Robustness of muscle synergies during visuomotor adaptation

Persistent changes in motor adaptation strategies after perturbations that require exploration of novel muscle activation patterns

Motor skill learning requires the acquisition of novel muscle patterns and a new control policy-a process that requires time. In contrast, motor adaptation often requires only the adjustment of existing muscle patterns-a fast process. By altering the mapping of muscle activations onto cursor movements in a myoelectrically controlled virtual environment, we are able to create perturbations that require either the recombination of existing muscle synergies (compatible virtual surgery) or the learning of novel muscle patterns (incompatible virtual surgery). We investigated whether adaptation to a compatible surgery is affected by prior exposure to an incompatible surgery, i.e., a motor skill learning task. We found that adaptation to a compatible surgery was characterized by a decrease in the quality of muscle pattern reconstructions using the original synergies and an increase in reaction times only after exposure to an incompatible surgery. In contrast, prior exposure to a compatible surgery did not affect the learning process required to overcome an incompatible surgery. The fact that exposure to an incompatible surgery had a profound effect on the muscle patterns during the adaptation to a subsequent compatible surgery and not vice versa suggests that null space exploration, possibly combined with an explicit exploration strategy, is engaged during exposure to an incompatible surgery and remains enhanced during a new adaptation episode. We conclude that motor skill learning, requiring novel muscle activation patterns, leads to changes in the exploration strategy employed during a subsequent perturbation.NEW & NOTEWORTHY Motor skill learning requires the acquisition of novel muscle patterns, whereas motor adaptation requires adjusting existing ones. We wondered whether training a new motor skill affects motor adaptation strategies. We show that learning an incompatible perturbation, a complex skill requiring new muscle synergies, affects the muscle patterns observed during adaption to a compatible perturbation, which requires adjusting the existing synergies. Our results suggest that motor skill learning results in persistent changes in the exploration strategy.

Muscle synergies in joystick manipulation

Extracting muscle synergies from surface electromyographic signals (sEMGs) during exercises has been widely applied to evaluate motor control strategies. This study explores the relationship between upper-limb muscle synergies and the performance of joystick manipulation tasks. Seventy-seven subjects, divided into three classes according to their maneuvering experience, were recruited to perform the left and right reciprocation of the joystick. Based on the motion encoder data, their manipulation performance was evaluated by the mean error, standard deviation, and extreme range of position of the joystick. Meanwhile, sEMG and acceleration signals from the upper limbs corresponding to the entire trial were collected. Muscle synergies were extracted from each subject's sEMG data by non-negative matrix factorization (NMF), based on which the synergy coordination index (SCI), which indicates the size of the synergy space and the variability of the center of activity (CoA), evaluated the temporal activation variability. The synergy pattern space and CoA of all participants were calculated within each class to analyze the correlation between the variability of muscle synergies and the manipulation performance metrics. The correlation level of each class was further compared. The experimental results evidenced a positive correlation between manipulation performance and maneuvering experience. Similar muscle synergy patterns were reflected between the three classes and the structure of the muscle synergies showed stability. In the class of rich maneuvering experience, the correlation between manipulation performance metrics and muscle synergy is more significant than in the classes of trainees and newbies, suggesting that long-term training and practicing can improve manipulation performance, stability of synergy compositions, and temporal activation variability but not alter the structure of muscle synergies determined by a specific task. Our approaches and findings could be applied to 1) reduce manipulation errors, 2) assist maneuvering training and evaluation to enhance transportation safety, and 3) design technical support for sports.

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.

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.

Detecting task-relevant spatiotemporal modules and their relation to motor adaptation

How does the central nervous system (CNS) control our bodies, including hundreds of degrees of freedom (DoFs)? A hypothesis to reduce the number of DoFs posits that the CNS controls groups of joints or muscles (i.e., modules) rather than each joint or muscle independently. Another hypothesis posits that the CNS primarily controls motion components relevant to task achievements (i.e., task-relevant components). Although the two hypotheses are examined intensively, the relationship between the two concepts remains unknown, e.g., unimportant modules may possess task-relevant information. Here, we propose a framework of task-relevant modules, i.e., modules relevant to task achievements, while combining the two concepts mentioned above in a data-driven manner. To examine the possible role of the task-relevant modules, we examined the modulation of the task-relevant modules in a motor adaptation paradigm in which trial-to-trial modifications of motor output are observable. The task-relevant modules, rather than conventional modules, showed adaptation-dependent modulations, indicating the relevance of task-relevant modules to trial-to-trial updates of motor output. Our method provides insight into motor control and adaptation via an integrated framework of modules and task-relevant components.

