muscles, referred to as muscle synergies. Synergies are altered after neurological injury, such as stroke or cerebral palsy (CP), and may provide a method for improving subject-specific models of neuromuscular control. The aim of this study was to test whether constraining simulation to synergies could improve estimated muscle activations compared to EMG data. We evaluated modeled muscle activations during gait for six typically developing (TD) children and six children with CP. Muscle activations were estimated with: (1) static optimization (SO), minimizing muscle activations squared, and (2) synergy SO (SynSO), minimizing synergy activations squared using the weights identified from EMG data for two to five synergies. While SynSO caused changes in estimated activations compared to SO, the correlation to EMG data was not higher in SynSO than SO for either TD or CP groups. The correlations to EMG were higher in CP than TD for both SO (CP: 0.48, TD: 0.36) and SynSO (CP: 0.46, TD: 0.26 for five synergies). Constraining activations to SynSO caused the simulated muscle stress to increase compared to SO for all individuals, causing a 157% increase with two synergies. These results suggest that constraining simulated activations in inverse dynamic simulations to subject-specific synergies alone may not improve estimation of muscle activations during gait for generic musculoskeletal models.

Antagonist muscle activity during reactive balance responses is elevated in Parkinson's disease and in balance impairment

BACKGROUND

Abnormal antagonist leg muscle activity could indicate increased muscle co-contraction and clarify mechanisms of balance impairments in Parkinson's disease (PD). Prior studies in carefully selected patients showed PD patients demonstrate earlier, longer, and larger antagonist muscle activation during reactive balance responses to perturbations.

RESEARCH QUESTION

Here, we tested whether antagonist leg muscle activity was abnormal in a group of PD patients who were not selected for phenotype and most of whom had volunteered for exercise-based rehabilitation.

METHODS

We compared antagonist activation during reactive balance responses to multidirectional support-surface translation perturbations in 31 patients with mild-moderate PD (age 68±9; H&Y 1-3; UPDRS-III 32±10) and 13 matched individuals (age 65±9). We quantified modulation of muscle activity (i.e., the ability to activate and inhibit muscles appropriately according to the perturbation direction) using modulation indices (MI) derived from minimum and maximum EMG activation levels observed across perturbation directions.

RESULTS

Antagonist leg muscle activity was abnormal in unselected PD patients compared to controls. Linear mixed models identified significant associations between impaired modulation and PD (P<0.05) and PD severity (P<0.01); models assessing the entire sample without referencing PD status identified associations with balance ability (P<0.05), but not age (P = 0.10).

SIGNIFICANCE

Antagonist activity is increased during reactive balance responses in PD patients who are not selected on phenotype and are candidates for exercise-based rehabilitation. This activity may be a mechanism of balance impairment in PD and a potential rehabilitation target or outcome measure.

Modularity speeds up motor learning by overcoming mechanical bias in musculoskeletal geometry

We can easily learn and perform a variety of movements that fundamentally require complex neuromuscular control. Many empirical findings have demonstrated that a wide range of complex muscle activation patterns could be well captured by the combination of a few functional modules, the so-called muscle synergies. Modularity represented by muscle synergies would simplify the control of a redundant neuromuscular system. However, how the reduction of neuromuscular redundancy through a modular controller contributes to sensorimotor learning remains unclear. To clarify such roles, we constructed a simple neural network model of the motor control system that included three intermediate layers representing neurons in the primary motor cortex, spinal interneurons organized into modules and motoneurons controlling upper-arm muscles. After a model learning period to generate the desired shoulder and/or elbow joint torques, we compared the adaptation to a novel rotational perturbation between modular and non-modular models. A series of simulations demonstrated that the modules reduced the effect of the bias in the distribution of muscle pulling directions, as well as in the distribution of torques associated with individual cortical neurons, which led to a more rapid adaptation to multi-directional force generation. These results suggest that modularity is crucial not only for reducing musculoskeletal redundancy but also for overcoming mechanical bias due to the musculoskeletal geometry allowing for faster adaptation to certain external environments.

