Endpoint force fluctuations reveal flexible rather than synergistic patterns of muscle cooperation

Muscle synergies for multidirectional isometric force generation during maintenance of upright standing posture

Muscle synergies are defined as coordinated recruitment of groups of muscles with specific activation balances and time profiles aimed at generating task-specific motor commands. While muscle synergies in postural control have been investigated primarily in reactive balance conditions, the neuromechanical contribution of muscle synergies during voluntary control of upright standing is still unclear. In this study, muscle synergies were investigated during the generation of isometric force at the trunk during the maintenance of standing posture. Participants were asked to maintain the steady-state upright standing posture while pulling forces of different magnitudes were applied at the level at the waist in eight horizontal directions. Muscle synergies were extracted by nonnegative matrix factorization from sixteen lower limb and trunk muscles. An average of 5-6 muscle synergies were sufficient to account for a wide variety of EMG waveforms associated with changes in the magnitude and direction of pulling forces. A cluster analysis partitioned the muscle synergies of the participants into a large group of clusters according to their similarity, indicating the use of a subjective combination of muscles to generate a multidirectional force vector in standing. Furthermore, we found a participant-specific distribution in the values of cosine directional tuning parameters of synergy amplitude coefficients, suggesting the existence of individual neuromechanical strategies to stabilize the whole-body posture. Our findings provide a starting point for the development of novel diagnostic tools to assess muscle coordination in postural control and lay the foundation for potential applications of muscle synergies in rehabilitation.

Stabilizing leaning postures with feedback controlled functional neuromuscular stimulation after trunk paralysis

Spinal cord injury (SCI) can cause paralysis of trunk and hip musculature that negatively impacts seated balance and ability to lean away from an upright posture and interact fully with the environment. Constant levels of electrical stimulation of peripheral nerves can activate typically paralyzed muscles and aid in maintaining a single upright seated posture. However, in the absence of a feedback controller, such seated postures and leaning motions are inherently unstable and unable to respond to perturbations. Three individuals with motor complete SCI who had previously received a neuroprosthesis capable of activating the hip and trunk musculature volunteered for this study. Subject-specific muscle synergies were identified through system identification of the lumbar moments produced via neural stimulation. Synergy-based calculations determined the real-time stimulation parameters required to assume leaning postures. When combined with a proportional, integral, derivative (PID) feedback controller and an accelerometer to infer trunk orientation, all individuals were able to assume non-erect postures of 30-40° flexion and 15° lateral bending. Leaning postures increased forward reaching capabilities by 10.2, 46.7, and 16 cm respectively for each subject when compared with no stimulation. Additionally, the leaning controllers were able to resist perturbations of up to 90 N, and all subjects perceived the leaning postures as moderately to very stable. Implementation of leaning controllers for neuroprostheses have the potential of expanding workspaces, increasing independence, and facilitating activities of daily living for individuals with paralysis.

Loss of finger control complexity and intrusion of flexor biases are dissociable in finger individuation impairment after stroke

The ability to control each finger independently is an essential component of human hand dexterity. A common observation of hand function impairment after stroke is the loss of this finger individuation ability, often referred to as enslavement, i.e., the unwanted coactivation of non-intended fingers in individuated finger movements. In the previous literature, this impairment has been attributed to several factors, such as the loss of corticospinal drive, an intrusion of flexor synergy due to upregulations of the subcortical pathways, and/or biomechanical constraints. These factors may or may not be mutually exclusive and are often difficult to tease apart. It has also been suggested, based on a prevailing impression, that the intrusion of flexor synergy appears to be an exaggerated pattern of the involuntary coactivations of task-irrelevant fingers seen in a healthy hand, often referred to as a flexor bias. Most previous studies, however, were based on assessments of enslavement in a single dimension (i.e., finger flexion/extension) that coincide with the flexor bias, making it difficult to tease apart the other aforementioned factors. Here, we set out to closely examine the nature of individuated finger control and finger coactivation patterns in all dimensions. Using a novel measurement device and a 3D finger-individuation paradigm, we aim to tease apart the contributions of lower biomechanical, subcortical constraints, and top-down cortical control to these patterns in both healthy and stroke hands. For the first time, we assessed all five fingers' full capacity for individuation. Our results show that these patterns in the healthy and paretic hands present distinctly different shapes and magnitudes that are not influenced by biomechanical constraints. Those in the healthy hand presented larger angular distances that were dependent on top-down task goals, whereas those in the paretic hand presented larger Euclidean distances that arise from two dissociable factors: a loss of complexity in finger control and the dominance of an intrusion of flexor bias. These results suggest that finger individuation impairment after stroke is due to two dissociable factors: the loss of finger control complexity present in the healthy hand reflecting a top-down neural control strategy and an intrusion of flexor bias likely due to an upregulation of subcortical pathways. Our device and paradigm are demonstrated to be a promising tool to assess all aspects of the dexterous capacity of the hand.

Generalizability of muscle synergies in isometric force generation versus point-to-point reaching in the human upper extremity workspace

Isometric force generation and kinematic reaching in the upper extremity has been found to be represented by a limited number of muscle synergies, even across task-specific variations. However, the extent of the generalizability of muscle synergies between these two motor tasks within the arm workspace remains unknown. In this study, we recorded electromyographic (EMG) signals from 13 different arm, shoulder, and back muscles of ten healthy individuals while they performed isometric and kinematic center-out target matches to one of 12 equidistant directional targets in the horizontal plane and at each of four starting arm positions. Non-negative matrix factorization was applied to the EMG data to identify the muscle synergies. Five and six muscle synergies were found to represent the isometric force generation and point-to-point reaches. We also found that the number and composition of muscle synergies were conserved across the arm workspace per motor task. Similar tuning directions of muscle synergy activation profiles were observed at different starting arm locations. Between the isometric and kinematic motor tasks, we found that two to four out of five muscle synergies were common in the composition and activation profiles across the starting arm locations. The greater number of muscle synergies that were involved in achieving a target match in the reaching task compared to the isometric task may explain the complexity of neuromotor control in arm reaching movements. Overall, our results may provide further insight into the neuromotor compartmentalization of shared muscle synergies between two different arm motor tasks and can be utilized to assess motor disabilities in individuals with upper limb motor impairments.

Neuromuscular control: from a biomechanist's perspective

Understanding neural control of movement necessitates a collaborative approach between many disciplines, including biomechanics, neuroscience, and motor control. Biomechanics grounds us to the laws of physics that our musculoskeletal system must obey. Neuroscience reveals the inner workings of our nervous system that functions to control our body. Motor control investigates the coordinated motor behaviours we display when interacting with our environment. The combined efforts across the many disciplines aimed at understanding human movement has resulted in a rich and rapidly growing body of literature overflowing with theories, models, and experimental paradigms. As a result, gathering knowledge and drawing connections between the overlapping but seemingly disparate fields can be an overwhelming endeavour. This review paper evolved as a need for us to learn of the diverse perspectives underlying current understanding of neuromuscular control. The purpose of our review paper is to integrate ideas from biomechanics, neuroscience, and motor control to better understand how we voluntarily control our muscles. As biomechanists, we approach this paper starting from a biomechanical modelling framework. We first define the theoretical solutions (i.e., muscle activity patterns) that an individual could feasibly use to complete a motor task. The theoretical solutions will be compared to experimental findings and reveal that individuals display structured muscle activity patterns that do not span the entire theoretical solution space. Prevalent neuromuscular control theories will be discussed in length, highlighting optimality, probabilistic principles, and neuromechanical constraints, that may guide individuals to families of muscle activity solutions within what is theoretically possible. Our intention is for this paper to serve as a primer for the neuromuscular control scientific community by introducing and integrating many of the ideas common across disciplines today, as well as inspire future work to improve the representation of neural control in biomechanical models.