Visuomotor Adaptation of Lower Extremity Movements During Virtual Ball-Kicking Task

Sophisticated soccer players can skillfully manipulate a ball with their feet depending on the external environment. This ability of goal-directed control in the lower limbs has not been fully elucidated, although upper limb movements have been studied extensively using motor adaptation tasks. The purpose of this study was to clarify how the goal-directed movements of the lower limbs is acquired by conducting an experiment of visuomotor adaptation in ball-kicking movements. In this study, healthy young participants with and without experience playing soccer or futsal performed ball-kicking movements. They were instructed to move a cursor representing the right foot position and shoot a virtual ball to a target on a display in front of them. During the learning trials, the trajectories of the virtual ball were rotated by 15° either clockwise or counterclockwise relative to the actual ball direction. As a result, participants adapted their lower limb movements to novel visuomotor perturbation regardless of the soccer playing experience, and changed their whole trajectories not just the kicking position during adaptation. These results indicate that the goal-directed lower limb movements can be adapted to the novel environment. Moreover, it was suggested that fundamental structure of visuomotor adaptation is common between goal-directed movements in the upper and lower limbs.

Design of an Isometric End-Point Force Control Task for Electromyography Normalization and Muscle Synergy Extraction From the Upper Limb Without Maximum Voluntary Contraction

Muscle synergy analysis via surface electromyography (EMG) is useful to study muscle coordination in motor learning, clinical diagnosis, and neurorehabilitation. However, current methods to extract muscle synergies in the upper limb suffer from two major issues. First, the necessary normalization of EMG signals is performed via maximum voluntary contraction (MVC), which requires maximal isometric force production in each muscle. However, some individuals with motor impairments have difficulties producing maximal effort in the MVC task. In addition, the MVC is known to be highly unreliable, with widely different forces produced in repeated measures. Second, synergy extraction in the upper limb is typically performed with a multidirection reaching task. However, some participants with motor impairments cannot perform this task because it requires precise motor control. In this study, we proposed a new isometric rotating task that does not require precise motor control or large forces. In this task, participants maintain a cursor controlled by the arm end-point force on a target that rotates at a constant angular velocity at a designated force level. To relax constraints on motor control precision, the target is widened and blurred. To obtain a reference EMG value for normalization without requiring maximal effort, we estimated a linear relationship between joint torques and muscle activations. We assessed the reliability of joint torque normalization and synergy extraction in the rotating task in young neurotypical individuals. Compared with normalization with MVC, joint torque normalization allowed reliable EMG normalization at low force levels. In addition, the extraction of synergies was as reliable and more stable than with the multidirection reaching task. The proposed rotating task can, therefore, be used in future motor learning, clinical diagnosis, and neurorehabilitation studies.

Muscle synergies of multi-directional postural control in astronauts on Earth after a long-term stay in space

Movements of the human biological system have adapted to the physical environment under the 1-g gravitational force on Earth. However, the effects of microgravity in space on the underlying functional neuromuscular control behaviors remain poorly understood. Here, we aimed to elucidate the effects of prolonged exposure to a microgravity environment on the functional coordination of multiple muscle activities. The activities of 16 lower limb muscles of 5 astronauts who stayed in space for at least 3 months were recorded while they maintained multidirectional postural control during bipedal standing. The coordinated activation patterns of groups of muscles, i.e., muscle synergies, were estimated from the muscle activation datasets using a factorization algorithm. The experiments were repeated a total of 5 times for each astronaut, once before and 4 times after spaceflight. The compositions of muscle synergies were altered, with a constant number of synergies, after long-term exposure to microgravity, and the extent of the changes was correlated with the severity of the deficits in postural stability. Furthermore, the muscle synergies extracted 3 months after the return were similar in their activation profile but not in their muscle composition compared with those extracted in the preflight condition. These results suggest that the modularity in the neuromuscular system became reorganized to adapt to the microgravity environment and then possibly reoptimized to the new sensorimotor environment after the astronauts were re-exposed to a gravitational force. It is expected that muscle synergies can be used as physiological markers of the status of astronauts with gravity-dependent change.