Deciphering the functional role of spatial and temporal muscle synergies in whole-body movements

Voluntary movement is hypothesized to rely on a limited number of muscle synergies, the recruitment of which translates task goals into effective muscle activity. In this study, we investigated how to analytically characterize the functional role of different types of muscle synergies in task performance. To this end, we recorded a comprehensive dataset of muscle activity during a variety of whole-body pointing movements. We decomposed the electromyographic (EMG) signals using a space-by-time modularity model which encompasses the main types of synergies. We then used a task decoding and information theoretic analysis to probe the role of each synergy by mapping it to specific task features. We found that the temporal and spatial aspects of the movements were encoded by different temporal and spatial muscle synergies, respectively, consistent with the intuition that there should a correspondence between major attributes of movement and major features of synergies. This approach led to the development of a novel computational method for comparing muscle synergies from different participants according to their functional role. This functional similarity analysis yielded a small set of temporal and spatial synergies that describes the main features of whole-body reaching movements.

Exploring novel objective functions for simulating muscle coactivation in the neck

Musculoskeletal modeling allows for analysis of individual muscles in various situations. However, current techniques to realistically simulate muscle response when significant amounts of intentional coactivation is required are inadequate. This would include stiffening the neck or spine through muscle coactivation in preparation for perturbations or impacts. Muscle coactivation has been modeled previously in the neck and spine using optimization techniques that seek to maximize the joint stiffness by maximizing total muscle activation or muscle force. These approaches have not sought to replicate human response, but rather to explore the possible effects of active muscle. Coactivation remains a challenging feature to include in musculoskeletal models, and may be improved by extracting optimization objective functions from experimental data. However, the components of such an objective function must be known before fitting to experimental data. This study explores the effect of components in several objective functions, in order to recommend components to be used for fitting to experimental data. Four novel approaches to modeling coactivation through optimization techniques are presented, two of which produce greater levels of stiffness than previous techniques. Simulations were performed using OpenSim and MATLAB cooperatively. Results show that maximizing the moment generated by a particular muscle appears analogous to maximizing joint stiffness. The approach of optimizing for maximum moment generated by individual muscles may be a good candidate for developing objective functions that accurately simulate muscle coactivation in complex joints. This new approach will be the focus of future studies with human subjects.

Movement goals encoded within the cortex and muscle synergies to reduce redundancy pre and post-stroke. The relevance for gait rehabilitation and the prescription of walking-aids. A literature review and scholarly discussion

Current knowledge of neural and neuromuscular processes controlling gait and movement as well as an understanding of how these mechanisms change following stroke is an important basis for the development of effective rehabilitation interventions. To support the translation of findings from basic research into useful treatments in clinical practice, up-to-date neuroscience should be presented in forms accessible to all members of the multidisciplinary team. In this review we discuss aspects of cortical control of gait and movement, muscle synergies as a way of translating cortical commands into specific muscle activity and as an efficient means of reducing neural and musculoskeletal redundancy. We discuss how these mechanisms change following stroke, potential consequences for gait rehabilitation, and the prescription and use of walking-aids as well as areas requiring further research.

Evidence for sparse synergies in grasping actions

Converging evidence shows that hand-actions are controlled at the level of synergies and not single muscles. One intriguing aspect of synergy-based action-representation is that it may be intrinsically sparse and the same synergies can be shared across several distinct types of hand-actions. Here, adopting a normative angle, we consider three hypotheses for hand-action optimal-control: sparse-combination hypothesis (SC) – sparsity in the mapping between synergies and actions - i.e., actions implemented using a sparse combination of synergies; sparse-elements hypothesis (SE) – sparsity in synergy representation – i.e., the mapping between degrees-of-freedom (DoF) and synergies is sparse; double-sparsity hypothesis (DS) – a novel view combining both SC and SE – i.e., both the mapping between DoF and synergies and between synergies and actions are sparse, each action implementing a sparse combination of synergies (as in SC), each using a limited set of DoFs (as in SE). We evaluate these hypotheses using hand kinematic data from six human subjects performing nine different types of reach-to-grasp actions. Our results support DS, suggesting that the best action representation is based on a relatively large set of synergies, each involving a reduced number of degrees-of-freedom, and that distinct sets of synergies may be involved in distinct tasks.