Muscle synergy analysis of eight inter-limb coordination modes during human hands-knees crawling movement

In order to reveal in-depth the neuromuscular control mechanism of human crawling, this study carries out muscle synergy extraction and analysis on human hands-knees crawling under eight specific inter-limb coordination modes, which are defined according to the swing sequence of limbs and includes two-limb swing crawling modes and six single-limb swing crawling modes. Ten healthy adults participate in crawling data collection, and surface electromyography (sEMG) signals are recorded from 30 muscles of limbs and trunk. Non-negative matrix factorization (NNMF) algorithm is adopted for muscle synergy extraction, and a three-step muscle synergy analysis scheme is implemented by using the hierarchical clustering method. Based on results of muscle synergy extraction, 4 to 7 synergies are extracted from each participant in each inter-limb coordination mode, which supports the muscle synergy hypothesis to some extent, namely, central nervous system (CNS) controls the inter-limb coordination modes during crawling movement by recruiting a certain amount of muscle synergies, rather than a single muscle. In addition, when different participants crawl in the same inter-limb coordination mode, they share more temporal features in recruiting muscle synergies. Further, by extracting and analyzing intra-mode shared synergies among participants and inter-mode shared synergies among the eight inter-limb coordination modes, the CNS is found to realize single-limb swing crawling modes by recruiting the four inter-mode shared synergy structures related to the swing function of each limb in different orders, and realize the two-limb swing crawling modes by recruiting synchronously two intra-mode shared synergy structures. The research results of the muscle synergy analysis on the eight specific inter-limb coordination modes, on the one hand, provide a basis for muscle synergy hypothesis from the perspective of crawling motion, on the other hand, also provide a possible explanation for the choice of the inter-limb coordination mode in human crawling.

Muscle co-activation in the elderly contributes to control of hip and knee joint torque and endpoint force

We investigated the coordinated activity patterns of muscles based on cosine tuning in the elderly during an isometric force exertion task. We also clarified whether these coordinated activity patterns contribute to the control of hip and knee joint torque and endpoint force as co-activation. Preferred direction (PD) of activity for each muscle in 10 young and 8 older males was calculated from the lower limb muscle activity during isometric force exertion task in various directions. The covariance of endpoint force (η) was calculated from the exerted force data using a force sensor. Relationship between PD and η was used to examine the effect of muscle co-activation on the control of endpoint force. Co-activation between rectus femoris and semitendinosus/biceps femoris increased with changes in muscle PD. Additionally, the η values were significantly low, suggesting that co-activation of multiple muscles may contribute to endpoint force exertion. The mechanism for cooperative muscle activity is determined by the cosine tuning of the PD of each muscle, which affects the generation of hip and knee joint torque and endpoint force exertion. Co-activation of each muscle's PD changes with age, causing increased muscle co-activation to control torque and force. We demonstrated that co-activation in the elderly is a stabilizer of unsteady joints and a muscle control strategy for cooperative muscle activity.

Use of Surface Electromyography to Estimate End-Point Force in Redundant Systems: Comparison between Linear Approaches

Estimation of the force exerted by muscles from their electromyographic (EMG) activity may be useful to control robotic devices. Approximating end-point forces as a linear combination of the activities of multiple muscles acting on a limb may lead to an inaccurate estimation because of the dependency between the EMG signals, i.e., multi-collinearity. This study compared the EMG-to-force mapping estimation performed with standard multiple linear regression and with three other algorithms designed to reduce different sources of the detrimental effects of multi-collinearity: Ridge Regression, which performs an L2 regularization through a penalty term; linear regression with constraints from foreknown anatomical boundaries, derived from a musculoskeletal model; linear regression of a reduced number of muscular degrees of freedom through the identification of muscle synergies. Two datasets, both collected during the exertion of submaximal isometric forces along multiple directions with the upper limb, were exploited. One included data collected across five sessions and the other during the simultaneous exertion of force and generation of different levels of co-contraction. The accuracy and consistency of the EMG-to-force mappings were assessed to determine the strengths and drawbacks of each algorithm. When applied to multiple sessions, Ridge Regression achieved higher accuracy (R² = 0.70) but estimations based on muscle synergies were more consistent (differences between the pulling vectors of mappings extracted from different sessions: 67%). In contrast, the implementation of anatomical constraints was the best solution, both in terms of consistency (R² = 0.64) and accuracy (74%), in the case of different co-contraction conditions. These results may be used for the selection of the mapping between EMG and force to be implemented in myoelectrically controlled robotic devices.

Opportunities for Improving Motor Assessment and Rehabilitation After Stroke by Leveraging Video-Based Pose Estimation

ABSTRACT

Stroke is a leading cause of long-term disability in adults in the United States. As the healthcare system moves further into an era of digital medicine and remote monitoring, technology continues to play an increasingly important role in post-stroke care. In this Analysis and Perspective article, opportunities for using human pose estimation-an emerging technology that uses artificial intelligence to track human movement kinematics from simple videos recorded using household devices (e.g., smartphones, tablets)-to improve motor assessment and rehabilitation after stroke are discussed. The focus is on the potential of two key applications: (1) improving access to quantitative, objective motor assessment and (2) advancing telerehabilitation for persons post-stroke.

Muscle synergies are modified with improved task performance in skill learning

How do muscle synergies change as motor skills are learned? The purpose of this study was to investigate the relationship between synergy number and skill acquisition, and to examine learning-related changes in synergy structure and activation patterns. We performed muscle synergy analysis using non-negative matrix factorization to identify muscle synergies from activation patterns of ten major leg muscles before and after recreational cyclists learned a novel one-legged pedal force aiming task (Park, Van Emmerik, & Caldwell, 2021). Synergy number was defined as the smallest number of factors from the matrix factorization algorithm that could explain more than the predefined threshold values. Improvements in pedal force direction after practice occurred without a change in the number of muscle synergies (four), suggesting that task constraints (e.g. the need for smooth pedaling motion) in this novel targeting task may limit the CNS to the same number of muscle synergies before and after practice. Improved task performance while continuing to satisfy multiple biomechanical tasks was obtained with changes in structure (muscle weightings) for one synergy, and activation amplitudes without changes in timing or pattern for three synergies. In each crank cycle quadrant, multiple synergies were altered in either structure or activation amplitude, suggesting that the cooperative changes may be essential for improving task performance while producing a smooth pedaling motion. Changes in both synergy structure and activation levels could be muscle coordination strategies in motor skill learning.