Task space exploration improves adaptation after incompatible virtual surgeries

Humans have a remarkable capacity to learn new motor skills, a process that requires novel muscle activity patterns. Muscle synergies may simplify the generation of muscle patterns through the selection of a small number of synergy combinations. Learning new motor skills may then be achieved by acquiring novel muscle synergies. In a previous study, we used myoelectric control to construct virtual surgeries that altered the mapping from muscle activity to cursor movements. After compatible virtual surgeries, which could be compensated by recombining subject-specific muscle synergies, participants adapted quickly. In contrast, after incompatible virtual surgeries, which could not be compensated by recombining existing synergies, participants explored new muscle patterns, but failed to adapt. Here, we tested whether task space exploration can promote learning of novel muscle synergies, required to overcome an incompatible surgery. Participants performed the same reaching task as in our previous study, but with more time to complete each trial, thus allowing for exploration. We found an improvement in trial success after incompatible virtual surgeries. Remarkably, improvements in movement direction accuracy after incompatible surgeries occurred faster for corrective movements than for the initial movement, suggesting that learning of new synergies is more effective when used for feedback control. Moreover, reaction time was significantly higher after incompatible than after compatible virtual surgeries, suggesting an increased use of an explicit adaptive strategy to overcome incompatible surgeries. Taken together, these results indicate that exploration is important for skill learning and suggest that human participants, with sufficient time, can learn new muscle synergies.

Forward and backward walking share the same motor modules and locomotor adaptation strategies

Forward and backward walking are remarkably similar motor behaviors to the extent that backward walking has been described as a time-reversed version of forward walking. However, because they display different muscle activity patterns, it has been questioned if forward and backward walking share common control strategies. To investigate this point, we used a split-belt treadmill experimental paradigm designed to elicit healthy individuals' motor adaptation by changing the speed of one of the treadmill belts, while keeping the speed of the other belt constant. We applied this experimental paradigm to both forward and backward walking. We analyzed several adaptation parameters including step symmetry, stability, and energy expenditure as well as the characteristics of the synergies of lower-limb muscles. We found that forward and backward walking share the same muscle synergy modules. We showed that these modules are marked by similar patterns of adaptation driven by stability and energy consumption minimization criteria, both relying on modulating the temporal activation of the muscle synergies. Our results provide evidence that forward and backward walking are governed by the same control and adaptation mechanisms.

The Effect of ICA and Non-negative Matrix Factorization Analysis for EMG Signals Recorded From Multi-Channel EMG Sensors

Surface electromyography (EMG) measurements are affected by various noises such as power source and movement artifacts and adjacent muscle activities. Hardware solutions have been found that use multi-channel EMG signal to attenuate noise signals related to sensor positions. However, studies addressing the overcoming of crosstalk from EMG and the division of overlaid superficial and deep muscles are scarce. In this study, two signal decompositions-independent component analysis and non-negative matrix factorization-were used to create a low-dimensional input signal that divides noise, surface muscles, and deep muscles and utilizes them for movement classification based on direction. In the case of index finger movement, it was confirmed that the proposed decomposition method improved the classification performance with the least input dimensions. These results suggest a new method to analyze more dexterous movements of the hand by separating superficial and deep muscles in the future using multi-channel EMG signals.

Relations between Speed-Accuracy Trade-Off and Muscle Synergy in Isometric Contraction Tasks

Muscle synergy is the theory that movements are controlled by a module of coordinated combined muscles. This theory is thought to solve the degrees-of-freedom problem in the musculoskeletal system. Previous studies have investigated the robustness of muscle synergies under conditions such as varying speeds and required degrees of accuracy. One of the principles of human movement is that when movement becomes faster, spatial accuracy is reduced. This is called the "speed-accuracy trade-off" (SAT), and many models have been proposed to explain this phenomenon. Studies on muscle synergies have shown that muscle synergy modules are robust against changes in speed; however, the relationship between SAT and motor control by muscle synergies remains unclear. Therefore, we investigated the relationship between changes in spatial accuracy and changes in speed and muscle synergies from measured behavioral data and surface electromyography. This was achieved by performing an isometric contraction task in which subjects exerted a horizontal force with various movement speeds. The results showed that the module structures of muscle synergies were robust against speed changes, and that the neural commands to muscle synergies changed in response to speed changes. In addition, changes in spatial accuracy with variations in speed tended to increase when movement was performed with a single muscle synergy. These results suggest that the number of muscle synergies used for movement may affect movement accuracy.Clinical Relevance-The results of this study suggest that the number of muscle synergies used for movement affects spatial accuracy.