Patients with Duchenne and Becker muscular dystrophies are not more asymmetrical than healthy controls on timed performance of upper limb tasks

This study aimed to investigate possible asymmetries and relationships between performance of dominant and non-dominant upper limbs (UL) in patients with Duchenne and Becker muscular dystrophies (DMD/BMD), to compare UL performance of patients and healthy subjects and to investigate the relationship between timed performance of UL and age, motor function and muscle strength in DMD/BMD patients. Sixteen patients with DMD and 3 with BMD were evaluated with Jebsen-Taylor Test (timed performance), Vignos scale and Dimension 3 of Motor Function Measure (motor function), and Medical Research Council scale (muscle strength) on a single session. ANOVA showed no asymmetry between dominant and non-dominant UL, except in the writing subtest, in patients and in healthy controls. There were relationships between dominant and non-dominant UL performances. Correlations between timed performance, motor function and muscle strength were found, but age was not correlated with these variables. These findings may reduce the assessment time, prevent fatigue and provide more accurate clinical reasoning involving UL in DMD/BMD treatment.

Balance, Body Motion, and Muscle Activity After High-Volume Short-Term Dance-Based Rehabilitation in Persons With Parkinson Disease: A Pilot Study

BACKGROUND AND PURPOSE

The objectives of this pilot study were to (1) evaluate the feasibility and investigate the efficacy of a 3-week, high-volume (450 minutes per week) Adapted Tango intervention for community-dwelling individuals with mild-moderate Parkinson disease (PD) and (2) investigate the potential efficacy of Adapted Tango in modifying electromyographic (EMG) activity and center of body mass (CoM) displacement during automatic postural responses to support surface perturbations.

METHODS

Individuals with PD (n = 26) were recruited for high-volume Adapted Tango (15 lessons, 1.5 hour each over 3 weeks). Twenty participants were assessed with clinical balance and gait measures before and after the intervention. Nine participants were also assessed with support-surface translation perturbations.

RESULTS

Overall adherence to the intervention was 77%. At posttest, peak forward CoM displacement was reduced (4.0 ± 0.9 cm, pretest, vs 3.7 ± 1.1 cm, posttest; P = 0.03; Cohen's d = 0.30) and correlated to improvements on Berg Balance Scale (ρ = -0.68; P = 0.04) and Dynamic Gait Index (ρ = -0.75; P = 0.03). Overall antagonist onset time was delayed (27 ms; P = 0.02; d = 0.90) and duration was reduced (56 ms, ≈39%, P = 0.02; d = 0.45). Reductions in EMG magnitude were also observed (P < 0.05).

DISCUSSION AND CONCLUSIONS

Following participation in Adapted Tango, changes in kinematic and some EMG measures of perturbation responses were observed in addition to improvements in clinical measures. We conclude that 3-week, high-volume Adapted Tango is feasible and represents a viable alternative to longer duration adapted dance programs.Video Abstract available for more insights from the authors (see Supplemental Digital Content 1, http://links.lww.com/JNPT/A143).

Whole-Body Movements in Long-Term Weightlessness: Hierarchies of the Controlled Variables Are Gravity-Dependent

Switching between contexts affects the mechanisms underlying motion planning, in particular it may entail reranking the variables to be controlled in defining the motor solutions. Three astronauts performed multiple sessions of whole-body pointing, in normogravity before launch, in prolonged weightlessness onboard the International Space Station, and after return. The effect of gravity context on kinematic and dynamic components was evaluated. Hand trajectory was gravity independent; center-of-mass excursion was highly variable within and between subjects. The body-environment effort exchange, expressed as inertial ankle momentum, was systematically lower in weightlessness than in normogravity. After return on Earth, the system underwent a rapid 1-week readaptation. The study indicates that minimizing the control effort is given greater weight when optimizing the motor plan in weightlessness compared to normogravity: the hierarchies of the controlled variables are gravity dependent.