Gaze control during reaching is flexibly modulated to optimize task outcome

When reaching for an object with the hand, the gaze is usually directed at the target. In a laboratory setting, fixation is strongly maintained at the reach target until the reaching is completed, a phenomenon known as "gaze anchoring." While conventional accounts of such tight eye-hand coordination have often emphasized the internal synergetic linkage between both motor systems, more recent optimal control theories regard motor coordination as the adaptive solution to task requirements. We here investigated to what degree gaze control during reaching is modulated by task demands. We adopted a gaze-anchoring paradigm in which participants had to reach for a target location. During the reach, they additionally had to make a saccadic eye movement to a salient visual cue presented at locations other than the target. We manipulated the task demands by independently changing reward contingencies for saccade reaction time (RT) and reaching accuracy. On average, both saccade RTs and reach error varied systematically with reward condition, with reach accuracy improving when the saccade was delayed. The distribution of the saccade RTs showed two types of eye movements: fast saccades with short RTs, and voluntary saccade with longer RTs. Increased reward for high reach accuracy reduced the probability of fast saccades but left their latency unchanged. The results suggest that gaze anchoring acts through a suppression of fast saccades, a mechanism that can be adaptively adjusted to the current task demands.NEW & NOTEWORTHY During visually guided reaching, our eyes usually fixate the target and saccades elsewhere are delayed ("gaze anchoring"). We here show that the degree of gaze anchoring is flexibly modulated by the reward contingencies of saccade latency and reach accuracy. Reach error became larger when saccades occurred earlier. These results suggest that early saccades are costly for reaching and the brain modulates inhibitory online coordination from the hand to the eye system depending on task requirements.

A Systematic Review of EMG Applications for the Characterization of Forearm and Hand Muscle Activity during Activities of Daily Living: Results, Challenges, and Open Issues

The role of the hand is crucial for the performance of activities of daily living, thereby ensuring a full and autonomous life. Its motion is controlled by a complex musculoskeletal system of approximately 38 muscles. Therefore, measuring and interpreting the muscle activation signals that drive hand motion is of great importance in many scientific domains, such as neuroscience, rehabilitation, physiotherapy, robotics, prosthetics, and biomechanics. Electromyography (EMG) can be used to carry out the neuromuscular characterization, but it is cumbersome because of the complexity of the musculoskeletal system of the forearm and hand. This paper reviews the main studies in which EMG has been applied to characterize the muscle activity of the forearm and hand during activities of daily living, with special attention to muscle synergies, which are thought to be used by the nervous system to simplify the control of the numerous muscles by actuating them in task-relevant subgroups. The state of the art of the current results are presented, which may help to guide and foster progress in many scientific domains. Furthermore, the most important challenges and open issues are identified in order to achieve a better understanding of human hand behavior, improve rehabilitation protocols, more intuitive control of prostheses, and more realistic biomechanical models.

Usefulness of Muscle Synergy Analysis in Individuals With Knee Osteoarthritis During Gait

OBJECTIVE

To clarify whether there are any muscle synergy changes in individuals with knee osteoarthritis, and to determine whether muscle synergy analysis could be applied to other musculoskeletal diseases.

METHODS

Subjects in this study included 11 young controls (YC), 10 elderly controls (EC), and 10 knee osteoarthritis patients (KOA). Gait was assessed on a split-belt treadmill at 3 km/h. A non-negative matrix factorization (NNMF) was applied to the electromyogram data matrix to extract muscle synergies. To assess the similarity of each module, we performed the NNMF analysis assuming four modules for all of the participants. Further, we calculated joint angles to compare the kinematic data between the module groups.

RESULTS

The number of muscle modules was significantly lower in the EC (2-3) and KOA (2-3) groups than in the YC group (3-4), which reflects the merging of late swing and early stance modules. The EC and KOA groups also showed greater knee flexion angles in the early stance phase. Contrarily, by focusing on the module structure, we found that the merging of early and late stance modules is characteristic in KOA.

CONCLUSION

The lower number of modules in the EC and KOA groups was due to the muscle co-contraction with increased knee flexion angle. Contrarily, the merging of early and late stance modules are modular structures specific to KOA and may be biomarkers for detecting KOA.

SIGNIFICANCE

Describing the changes in multiple muscle control associated with musculoskeletal degeneration can serve as a fundamental biomarker in joint disease.

Unexpected complexity of everyday manual behaviors

How does the brain control an effector as complex and versatile as the hand? One possibility is that neural control is simplified by limiting the space of hand movements. Indeed, hand kinematics can be largely described within 8 to 10 dimensions. This oft replicated finding has been construed as evidence that hand postures are confined to this subspace. A prediction from this hypothesis is that dimensions outside of this subspace reflect noise. To address this question, we track the hand of human participants as they perform two tasks-grasping and signing in American Sign Language. We apply multiple dimension reduction techniques and replicate the finding that most postural variance falls within a reduced subspace. However, we show that dimensions outside of this subspace are highly structured and task dependent, suggesting they too are under volitional control. We propose that hand control occupies a higher dimensional space than previously considered.

Muscle Synergy Control During Hand Reach Task on Varying Shoulder Configuration

Control of human arm in reaching task is a result of complex neural interaction involving central nervous and musculoskeletal system, where, group of muscle activation are planned through synergistic and coordinated recruitment, often to reach an optimal strategy. Aim of this paper is to explore muscle synergy distribution on several reaching task of similar elbow trajectory but changing shoulder configuration. A musculoskeletal model of human arm comprising shoulder, elbow and wrist joint have been designed and is used to calculate muscle activation required to perform three specific reaching tasks. Muscle synergy have been computed on the simulated activation to find a relation between synergy and energy requirement with the change of rotation and elevation of shoulder and its effect on the motion path of the elbow joint. These findings may help to define optimal joint configuration for a planned range of motion during rehabilitation exercises and also in developing neural prosthesis and myoelectric interfaces for efficient arm motion control.

Jaw Elevator Muscle Coordination during Rhythmic Mastication in Primates: Are Triplets Units of Motor Control?

The activity of mammal jaw elevator muscles during chewing has often been described using the concept of the triplet motor pattern, in which triplet I (balancing side superficial masseter and medial pterygoid; working side posterior temporalis) is consistently activated before triplet II (working side superficial masseter and medial pterygoid; balancing side posterior temporalis), and each triplet of muscles is recruited and modulated as a unit. Here, new measures of unison, synchrony, and coordination are used to determine whether in 5 primate species (Propithecus verreauxi, Eulemur fulvus, Papio anubis, Macaca fuscata,and Pan troglodytes)muscles in the same triplet are active more in unison, are more synchronized, and are more highly coordinated than muscles in different triplets. Results show that triplet I muscle pairs are active more in unison than other muscle pairs in Eulemur, Macaca, and Papio,buttriplet muscle pairs are mostly not more tightly synchronized than non-triplet pairs. Triplet muscles are more coordinated during triplet pattern cycles than non-triplet cycles, while non-triplet muscle pairs are more coordinated during non-triplet cycles than triplet cycles. These results suggest that the central nervous system alters patterns of coordination between cycles, recruiting triplet muscles as a coordinated unit during triplet cycles but employing a different pattern of muscle coordination during non-triplet cycles. The triplet motor pattern may simplify modulation of rhythmic mastication by being one possible unit of coordination that can be recruited on a cycle-to-cycle basis.