Motor Modules are Impacted by the Number of Reaching Directions Included in the Analysis

Muscle synergy analysis is commonly used to study how the nervous system coordinates the activation of a large number of muscles during human reaching. In synergy analysis, muscle activation data collected from various reaching directions are subjected to dimensionality reduction techniques to extract muscle synergies. Typically, muscle activation data are obtained only from a limited set of reaches with an inherent assumption that the performed reaches adequately represent all possible reaches. In this study, we investigated how the number of reaching directions included in the synergy analysis influences the validity of the extracted synergies. We used a musculoskeletal model to compute muscle activations required to perform 36 evenly spaced planar reaches. Nonnegative matrix factorization (NMF) and principal component analysis (PCA) were then used to extract reference synergies. We then selected several subsets of reaches and compared the ability of the extracted synergies from each subset to represent the muscle activation from all 36 reaches. We found that 6 reaches were required to extract valid synergies, and a further reduction in the number of reaches changed the composition of the resulting synergies. Further, we found that the choice of reaching directions included in the analysis for a given number of reaches also affected the validity of the extracted synergies. These findings indicate that both the number and the choice of reaching directions included in the analysis impacted the validity of the extracted synergies.

Reorganization of Muscle Coordination Underlying Motor Learning in Cycling Tasks

The hypothesis of modular control, which stands on the existence of muscle synergies as building blocks of muscle coordination, has been investigated in a great variety of motor tasks and species. Yet, its role during learning processes is still largely unexplored. To what extent is such modular control flexible, in terms of spatial structure and temporal activation, to externally or internally induced adaptations, is a debated issue. To address this question, we designed a biofeedback experiment to induce changes in the timing of muscle activations during leg cycling movements. The protocol consisted in delaying the peak of activation of one target muscle and using its electromyography (EMG) envelope as visual biofeedback. For each of the 10 healthy participants, the protocol was repeated for three different target muscles: Tibialis Anterioris (TA), Gastrocnemius Medialis (GM), and Vastus Lateralis (VL). To explore the effects of the conditioning protocol, we analyzed changes in the activity of eight lower limb muscles by applying different models of modular motor control [i.e., fixed spatial components (FSC) and fixed temporal components (FTC)]. Our results confirm the hypothesis that visual EMG biofeedback is able to induce changes in muscle coordination. Subjects were able to shift the peak of activation of the target muscle, with a delay of (49 ± 27°) across subjects and conditions. This time shift generated a reorganization of all the other muscles in terms of timing and amplitude. By using different models of modular motor control, we demonstrated that neither spatially invariant nor temporally invariant muscle synergies alone were able to account for these changes in muscle coordination after learning, while temporally invariant muscle synergies with adjustments in timing could capture most of muscle activity adaptations observed after the conditioning protocol. These results suggest that short-term learning in rhythmic tasks is built upon synergistic temporal commands that are robust to changes in the task demands.

Characterization of the Adaptation to Visuomotor Rotations in the Muscle Synergies Space