Mechanical effort predicts the selection of ankle over hip strategies in nonstepping postural responses

Experimental studies have shown that a continuum of ankle and hip strategies is used to restore posture following an external perturbation. Postural responses can be modeled by feedback control with feedback gains that optimize a specific objective. On the one hand, feedback gains that minimize effort have been used to predict muscle activity during perturbed standing. On the other hand, hip and ankle strategies have been predicted by minimizing postural instability and deviation from upright posture. It remains unclear, however, whether and how effort minimization influences the selection of a specific postural response. We hypothesize that the relative importance of minimizing mechanical work vs. postural instability influences the strategy used to restore upright posture. This hypothesis was investigated based on experiments and predictive simulations of the postural response following a backward support surface translation. Peak hip flexion angle was significantly correlated with three experimentally determined measures of effort, i.e., mechanical work, mean muscle activity and metabolic energy. Furthermore, a continuum of ankle and hip strategies was predicted in simulation when changing the relative importance of minimizing mechanical work and postural instability, with increased weighting of mechanical work resulting in an ankle strategy. In conclusion, the combination of experimental measurements and predictive simulations of the postural response to a backward support surface translation showed that the trade-off between effort and postural instability minimization can explain the selection of a specific postural response in the continuum of potential ankle and hip strategies.

Suboptimal Muscle Synergy Activation Patterns Generalize their Motor Function across Postures

We used a musculoskeletal model to investigate the possible biomechanical and neural bases of using consistent muscle synergy patterns to produce functional motor outputs across different biomechanical conditions, which we define as generalizability. Experimental studies in cats demonstrate that the same muscle synergies are used during reactive postural responses at widely varying configurations, producing similarly-oriented endpoint force vectors with respect to the limb axis. However, whether generalizability across postures arises due to similar biomechanical properties or to neural selection of a particular muscle activation pattern has not been explicitly tested. Here, we used a detailed cat hindlimb model to explore the set of feasible muscle activation patterns that produce experimental synergy force vectors at a target posture, and tested their generalizability by applying them to different test postures. We used three methods to select candidate muscle activation patterns: (1) randomly-selected feasible muscle activation patterns, (2) optimal muscle activation patterns minimizing muscle effort at a given posture, and (3) generalizable muscle activation patterns that explicitly minimized deviations from experimentally-identified synergy force vectors across all postures. Generalizability was measured by the deviation between the simulated force direction of the candidate muscle activation pattern and the experimental synergy force vectors at the test postures. Force angle deviations were the greatest for the randomly selected feasible muscle activation patterns (e.g., >100°), intermediate for effort-wise optimal muscle activation patterns (e.g., ~20°), and smallest for generalizable muscle activation patterns (e.g., <5°). Generalizable muscle activation patterns were suboptimal in terms of effort, often exceeding 50% of the maximum possible effort (cf. ~5% in minimum-effort muscle activation patterns). The feasible muscle activation ranges of individual muscles associated with producing a specific synergy force vector was reduced by ~45% when generalizability requirements were imposed. Muscles recruited in the generalizable muscle activation patterns had less sensitive torque-producing characteristics to changes in postures. We conclude that generalization of function across postures does not arise from limb biomechanics or a single optimality criterion. Muscle synergies may reflect acquired motor solutions globally tuned for generalizability across biomechanical contexts, facilitating rapid motor adaptation.