Motor modules account for active perception of force

Despite longstanding evidence suggesting a relation between action and perception, the mechanisms underlying their integration are still unclear. It has been proposed that to simplify the sensorimotor integration processes underlying active perception, the central nervous system (CNS) selects patterns of movements aimed at maximizing sampling of task-related sensory input. While previous studies investigated the action-perception loop focusing on the role of higher-level features of motor behavior (e.g., kinematic invariants, effort), the present study explored and quantified the contribution of lower-level organization of motor control. We tested the hypothesis that the coordinated recruitment of group of muscles (i.e., motor modules) engaged to counteract an external force contributes to participants’ perception of the same force. We found that: 1) a model describing the modulation of a subset of motor modules involved in the motor task accounted for about 70% of participants’ perceptual variance; 2) an alternative model, incompatible with the motor modules hypothesis, accounted for significantly lower variance of participants’ detection performance. Our results provide empirical evidence of the potential role played by muscle activation patterns in active perception of force. They also suggest that a modular organization of motor control may mediate not only coordination of multiple muscles, but also perceptual inference.

Internal Models in Biological Control

Rationality principles such as optimal feedback control and Bayesian inference underpin a probabilistic framework that has accounted for a range of empirical phenomena in biological sensorimotor control. To facilitate the optimization of flexible and robust behaviors consistent with these theories, the ability to construct internal models of the motor system and environmental dynamics can be crucial. In the context of this theoretic formalism, we review the computational roles played by such internal models and the neural and behavioral evidence for their implementation in the brain.

On the Relationship Between Muscle Synergies and Redundant Degrees of Freedom in Musculoskeletal Systems

It has been suggested that the human nervous system controls motions in the task (or operational) space. However, little attention has been given to the separation of the control of the task-related and task-irrelevant degrees of freedom. Aim: We investigate how muscle synergies may be used to separately control the task-related and redundant degrees of freedom in a computational model. Approach: We generalize an existing motor control model, and assume that the task and redundant spaces have orthogonal basis vectors. This assumption originates from observations that the human nervous system tightly controls the task-related variables, and leaves the rest uncontrolled. In other words, controlling the variables in one space does not affect the other space; thus, the actuations must be orthogonal in the two spaces. We implemented this assumption in the model by selecting muscle synergies that produce force vectors with orthogonal directions in the task and redundant spaces. Findings: Our experimental results show that the orthogonality assumption performs well in reconstructing the muscle activities from the measured kinematics/dynamics in the task and redundant spaces. Specifically, we found that approximately 70% of the variation in the measured muscle activity can be captured with the orthogonality assumption, while allowing efficient separation of the control in the two spaces. Implications: The developed motor control model is a viable tool in real-time simulations of musculoskeletal systems, as well as model-based control of bio-mechatronic systems, where a computationally efficient representation of the human motion controller is needed.

A Synergy-Based Motor Control Framework for the Fast Feedback Control of Musculoskeletal Systems

This paper presents a computational framework for the fast feedback control of musculoskeletal systems using muscle synergies. The proposed motor control framework has a hierarchical structure. A feedback controller at the higher level of hierarchy handles the trajectory planning and error compensation in the task space. This high-level task space controller only deals with the task-related kinematic variables, and thus is computationally efficient. The output of the task space controller is a force vector in the task space, which is fed to the low-level controller to be translated into muscle activity commands. Muscle synergies are employed to make this force-to-activation (F2A) mapping computationally efficient. The explicit relationship between the muscle synergies and task space forces allows for the fast estimation of muscle activations that result in the reference force. The synergy-enabled F2A mapping replaces a computationally heavy nonlinear optimization process by a vector decomposition problem that is solvable in real time. The estimation performance of the F2A mapping is evaluated by comparing the F2A-estimated muscle activities against the measured electromyography (EMG) data. The results show that the F2A algorithm can estimate the muscle activations using only the task-related kinematics/dynamics information with ∼70% accuracy. An example predictive simulation is also presented, and the results show that this feedback motor control framework can control arbitrary movements of a three-dimensional (3D) musculoskeletal arm model quickly and near optimally. It is two orders-of-magnitude faster than the optimal controller, with only 12% increase in muscle activities compared to the optimal. The developed motor control model can be used for real-time near-optimal predictive control of musculoskeletal system dynamics.

Neuromuscular synergies in motor control in normal and poststroke individuals

Muscle synergies are proposed to function as motor primitives that are modulated by frontal brain areas to construct a large repertoire of movement. This paper reviews the history of the development of our current theoretical understanding of nervous system-based motor control mechanisms and more specifically the concept of muscle synergies. Computational models of muscle synergies, especially the nonnegative matrix factorization algorithm, are discussed with specific reference to the changes in synergy control post-central nervous system (CNS) lesions. An alternative approach for motor control is suggested, exploiting a combination of synergies control or flexible muscle control used for gross motor skills and for individualized finger movements. Rehabilitation approaches, either supporting or inhibiting the use of basic movement patterns, are discussed in the context of muscle synergies. Applications are discussed for the use of advanced technologies that can promote the recovery and functioning of the human CNS after stroke.

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.

Synergies in coordination: a comprehensive overview of neural, computational, and behavioral approaches

At face value, the term "synergy" provides a unifying concept within a fractured field that encompasses complementary neural, computational, and behavioral approaches. However, the term is not used synonymously by different researchers but has substantially different meanings depending on the research approach. With so many operational definitions for the one term, it becomes difficult to use as either a descriptive or explanatory concept, yet it remains pervasive and apparently indispensable. Here we provide a summary of different approaches that invoke synergies in a descriptive or explanatory context, summarizing progress, not within the one approach, but across the theoretical landscape. Bernstein's framework of flexible hierarchical control may provide a unifying framework here, since it can incorporate divergent ideas about synergies. In the current motor control literature, synergy may refer to conceptually different processes that could potentially operate in parallel, across different levels within the same hierarchical control scheme. There is evidence for the concurrent existence of synergies with different features, both "hard-wired" and "soft-wired," and task independent and task dependent. By providing a comprehensive overview of the multifaceted ideas about synergies, our goal is to move away from the compartmentalization and narrow the focus on one level and promote a broader perspective on the control and coordination of movement.

Direction Modulation of Muscle Synergies in a Hand-Reaching Task

Functional tasks of the upper extremity can be executed by a variety of muscular patterns, independent of the direction, speed and load of the task. This large number of degrees of freedom imposes a significant control burden on the CNS. Previous studies suggested that the human cortex synchronizes a discrete number of neural functional units within the brainstem and spinal cord, i.e. muscle synergies, by linearly combining them to execute a great repertoire of movements. Further exploring this control mechanism, we aim to study whether a single set of muscle synergies might be generalized to express movements in different directions. This was implemented by using a modified version of the non-negative matrix factorization algorithm on EMG data sets of the upper extremity of healthy people. Our twelve participants executed hand-reaching movements in multiple directions. Muscle synergies that were extracted from movements to the center of the reaching space could be generalized to synergies for other movement directions. This finding was also supported by the application of a weighted correlation matrix, the similarity index and the results of the K-means cluster analysis. This might reinforce the notion that the CNS flexibly combines a single set of small number of synergies in different amplitudes to modulate movement for different directions.