The adaptation to visuomotor rotations is one of the most studied paradigms of motor learning. Previous literature has presented evidence of a dependency between the process of adaptation to visuomotor rotations and the constrains dictated by the workspace of the biological actuators, the muscles, and their co-activation strategies, modeled using muscle synergies analysis. To better understand this relationship, we asked a sample of healthy individuals (N = 7) to perform two experiments aiming at characterizing the adaptation to visuomotor rotations in terms of rotations of the activation space of the muscle synergies during isometric reaching tasks. In both experiments, subjects were asked to adapt to visual rotations altering the position mapping between the force exerted on a fixed manipulandum and the movement of a cursor on a screen. In the first experiment subjects adapted to three different visuomotor rotation angles (30°, 40°, and 50° clockwise) applied to the whole experimental workspace. In the second experiment subjects adapted to a single visuomotor rotation angle (45° clockwise) applied to eight different sub-spaces of the whole workspace, while also performing movements in the rest of the unperturbed workspace. The results from the first experiment confirmed the hypothesis that visuomotor rotations induce rotations in the synergies activation workspace that are proportional to the visuomotor rotation angle. The results from the second experiment showed that rotations affecting limited sub-spaces of the whole workspace are adapted for by rotating only the synergies involved in the movement, with an angle proportional to the distance between the preferred angle of the synergy and the sub-space covered by the rotation. Moreover, we show that the activation of a synergy is only rotated when the sub-space covered by the visual perturbation is applied at the boundaries of the workspace of the synergy. We found these results to be consistent across subjects, synergies and sub-spaces. Moreover, we found a correlation between synergies and muscle rotations further confirming that the adaptation process can be well described, at the neuromuscular level, using the muscle synergies model. These results provide information on how visuomotor rotations can be used to induce a desired neuromuscular response.

The effects of motor modularity on performance, learning and generalizability in upper-extremity reaching: a computational analysis

It has been hypothesized that the central nervous system simplifies the production of movement by limiting motor commands to a small set of modules known as muscle synergies. Recently, investigators have questioned whether a low-dimensional controller can produce the rich and flexible behaviours seen in everyday movements. To study this issue, we implemented muscle synergies in a biomechanically realistic model of the human upper extremity and performed computational experiments to determine whether synergies introduced task performance deficits, facilitated the learning of movements, and generalized to different movements. We derived sets of synergies from the muscle excitations our dynamic optimizations computed for a nominal task (reaching in a plane). Then we compared the performance and learning rates of a controller that activated all muscles independently to controllers that activated the synergies derived from the nominal reaching task. We found that a controller based on synergies had errors within 1 cm of a full-dimensional controller and achieved faster learning rates (as estimated from computational time to converge). The synergy-based controllers could also accomplish new tasks-such as reaching to targets on a higher or lower plane, and starting from alternative initial poses-with average errors similar to a full-dimensional controller.

Does the cerebellum shape the spatiotemporal organization of muscle patterns? Insights from subjects with cerebellar ataxias

The role of the cerebellum in motor control has been investigated extensively, but its contribution to the muscle pattern organization underlying goal-directed movements is still not fully understood. Muscle synergies may be used to characterize multimuscle pattern organization irrespective of time (spatial synergies), in time irrespective of the muscles (temporal synergies), and both across muscles and in time (spatiotemporal synergies). The decomposition of muscle patterns as combinations of different types of muscle synergies offers the possibility to identify specific changes due to neurological lesions. In this study, we recorded electromyographic activity from 13 shoulder and arm muscles in subjects with cerebellar ataxias (CA) and in age-matched healthy subjects (HS) while they performed reaching movements in multiple directions. We assessed whether cerebellar damage affects the organization of muscle patterns by extracting different types of muscle synergies from the muscle patterns of each HS and using these synergies to reconstruct the muscle patterns of all other participants. We found that CA muscle patterns could be accurately captured only by spatial muscle synergies of HS. In contrast, there were significant differences in the reconstruction R² values for both spatiotemporal and temporal synergies, with an interaction between the two synergy types indicating a larger difference for spatiotemporal synergies. Moreover, the reconstruction quality using spatiotemporal synergies correlated with the severity of impairment. These results indicate that cerebellar damage affects the temporal and spatiotemporal organization, but not the spatial organization, of the muscle patterns, suggesting that the cerebellum plays a key role in shaping their spatiotemporal organization.NEW & NOTEWORTHY In recent studies, the decomposition of muscle activity patterns has revealed a modular organization of the motor commands. We show, for the first time, that muscle patterns of subjects with cerebellar damage share with healthy controls spatial, but not temporal and spatiotemporal, modules. Moreover, changes in spatiotemporal organization characterize the severity of the subject's impairment. These results suggest that the cerebellum has a specific role in shaping the spatiotemporal organization of the muscle patterns.