Long-term training modifies the modular structure and organization of walking balance control

How does long-term training affect the neural control of movements? Here we tested the hypothesis that long-term training leading to skilled motor performance alters muscle coordination during challenging, as well as nominal everyday motor behaviors. Using motor module (a.k.a., muscle synergy) analyses, we identified differences in muscle coordination patterns between professionally trained ballet dancers (experts) and untrained novices that accompanied differences in walking balance proficiency assessed using a challenging beam-walking test. During beam walking, we found that experts recruited more motor modules than novices, suggesting an increase in motor repertoire size. Motor modules in experts had less muscle coactivity and were more consistent than in novices, reflecting greater efficiency in muscle output. Moreover, the pool of motor modules shared between beam and overground walking was larger in experts compared with novices, suggesting greater generalization of motor module function across multiple behaviors. These differences in motor output between experts and novices could not be explained by differences in kinematics, suggesting that they likely reflect differences in the neural control of movement following years of training rather than biomechanical constraints imposed by the activity or musculoskeletal structure and function. Our results suggest that to learn challenging new behaviors, we may take advantage of existing motor modules used for related behaviors and sculpt them to meet the demands of a new behavior.

Neuromechanical principles underlying movement modularity and their implications for rehabilitation

Neuromechanical principles define the properties and problems that shape neural solutions for movement. Although the theoretical and experimental evidence is debated, we present arguments for consistent structures in motor patterns, i.e., motor modules, that are neuromechanical solutions for movement particular to an individual and shaped by evolutionary, developmental, and learning processes. As a consequence, motor modules may be useful in assessing sensorimotor deficits specific to an individual and define targets for the rational development of novel rehabilitation therapies that enhance neural plasticity and sculpt motor recovery. We propose that motor module organization is disrupted and may be improved by therapy in spinal cord injury, stroke, and Parkinson's disease. Recent studies provide insights into the yet-unknown underlying neural mechanisms of motor modules, motor impairment, and motor learning and may lead to better understanding of the causal nature of modularity and its underlying neural substrates.

Motor primitives--new data and future questions

Motor primitives allow integration across scales in the motor system and may link movement construction and circuit organization. This review examines support for primitives, and new data relating primitives to concrete circuit elements across species. Both kinematic motor primitives and muscle synergy/kinetic motor primitives are reviewed. Motor primitives allow a modular hierarchy that may be re-used by volitional systems in novel ways. They can provide a developmental bootstrap for ethologically important actions. Collections of primitives somewhat constrain motor acts, but at the same time sets of primitives facilitate the rapid construction of these constrained actions, and can allow use of simpler controls. Novel motor skill likely requires augmentation to transcend the constraints present in initial collections of low level motor primitives. The benefits and limitations of motor primitives and the recognized knowledge gaps and needs for future research are briefly discussed.

A general-purpose framework to simulate musculoskeletal system of human body: using a motion tracking approach

Computation of muscle force patterns that produce specified movements of muscle-actuated dynamic models is an important and challenging problem. This problem is an undetermined one, and then a proper optimization is required to calculate muscle forces. The purpose of this paper is to develop a general model for calculating all muscle activation and force patterns in an arbitrary human body movement. For this aim, the equations of a multibody system forward dynamics, which is considered for skeletal system of the human body model, is derived using Lagrange-Euler formulation. Next, muscle contraction dynamics is added to this model and forward dynamics of an arbitrary musculoskeletal system is obtained. For optimization purpose, the obtained model is used in computed muscle control algorithm, and a closed-loop system for tracking desired motions is derived. Finally, a popular sport exercise, biceps curl, is simulated by using this algorithm and the validity of the obtained results is evaluated via EMG signals.