Distributed representation of pelvic floor muscles in human motor cortex

Human motor cortex can activate pelvic floor muscles (PFM), but the motor cortical representation of the PFM is not well characterized. PFM representation is thought to be focused in the supplementary motor area (SMA). Here we examine the degree to which PFM representation is distributed between SMA and the primary motor cortex (M1), and how this representation is utilized to activate the PFM in different coordination patterns. We show that two types of coordination patterns involving PFM can be voluntarily accessed: one activates PFM independently of synergists and a second activates PFM prior to and in proportion with synergists (in this study, the gluteus maximus muscle - GMM). Functional magnetic resonance imaging (fMRI) showed that both coordination patterns involve overlapping activation in SMA and M1, suggesting the presence of intermingled but independent neural populations that access the different patterns. Transcranial magnetic stimulation (TMS) confirmed SMA and M1 representation for the PFM. TMS also showed that, equally for SMA and M1, PFM can be activated during rest but GMM can only be activated after voluntary drive to GMM, suggesting that these populations are distinguished by activation threshold. We conclude that PFM representation is broadly distributed in SMA and M1 in humans.

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.

Space-by-Time Modular Decomposition Effectively Describes Whole-Body Muscle Activity During Upright Reaching in Various Directions

The modular control hypothesis suggests that motor commands are built from precoded modules whose specific combined recruitment can allow the performance of virtually any motor task. Despite considerable experimental support, this hypothesis remains tentative as classical findings of reduced dimensionality in muscle activity may also result from other constraints (biomechanical couplings, data averaging or low dimensionality of motor tasks). Here we assessed the effectiveness of modularity in describing muscle activity in a comprehensive experiment comprising 72 distinct point-to-point whole-body movements during which the activity of 30 muscles was recorded. To identify invariant modules of a temporal and spatial nature, we used a space-by-time decomposition of muscle activity that has been shown to encompass classical modularity models. To examine the decompositions, we focused not only on the amount of variance they explained but also on whether the task performed on each trial could be decoded from the single-trial activations of modules. For the sake of comparison, we confronted these scores to the scores obtained from alternative non-modular descriptions of the muscle data. We found that the space-by-time decomposition was effective in terms of data approximation and task discrimination at comparable reduction of dimensionality. These findings show that few spatial and temporal modules give a compact yet approximate representation of muscle patterns carrying nearly all task-relevant information for a variety of whole-body reaching movements.

Modular Organization of Exploratory Force Development Under Isometric Conditions in the Human Arm

Muscle coordination of isometric force production can be explained by a smaller number of modules. Variability in force output, however, is higher during exploratory/transient force development phases than force maintenance phase, and it is not clear whether the same modular structure underlies both phases. In this study, eight neurologically-intact adults isometrically performed target force matches in 54 directions at hands, and electromyographic (EMG) data from eight muscles were parsed into four sequential phases. Despite the varying degree of motor complexity across phases (significant between-phase differences in EMG-force correlation, angular errors, and between-force correlations), the number/composition of motor modules were found equivalent across phases, suggesting that the CNS systematically modulated activation of the same set of motor modules throughout sequential force development.

Motor modules during adaptation to walking in a powered ankle exoskeleton

BACKGROUND

Modules of muscle recruitment can be extracted from electromyography (EMG) during motions, such as walking, running, and swimming, to identify key features of muscle coordination. These features may provide insight into gait adaptation as a result of powered assistance. The aim of this study was to investigate the changes (module size, module timing and weighting patterns) of surface EMG data during assisted and unassisted walking in an powered, myoelectric, ankle-foot orthosis (ankle exoskeleton).

METHODS

Eight healthy subjects wore bilateral ankle exoskeletons and walked at 1.2 m/s on a treadmill. In three training sessions, subjects walked for 40 min in two conditions: unpowered (10 min) and powered (30 min). During each session, we extracted modules of muscle recruitment via nonnegative matrix factorization (NNMF) from the surface EMG signals of ten muscles in the lower limb. We evaluated reconstruction quality for each muscle individually using R2 and normalized root mean squared error (NRMSE). We hypothesized that the number of modules needed to reconstruct muscle data would be the same between conditions and that there would be greater similarity in module timings than weightings.

RESULTS

Across subjects, we found that six modules were sufficient to reconstruct the muscle data for both conditions, suggesting that the number of modules was preserved. The similarity of module timings and weightings between conditions was greater then random chance, indicating that muscle coordination was also preserved. Motor adaptation during walking in the exoskeleton was dominated by changes in the module timings rather than module weightings. The segment number and the session number were significant fixed effects in a linear mixed-effect model for the increase in R2 with time.

CONCLUSIONS

Our results show that subjects walking in a exoskeleton preserved the number of modules and the coordination of muscles within the modules across conditions. Training (motor adaptation within the session and motor skill consolidation across sessions) led to improved consistency of the muscle patterns. Subjects adapted primarily by changing the timing of their muscle patterns rather than the weightings of muscles in the modules. The results of this study give new insight into strategies for muscle recruitment during adaptation to a powered ankle exoskeleton.

Similarity of muscle synergies extracted from the lower limb including the deep muscles between level and uphill treadmill walking

This study aimed to examine muscle synergies involving the deeper muscles of the lower limb during level and uphill treadmill walking. Seven men and five women walked on a treadmill at three speeds (60, 80, and 100m/min) and two grades (level and 10% grade). Surface electromyographic (EMG) signals were recorded from 10 muscles of the lower limb, including vastus intermedius, adductor magnus, and adductor longus. Muscle synergies were extracted applying non-negative matrix factorization, and the relative co-activation across muscles and the temporal information of synergy recruitment were identified by the muscle synergy vector and synergy activation coefficient, respectively. Correlation coefficients between a pair of synergy vectors during level and uphill walking were analyzed as a similarity index, with the similarity criterion at r=0.76. Changes in synergy activation coefficients between the walking conditions were evaluated by cross-correlation analysis. The mean number of synergies ranged from 3.8 to 4.0 across all conditions, and they were not significantly different between level and uphill walking conditions. Similarity between walking conditions was high (r>0.76) for three muscle synergies, but not for one synergy that mainly consisted of the quadriceps femoris. The inter-condition similarity of the synergy activation coefficients was high for the four synergies, and a significant lag time for synergy 2, which consisted mainly of the activity of medial gastrocnemius, was found at 60 and 80m/min. The muscle synergies extracted from the lower limb involving the deeper muscles appear to be consistent during level and uphill treadmill walking.

Different contributions of primary motor cortex, reticular formation, and spinal cord to fractionated muscle activation

Coordinated movement requires patterned activation of muscles. In this study, we examined differences in selective activation of primate upper limb muscles by cortical and subcortical regions. Five macaque monkeys were trained to perform a reach and grasp task, and electromyogram (EMG) was recorded from 10 to 24 muscles while weak single-pulse stimuli were delivered through microelectrodes inserted in the motor cortex (M1), reticular formation (RF), or cervical spinal cord (SC). Stimulus intensity was adjusted to a level just above threshold. Stimulus-evoked effects were assessed from averages of rectified EMG. M1, RF, and SC activated 1.5 ± 0.9, 1.9 ± 0.8, and 2.5 ± 1.6 muscles per site (means ± SD); only M1 and SC differed significantly. In between recording sessions, natural muscle activity in the home cage was recorded using a miniature data logger. A novel analysis assessed how well natural activity could be reconstructed by stimulus-evoked responses. This provided two measures: normalized vector length L, reflecting how closely aligned natural and stimulus-evoked activity were, and normalized residual R, measuring the fraction of natural activity not reachable using stimulus-evoked patterns. Average values for M1, RF, and SC were L = 119.1 ± 9.6, 105.9 ± 6.2, and 109.3 ± 8.4% and R = 50.3 ± 4.9, 56.4 ± 3.5, and 51.5 ± 4.8%, respectively. RF was significantly different from M1 and SC on both measurements. RF is thus able to generate an approximation to the motor output with less activation than required by M1 and SC, but M1 and SC are more precise in reaching the exact activation pattern required. Cortical, brainstem, and spinal centers likely play distinct roles, as they cooperate to generate voluntary movements. NEW & NOTEWORTHY Brainstem reticular formation, primary motor cortex, and cervical spinal cord intermediate zone can all activate primate upper limb muscles. However, brainstem output is more efficient but less precise in producing natural patterns of motor output than motor cortex or spinal cord. We suggest that gross muscle synergies from the reticular formation are sculpted and refined by motor cortex and spinal circuits to reach the finely fractionated output characteristic of dexterous primate upper limb movements.

Muscle synergies are similar when typically developing children walk on a treadmill at different speeds and slopes

BACKGROUND

The aim of this study was to determine whether changes in synergies relate to changes in gait while walking on a treadmill at multiple speeds and slopes. The hypothesis was that significant changes in movement pattern would not be accompanied by significant changes in synergies, suggesting that synergies are not dependent on the mechanical constraints but are instead neurological in origin.

METHODS

Sixteen typically developing children walked on a treadmill for nine combinations (stages) of different speeds and slopes while simultaneously collecting kinematics, kinetics, and surface electromyography (EMG) data. The kinematics for each stride were summarized using a modified version of the Gait Deviation Index that only includes the sagittal plane. The kinetics for each stride were summarized using a modified version of the Gait Deviation Index - Kinetic which includes sagittal plane moments and powers. Within each synergy group, the correlations of the synergies were calculated between the treadmill stages.

RESULTS

While kinematics and kinetics were significantly altered at the highest slope compared to level ground when walking on a treadmill, synergies were similar across stages.

CONCLUSIONS

The high correlations between synergies across stages indicate that neuromuscular control strategies do not change as children walk at different speeds and slopes on a treadmill. However, the multiple significant differences in kinematics and kinetics between stages indicate real differences in movement pattern. This supports the theory that synergies are neurological in origin and not simply a response to the biomechanical task constraints.

Action Direction of Muscle Synergies in Voluntary Multi-Directional Postural Control

A muscle synergy is a coordinative structure of muscles that has been proposed as a strategy to reduce the number of variables that the central nervous system (CNS) has to address in motor tasks. In this article, the mechanical contribution of muscle synergies and coordinative structures of muscles in voluntary multi-directional postural control were investigated. The task for healthy, young subjects was to shift and align their center of pressure (COP) to targets dispersed in 12 different directions in the horizontal plane by leaning their bodies for 10 s. Electromyograms (EMGs) of 18 muscles and COPs were recorded in the experiment. Muscle synergies were extracted using non-negative matrix factorization (NMF), and the structure of coordinative modules to keep the posture leaning toward various directions was disclosed. Then the directional properties, such as the mechanical role (i.e., action directions, we use ADs as abbreviation below), of muscle synergies and muscles were estimated using an electromyogram-weighted averaging (EWA) method, which is based on a cross-correlation between the fluctuations in the activation of muscle synergies and the COP. The results revealed that the ADs of muscle synergies were almost uniformly distributed in the task space in most of the subjects, which indicates that mechanical characteristics reduce the redundancy in postural control. In terms of the composition of muscle synergies and the ADs of individual muscles, we confirmed that muscle synergies in multi-directional postural control comprised a combination of several muscles, including various ADs, that generate torque at different joints.

Identification of shoulder muscle synergies in healthy subjects during an isometric task

Muscle Synergy method has been proposed in the literature to provide a lower dimensional representation of motor commands from the central nervous system (CNS). Studies on post-stroke patients highlighted how features such as the minimum number of motor synergies accounting for most of the data variance correlate with impairments and motor function. In this study, we target healthy subjects to establish normative data in isometric tasks involving shoulder muscles. Five subjects performed an isometric, two-dimensional force-matching task in twelve planar directions with two force levels across shoulder joint. Muscle synergies and their respective activation curves were computed from nine upper limb muscles via a nonnegative matrix factorization (NNMF) algorithm. Four synergies, on an average, were able to explain 95% of the variance in EMG datasets across all subjects. The cosine similarity of the muscle synergies among the subjects on an average is found to be 0.79±0.20. Two subjects revealed the presence of subject-specific synergies which will require further investigation before examining impaired subjects.

Dynamic structure of locomotor behavior in walking fruit flies

The function of the brain is unlikely to be understood without an accurate description of its output, yet the nature of movement elements and their organization remains an open problem. Here, movement elements are identified from dynamics of walking in flies, using unbiased criteria. On one time scale, dynamics of walking are consistent over hundreds of milliseconds, allowing elementary features to be defined. Over longer periods, walking is well described by a stochastic process composed of these elementary features, and a generative model of this process reproduces individual behavior sequences accurately over seconds or longer. Within elementary features, velocities diverge, suggesting that dynamical stability of movement elements is a weak behavioral constraint. Rather, long-term instability can be limited by the finite memory between these elementary features. This structure suggests how complex dynamics may arise in biological systems from elements whose combination need not be tuned for dynamic stability.

Neural basis for hand muscle synergies in the primate spinal cord

Grasping is a highly complex movement that requires the coordination of multiple hand joints and muscles. Muscle synergies have been proposed to be the functional building blocks that coordinate such complex motor behaviors, but little is known about how they are implemented in the central nervous system. Here we demonstrate that premotor interneurons (PreM-INs) in the primate cervical spinal cord underlie the spatiotemporal patterns of hand muscle synergies during a voluntary grasping task. Using spike-triggered averaging of hand muscle activity, we found that the muscle fields of PreM-INs were not uniformly distributed across hand muscles but rather distributed as clusters corresponding to muscle synergies. Moreover, although individual PreM-INs have divergent activation patterns, the population activity of PreM-INs reflects the temporal activation of muscle synergies. These findings demonstrate that spinal PreM-INs underlie the muscle coordination required for voluntary hand movements in primates. Given the evolution of neural control of primate hand functions, we suggest that spinal premotor circuits provide the fundamental coordination of multiple joints and muscles upon which more fractionated control is achieved by superimposed, phylogenetically newer, pathways.

Children With and Without Dystonia Share Common Muscle Synergies While Performing Writing Tasks

Childhood dystonia is a movement disorder characterized by muscle overflow and variability. This is the first study that investigates upper limb muscle synergies in childhood dystonia with the twofold aim of deepening the understanding of neuromotor dysfunctions and paving the way to possible synergy-based myocontrol interfaces suitable for this neurological population. Nonnegative matrix factorization was applied to the activity of upper-limb muscles recorded during the execution of writing tasks in children with dystonia and age-matched controls. Despite children with dystonia presented compromised kinematics of the writing outcome, a strikingly similarity emerged in the number and structure of the synergy vectors extracted from children in the two groups. The analysis also revealed that the timing of activation of the synergy coefficients did not significantly differ, while the amplitude of the peaks presented a slight reduction. These results suggest that the synergy analysis has the ability of capturing the uncorrupted part of the electromyographic signal in dystonia. Such an ability supports a possible future use of muscle synergies in the design of myocontrol interfaces for children with dystonia.