A novel computational framework for deducing muscle synergies from experimental joint moments

Prior experimental studies have hypothesized the existence of a "muscle synergy" based control scheme for producing limb movements and locomotion in vertebrates. Such synergies have been suggested to consist of fixed muscle grouping schemes with the co-activation of all muscles in a synergy resulting in limb movement. Quantitative representations of these groupings (termed muscle weightings) and their control signals (termed synergy controls) have traditionally been derived by the factorization of experimentally measured EMG. This study presents a novel approach for deducing these weightings and controls from inverse dynamic joint moments that are computed from an alternative set of experimental measurements-movement kinematics and kinetics. This technique was applied to joint moments for healthy human walking at 0.7 and 1.7 m/s, and two sets of "simulated" synergies were computed based on two different criteria (1) synergies were required to minimize errors between experimental and simulated joint moments in a musculoskeletal model (pure-synergy solution) (2) along with minimizing joint moment errors, synergies also minimized muscle activation levels (optimal-synergy solution). On comparing the two solutions, it was observed that the introduction of optimality requirements (optimal-synergy) to a control strategy solely aimed at reproducing the joint moments (pure-synergy) did not necessitate major changes in the muscle grouping within synergies or the temporal profiles of synergy control signals. Synergies from both the simulated solutions exhibited many similarities to EMG derived synergies from a previously published study, thus implying that the analysis of the two different types of experimental data reveals similar, underlying synergy structures.

Perspectives on human-human sensorimotor interactions for the design of rehabilitation robots

Physical interactions between patients and therapists during rehabilitation have served as motivation for the design of rehabilitation robots, yet we lack a fundamental understanding of the principles governing such human-human interactions (HHI). Here we review the literature and pose important open questions regarding sensorimotor interaction during HHI that could facilitate the design of human-robot interactions (HRI) and haptic interfaces for rehabilitation. Based on the goals of physical rehabilitation, three subcategories of sensorimotor interaction are identified: sensorimotor collaboration, sensorimotor assistance, and sensorimotor education. Prior research has focused primarily on sensorimotor collaboration and is generally limited to relatively constrained visuomotor tasks. Moreover, the mechanisms by which performance improvements are achieved during sensorimotor cooperation with haptic interaction remains unknown. We propose that the effects of role assignment, motor redundancy, and skill level in sensorimotor cooperation should be explicitly studied. Additionally, the importance of haptic interactions may be better revealed in tasks that do not require visual feedback. Finally, cooperative motor tasks that allow for motor improvement during solo performance to be examined may be particularly relevant for rehabilitation robotics. Identifying principles that guide human-human sensorimotor interactions may lead to the development of robots that can physically interact with humans in more intuitive and biologically inspired ways, thereby enhancing rehabilitation outcomes.

Task constraints and minimization of muscle effort result in a small number of muscle synergies during gait

Finding muscle activity generating a given motion is a redundant problem, since there are many more muscles than degrees of freedom. The control strategies determining muscle recruitment from a redundant set are still poorly understood. One theory of motor control suggests that motion is produced through activating a small number of muscle synergies, i.e., muscle groups that are activated in a fixed ratio by a single input signal. Because of the reduced number of input signals, synergy-based control is low dimensional. But a major criticism on the theory of synergy-based control of muscles is that muscle synergies might reflect task constraints rather than a neural control strategy. Another theory of motor control suggests that muscles are recruited by optimizing performance. Optimization of performance has been widely used to calculate muscle recruitment underlying a given motion while assuming independent recruitment of muscles. If synergies indeed determine muscle recruitment underlying a given motion, optimization approaches that do not model synergy-based control could result in muscle activations that do not show the synergistic muscle action observed through electromyography (EMG). If, however, synergistic muscle action results from performance optimization and task constraints (joint kinematics and external forces), such optimization approaches are expected to result in low-dimensional synergistic muscle activations that are similar to EMG-based synergies. We calculated muscle recruitment underlying experimentally measured gait patterns by optimizing performance assuming independent recruitment of muscles. We found that the muscle activations calculated without any reference to synergies can be accurately explained by on average four synergies. These synergies are similar to EMG-based synergies. We therefore conclude that task constraints and performance optimization explain synergistic muscle recruitment from a redundant set of muscles.

Do muscle synergies reduce the dimensionality of behavior?