Neuromuscular responses differ between slip-induced falls and recoveries in older adults

How does the robust control of walking and balance break down during a fall? Here, as a first step in identifying the neuromuscular determinants of falls, we tested the hypothesis that falls and recoveries are characterized by differences in neuromuscular responses. Using muscle synergy analysis, conventional onset latencies, and peak activity, we identified differences in muscle coordination between older adults who fell and those who recovered from a laboratory-induced slip. We found that subjects who fell recruited fewer muscle synergies than those who recovered, suggesting a smaller motor repertoire. During slip trials, compared with subjects who recovered, subjects who fell had delayed knee flexor and extensor onset times in the leading/slip leg, as well as different muscle synergy structure involving those muscles. Therefore, the ability to coordinate muscle activity around the knee in a timely manner may be critical to avoiding falls from slips. Unique to subjects who fell during slip trials were greater bilateral (interlimb) muscle activation and the recruitment of a muscle synergy with excessive coactivation. These differences in muscle coordination between subjects who fell and those who recovered could not be explained by differences in gait-related variables at slip onset (i.e., initial motion state) or variations in slip difficulty, suggesting that differences in muscle coordination may reflect differences in neural control of movement rather than biomechanical constraints imposed by perturbation or initial walking mechanics. These results are the first step in determining the causation of falls from the perspective of muscle coordination. They suggest that there may be a neuromuscular basis for falls that could provide new insights into treatment and prevention. Further research comparing the muscle coordination and mechanics of falls and recoveries within subjects is necessary to establish the neuromuscular causation of falls.

NEW & NOTEWORTHY

A central question relevant to the prevention of falls is: How does the robust control of walking and balance break down during a fall? Previous work has focused on muscle coordination during successful balance recoveries or the kinematics and kinetics of falls. Here, for the first time, we identified differences in the spatial and temporal coordination of muscles among older adults who fell and those who recovered from an unexpected slip.

Biomechanical Constraints Underlying Motor Primitives Derived from the Musculoskeletal Anatomy of the Human Arm

Neural control of movement can only be realized though the interaction between the mechanical properties of the limb and the environment. Thus, a fundamental question is whether anatomy has evolved to simplify neural control by shaping these interactions in a beneficial way. This inductive data-driven study analyzed the patterns of muscle actions across multiple joints using the musculoskeletal model of the human upper limb. This model was used to calculate muscle lengths across the full range of motion of the arm and examined the correlations between these values between all pairs of muscles. Musculoskeletal coupling was quantified using hierarchical clustering analysis. Muscle lengths between multiple pairs of muscles across multiple postures were highly correlated. These correlations broadly formed two proximal and distal groups, where proximal muscles of the arm were correlated with each other and distal muscles of the arm and hand were correlated with each other, but not between groups. Using hierarchical clustering, between 11 and 14 reliable muscle groups were identified. This shows that musculoskeletal anatomy does indeed shape the mechanical interactions by grouping muscles into functional clusters that generally match the functional repertoire of the human arm. Together, these results support the idea that the structure of the musculoskeletal system is tuned to solve movement complexity problem by reducing the dimensionality of available solutions.

Constraining movement reveals motor capability in chronic stroke: an initial study

OBJECTIVE

To determine if persons with chronic stroke and decreased hip and knee flexion during swing can walk with improved swing-phase kinematics when the task demands constrained gait to the sagittal plane.

DESIGN

A one-day, within-subject design comparing gait kinematics under two conditions: Unconstrained treadmill walking and a constrained condition in which the treadmill walking space is reduced to limit limb advancement to occur in the sagittal plane.

SETTING

Outpatient physical therapy clinic.

SUBJECTS

Eight individuals (mean age, 64.1 ±9.3, 2 F) with mild-moderate paresis were enrolled.

MAIN MEASURES

Spatiotemporal gait characteristics and swing-phase hip and knee range of motion during unconstrained and constrained treadmill walking were compared using paired t-test and Cohen's d ( d) to determine effect size.

RESULTS

There was a significant, moderate-to-large effect of the constraint on hip flexion ( p < 0.001, d = -1.1) during initial swing, and hip ( p < 0.05, d = -0.8) and knee ( p < 0.001, d = -1.1) flexion during midswing. There was a moderate effect of constraint on terminal swing knee flexion ( p = 0.238, d = -0.6). Immediate and significant changes in step width ( p < 0.05, d = 0.9) and paretic step length ( p < 0.05, d = -0.5) were noted in the constrained condition compared with unconstrained.

CONCLUSION

Constraining the treadmill walking path altered the gait patterns among the study's participants. The immediate change during constrained walking suggests that patients with chronic stroke may have underlying movement capability that they do not preferentially utilize.

Is synergistic organisation of muscle coordination altered in people with lateral epicondylalgia? A case-control study

BACKGROUND

Lateral epicondylalgia is a common musculoskeletal disorder and is associated with deficits in the motor system including painful grip. This study compared coordination of forearm muscles (muscle synergies) during repeated gripping between individuals with and without lateral epicondylalgia.

METHODS

Twelve participants with lateral epicondylalgia and 14 controls performed 15 cyclical repetitions of sub-maximal (20% maximum grip force of asymptomatic arm), pain free dynamic gripping in four arm positions: shoulder neutral with elbow flexed to 90° and shoulder flexed to 90° with elbow extended both with forearm pronated and neutral. Muscle activity was recorded from extensor carpi radialis brevis/longus, extensor digitorum, flexor digitorum superficialis/profundus, and flexor carpi radialis, with intramuscular electrodes. Muscle synergies were extracted using non-negative matrix factorisation.

FINDINGS

Analysis of each position and participant, demonstrated that two muscle synergies accounted for >97% of the variance for both groups. Between-group differences were identified after electromyography patterns of the control group were used to reconstruct the patterns of the lateral epicondylalgia group. A greater variance accounted for was identified for the controls than lateral epicondylalgia (p=0.009). This difference might be explained by an additional burst of flexor digitorum superficialis electromyography during grip release in many lateral epicondylalgia participants.

INTERPRETATION

These data provide evidence of some differences in synergistic organisation of activation of forearm muscles between individuals with and without lateral epicondylalgia. Due to study design it is not possible to elucidate whether changes in the coordination of muscle activity during gripping are associated with the cause or effect of lateral epicondylalgia.

Modular Control of Treadmill vs Overground Running

Motorized treadmills have been widely used in locomotion studies, although a debate remains concerning the extrapolation of results obtained from treadmill experiments to overground locomotion. Slight differences between treadmill (TRD) and overground running (OVG) kinematics and muscle activity have previously been reported. However, little is known about differences in the modular control of muscle activation in these two conditions. Therefore, we aimed at investigating differences between motor modules extracted from TRD and OVG by factorization of multi-muscle electromyographic (EMG) signals. Twelve healthy men ran on a treadmill and overground at their preferred speed while we recorded tibial acceleration and surface EMG from 11 ipsilateral lower limb muscles. We extracted motor modules representing relative weightings of synergistic muscle activations by non-negative matrix factorization from 20 consecutive gait cycles. Four motor modules were sufficient to accurately reconstruct the EMG signals in both TRD and OVG (average reconstruction quality = 92±3%). Furthermore, a good reconstruction quality (80±7%) was obtained also when muscle weightings of one condition (either OVG or TRD) were used to reconstruct the EMG data from the other condition. The peak amplitudes of activation signals showed a similar timing (pattern) across conditions. The magnitude of peak activation for the module related to initial contact was significantly greater for OVG, whereas peak activation for modules related to leg swing and preparation to landing were greater for TRD. We conclude that TRD and OVG share similar muscle weightings throughout motion. In addition, modular control for TRD and OVG is achieved with minimal temporal adjustments, which were dependent on the phase of the running cycle.

How Variability and Effort Determine Coordination at Large Forces

Motor control is a challenging task for the central nervous system, since it involves redundant degrees of freedom, nonlinear dynamics of actuators and limbs, as well as noise. When an action is carried out, which factors does your nervous system consider to determine the appropriate set of muscle forces between redundant degrees-of-freedom? Important factors determining motor output likely encompass effort and the resulting motor noise. However, the tasks used in many previous motor control studies could not identify these two factors uniquely, as signal-dependent noise monotonically increases as a function of the effort. To address this, a recent paper introduced a force control paradigm involving one finger in each hand that can disambiguate these two factors. It showed that the central nervous system considers both force noise and amplitude, with a larger weight on the absolute force and lower weights on both noise and normalized force. While these results are valid for the relatively low force range considered in that paper, the magnitude of the force shared between the fingers for large forces is not known. This paper investigates this question experimentally, and develops an appropriate Markov chain Monte Carlo method in order to estimate the weightings given to these factors. Our results demonstrate that the force sharing strongly depends on the force level required, so that for higher force levels the normalized force is considered as much as the absolute force, whereas the role of noise minimization becomes negligible.

Muscle Synergies Heavily Influence the Neural Control of Arm Endpoint Stiffness and Energy Consumption

Much debate has arisen from research on muscle synergies with respect to both limb impedance control and energy consumption. Studies of limb impedance control in the context of reaching movements and postural tasks have produced divergent findings, and this study explores whether the use of synergies by the central nervous system (CNS) can resolve these findings and also provide insights on mechanisms of energy consumption. In this study, we phrase these debates at the conceptual level of interactions between neural degrees of freedom and tasks constraints. This allows us to examine the ability of experimentally-observed synergies—correlated muscle activations—to control both energy consumption and the stiffness component of limb endpoint impedance. In our nominal 6-muscle planar arm model, muscle synergies and the desired size, shape, and orientation of endpoint stiffness ellipses, are expressed as linear constraints that define the set of feasible muscle activation patterns. Quadratic programming allows us to predict whether and how energy consumption can be minimized throughout the workspace of the limb given those linear constraints. We show that the presence of synergies drastically decreases the ability of the CNS to vary the properties of the endpoint stiffness and can even preclude the ability to minimize energy. Furthermore, the capacity to minimize energy consumption—when available—can be greatly affected by arm posture. Our computational approach helps reconcile divergent findings and conclusions about task-specific regulation of endpoint stiffness and energy consumption in the context of synergies. But more generally, these results provide further evidence that the benefits and disadvantages of muscle synergies go hand-in-hand with the structure of feasible muscle activation patterns afforded by the mechanics of the limb and task constraints. These insights will help design experiments to elucidate the interplay between synergies and the mechanisms of learning, plasticity, versatility and pathology in neuromuscular systems.

The manner in which the nervous system coordinates the multiple muscles in the body is complex. It has been studied for decades, but a more full understanding is needed to enable the development of effective evaluation and treatment methods in disorders that cause neuromuscular disability such as cerebral palsy and stroke. In addition, the computational control of robots has and will continue to improve as the brain’s methods of muscular control are progressively reverse-engineered. Here, we study the capacity of arm muscles to regulate the stiffness of the hand for tasks such as using tools, stabilizing hand-held objects, and using doors. Using a simplified but generalizable model, we show that there will be necessary trade-offs in the functional capabilities of the limb if the nervous system chooses to control muscles in functional groups. This adds to our understanding of the consequences of different strategies to control muscles for real-world tasks with multiple and often competing demands. It enables future research and clinical experiments on the learning and execution of the multiple tasks of varying difficulty encountered in real life. It also sheds light on the design of control strategies for robots to operate in human and unstructured environments.

Normalized Index of Synergy for Evaluating the Coordination of Motor Commands

Humans perform various motor tasks by coordinating the redundant motor elements in their bodies. The coordination of motor outputs is produced by motor commands, as well properties of the musculoskeletal system. The aim of this study was to dissociate the coordination of motor commands from motor outputs. First, we conducted simulation experiments where the total elbow torque was generated by a model of a simple human right and left elbow with redundant muscles. The results demonstrated that muscle tension with signal-dependent noise formed a coordinated structure of trial-to-trial variability of muscle tension. Therefore, the removal of signal-dependent noise effects was required to evaluate the coordination of motor commands. We proposed a method to evaluate the coordination of motor commands, which removed signal-dependent noise from the measured variability of muscle tension. We used uncontrolled manifold analysis to calculate a normalized index of synergy. Simulation experiments confirmed that the proposed method could appropriately represent the coordinated structure of the variability of motor commands. We also conducted experiments in which subjects performed the same task as in the simulation experiments. The normalized index of synergy revealed that the subjects coordinated their motor commands to achieve the task. Finally, the normalized index of synergy was applied to a motor learning task to determine the utility of the proposed method. We hypothesized that a large part of the change in the coordination of motor outputs through learning was because of changes in motor commands. In a motor learning task, subjects tracked a target trajectory of the total torque. The change in the coordination of muscle tension through learning was dominated by that of motor commands, which supported the hypothesis. We conclude that the normalized index of synergy can be used to evaluate the coordination of motor commands independently from the properties of the musculoskeletal system.

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.

A model-based approach to predict muscle synergies using optimization: application to feedback control

This paper presents a new model-based method to define muscle synergies. Unlike the conventional factorization approach, which extracts synergies from electromyographic data, the proposed method employs a biomechanical model and formally defines the synergies as the solution of an optimal control problem. As a result, the number of required synergies is directly related to the dimensions of the operational space. The estimated synergies are posture-dependent, which correlate well with the results of standard factorization methods. Two examples are used to showcase this method: a two-dimensional forearm model, and a three-dimensional driver arm model. It has been shown here that the synergies need to be task-specific (i.e., they are defined for the specific operational spaces: the elbow angle and the steering wheel angle in the two systems). This functional definition of synergies results in a low-dimensional control space, in which every force in the operational space is accurately created by a unique combination of synergies. As such, there is no need for extra criteria (e.g., minimizing effort) in the process of motion control. This approach is motivated by the need for fast and bio-plausible feedback control of musculoskeletal systems, and can have important implications in engineering, motor control, and biomechanics.