The muscle synergy hypothesis is an archetype of the notion of Dimensionality Reduction (DR) occurring in the central nervous system due to modular organization. Toward validating this hypothesis, it is important to understand if muscle synergies can reduce the state-space dimensionality while maintaining task control. In this paper we present a scheme for investigating this reduction utilizing the temporal muscle synergy formulation. Our approach is based on the observation that constraining the control input to a weighted combination of temporal muscle synergies also constrains the dynamic behavior of a system in a trajectory-specific manner. We compute this constrained reformulation of system dynamics and then use the method of system balancing for quantifying the DR; we term this approach as Trajectory Specific Dimensionality Analysis (TSDA). We then investigate the consequence of minimization of the dimensionality for a given task. These methods are tested in simulations on a linear (tethered mass) and a non-linear (compliant kinematic chain) system. Dimensionality of various reaching trajectories is compared when using idealized temporal synergies. We show that as a consequence of this Minimum Dimensional Control (MDC) model, smooth straight-line Cartesian trajectories with bell-shaped velocity profiles emerged as the optima for the reaching task. We also investigated the effect on dimensionality due to adding via-points to a trajectory. The results indicate that a trajectory and synergy basis specific DR of behavior results from muscle synergy control. The implications of these results for the synergy hypothesis, optimal motor control, motor development, and robotics are discussed.

Effort minimization and synergistic muscle recruitment for three-dimensional force generation

To generate a force at the hand in a given spatial direction and with a given magnitude the central nervous system (CNS) has to coordinate the recruitment of many muscles. Because of the redundancy in the musculoskeletal system, the CNS can choose one of infinitely many possible muscle activation patterns which generate the same force. What strategies and constraints underlie such selection is an open issue. The CNS might optimize a performance criterion, such as accuracy or effort. Moreover, the CNS might simplify the solution by constraining it to be a combination of a few muscle synergies, coordinated recruitment of groups of muscles. We tested whether the CNS generates forces by minimum effort recruitment of either individual muscles or muscle synergies. We compared the activation of arm muscles observed during the generation of isometric forces at the hand across multiple three-dimensional force targets with the activation predicted by either minimizing the sum of squared muscle activations or the sum of squared synergy activations. Muscle synergies were identified from the recorded muscle pattern using non-negative matrix factorization. To perform both optimizations we assumed a linear relationship between rectified and filtered electromyographic (EMG) signal which we estimated using multiple linear regressions. We found that the minimum effort recruitment of synergies predicted the observed muscle patterns better than the minimum effort recruitment of individual muscles. However, both predictions had errors much larger than the reconstruction error obtained by the synergies, suggesting that the CNS generates three-dimensional forces by sub-optimal recruitment of muscle synergies.

Differences in adaptation rates after virtual surgeries provide direct evidence for modularity

Whether the nervous system relies on modularity to simplify acquisition and control of complex motor skills remains controversial. To date, evidence for modularity has been indirect, based on statistical regularities in the motor commands captured by muscle synergies. Here we provide direct evidence by testing the prediction that in a truly modular controller it must be harder to adapt to perturbations that are incompatible with the modules. We investigated a reaching task in which human subjects used myoelectric control to move a mass in a virtual environment. In this environment we could perturb the normal muscle-to-force mapping, as in a complex surgical rearrangement of the tendons, by altering the mapping between recorded muscle activity and simulated force applied on the mass. After identifying muscle synergies, we performed two types of virtual surgeries. After compatible virtual surgeries, a full range of movements could still be achieved recombining the synergies, whereas after incompatible virtual surgeries, new or modified synergies would be required. Adaptation rates after the two types of surgery were compared. If synergies were only a parsimonious description of the regularities in the muscle patterns generated by a nonmodular controller, we would expect adaptation rates to be similar, as both types of surgeries could be compensated with similar changes in the muscle patterns. In contrast, as predicted by modularity, we found strikingly faster adaptation after compatible surgeries than after incompatible ones. These results indicate that muscle synergies are key elements of a modular architecture underlying motor control and adaptation.

Muscle synergies in neuroscience and robotics: from input-space to task-space perspectives

In this paper we review the works related to muscle synergies that have been carried-out in neuroscience and control engineering. In particular, we refer to the hypothesis that the central nervous system (CNS) generates desired muscle contractions by combining a small number of predefined modules, called muscle synergies. We provide an overview of the methods that have been employed to test the validity of this scheme, and we show how the concept of muscle synergy has been generalized for the control of artificial agents. The comparison between these two lines of research, in particular their different goals and approaches, is instrumental to explain the computational implications of the hypothesized modular organization. Moreover, it clarifies the importance of assessing the functional role of muscle synergies: although these basic modules are defined at the level of muscle activations (input-space), they should result in the effective accomplishment of the desired task. This requirement is not always explicitly considered in experimental neuroscience, as muscle synergies are often estimated solely by analyzing recorded muscle activities. We suggest that synergy extraction methods should explicitly take into account task execution variables, thus moving from a perspective purely based on input-space to one grounded on task-space as well.

Are muscle synergies useful for neural control?

The observation that the activity of multiple muscles can be well approximated by a few linear synergies is viewed by some as a sign that such low-dimensional modules constitute a key component of the neural control system. Here, we argue that the usefulness of muscle synergies as a control principle should be evaluated in terms of errors produced not only in muscle space, but also in task space. We used data from a force-aiming task in two dimensions at the wrist, using an electromyograms (EMG)-driven virtual biomechanics technique that overcomes typical errors in predicting force from recorded EMG, to illustrate through simulation how synergy decomposition inevitably introduces substantial task space errors. Then, we computed the optimal pattern of muscle activation that minimizes summed-squared muscle activities, and demonstrated that synergy decomposition produced similar results on real and simulated data. We further assessed the influence of synergy decomposition on aiming errors (AEs) in a more redundant system, using the optimal muscle pattern computed for the elbow-joint complex (i.e., 13 muscles acting in two dimensions). Because EMG records are typically not available from all contributing muscles, we also explored reconstructions from incomplete sets of muscles. The redundancy of a given set of muscles had opposite effects on the goodness of muscle reconstruction and on task achievement; higher redundancy is associated with better EMG approximation (lower residuals), but with higher AEs. Finally, we showed that the number of synergies required to approximate the optimal muscle pattern for an arbitrary biomechanical system increases with task-space dimensionality, which indicates that the capacity of synergy decomposition to explain behavior depends critically on the scope of the original database. These results have implications regarding the viability of muscle synergy as a putative neural control mechanism, and also as a control algorithm to restore movements.

Review and perspective: neuromechanical considerations for predicting muscle activation patterns for movement

Muscle coordination may be difficult or impossible to predict accurately based on biomechanical considerations alone because of redundancy in the musculoskeletal system. Because many solutions exist for any given movement, the role of the nervous system in further constraining muscle coordination patterns for movement must be considered in both healthy and impaired motor control. On the basis of computational neuromechanical analyses of experimental data combined with modeling techniques, we have demonstrated several such neural constraints on the temporal and spatial patterns of muscle activity during both locomotion and postural responses to balance perturbations. We hypothesize that subject-specific and trial-by-trial differences in muscle activation can be parameterized and understood by a hierarchical and low-dimensional framework that reflects the neural control of task-level goals. In postural control, we demonstrate that temporal patterns of muscle activity may be governed by feedback control of task-level variables that represent the overall goal-directed motion of the body. These temporal patterns then recruit spatially-fixed patterns of muscle activity called muscle synergies that produce the desired task-level biomechanical functions that require multijoint coordination. Moreover, these principles apply more generally to movement, and in particular to locomotor tasks in both healthy and impaired individuals. Overall, understanding the goals and organization of the neural control of movement may provide useful reduced dimension parameter sets to address the degrees-of-freedom problem in musculoskeletal movement control. More importantly, however, neuromechanical analyses may lend insight and provide a framework for understanding subject-specific and trial-by-trial differences in movement across both healthy and motor-impaired populations.