Comparison of muscle synergies extracted from both legs during cycling at different mechanical conditions

Muscle synergies are the building blocks for generating movement by the central nervous system (CNS). According to this hypothesis, CNS decreases the complexity of motor control by combination of a small number of muscle synergies. The aim of this work is to investigate similarity of muscle synergies during cycling across various mechanical conditions. Twenty healthy subjects performed three 6- min cycling tasks at over a range of rotational speed (40, 50, and 60 rpm) and resistant torque (3, 5, and 7 N/m). Surface electromyography (sEMG) signals were recorded during pedaling from eight muscles of the right and left legs. We extracted four synchronous muscle synergies by using the non-negative matrix factorization (NMF) method. Mean and standard deviation of the goodness of the signal reconstruction (R²) for all subjects was obtained 0.9898 ± 0.0535. We investigated the functional roles of both leg muscles during cycling by synchronous muscle synergy extraction. We compared the muscle synergies extracted from all subjects in all mechanical conditions. The total mean and standard deviation of the similarity of synergy vectors for all subjects in all mechanical conditions was obtained 0.8788 ± 0.0709. We found the high degrees of similarity among the sets of synchronous muscle synergies across mechanical conditions and also across different subjects. Our results demonstrated that different subjects at different mechanical conditions use the same motor control strategies for cycling, despite inter-individual variability of muscle patterns.

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.

Fixed muscle synergies and their potential to improve the intuitive control of myoelectric assistive technology for upper extremities

BACKGROUND

Users of myoelectric controlled assistive technology (AT) for upper extremities experience difficulties in controlling this technology in daily life, partly because the control is non-intuitive. Making the control of myoelectric AT intuitive may resolve the experienced difficulties. The present paper was inspired by the suggestion that intuitive control may be achieved if the control of myoelectric AT is based on neuromotor control principles. A significant approach within neurocomputational motor control suggests that myosignals are produced via a limited number of fixed muscle synergies. To effectively employ this approach in myoelectric AT, it is required that a limited number of muscle synergies is systematically exploited, also when muscles are used differently as required in controlling myoelectric AT. Therefore, the present study examined the systematic exploitation of muscle synergies when muscles were used differently to complete point-to-point movements with and without a rod.

METHODS

Healthy participants made multidirectional point-to-point movements with different end-effectors, i.e. with the index finger and with rods of different lengths. Myosignals were collected from 22 muscles in the arm, trunk, and back, and subsequently partitioned into muscle synergies per end-effector and for a pooled dataset including all end-effectors. The exploitation of these muscle synergies was assessed by evaluating the similarity of structure and explanatory ability of myosignals of per end-effector muscle synergies and the contribution of pooled muscle synergies across end-effectors.

RESULTS

Per end-effector, 3-5 muscle synergies could explain 73.8-81.1% of myosignal variation, whereas 6-8 muscle synergies from the pooled dataset also captured this amount of myosignal variation. Subsequent analyses showed that gradually different muscle synergies-extracted from separate end-effectors-were exploited across end-effectors. In line with this result, the order of contribution of muscle synergies extracted from the pooled dataset gradually reversed across end-effectors.

CONCLUSION

A limited number of muscle synergies was systematically exploited in the examined set of movements, indicating a potential for the fixed muscle synergy approach to improve the intuitive control of myoelectric AT. Given the gradual change in muscle synergy exploitation across end-effectors, future research should examine whether this potential can be extended to a larger range of movements and tasks.

Temporal and spatial asymmetries during stationary cycling cause different feedforward and feedback modifications in the muscular control of the lower limbs

Motor adaptations are useful for studying the way in which the lower limbs are controlled by the brain. However, motor adaptation paradigms for the lower limbs are typically based on locomotion tasks, where the necessity of maintaining postural stability is the main driver of adaptation and could possibly mask other underlying processes. In this study we investigated whether small temporal or spatial asymmetries can trigger motor adaptations during stationary cycling, where stability is not directly compromised. Fourteen healthy individuals participated in two experiments: in one of the experiments, the angle between the crank arms of the pedals was altered by 10° to induce a temporal asymmetry; in the other experiment, the length of the right pedal was shortened by 2.4 cm to induce a spatial asymmetry. We recorded the acceleration of the crank arms and the electromographic signals of 16 muscles (8 per leg). The analysis of the accelerometer data was used to investigate the presence of motor adaptations. Muscle synergy analysis was performed on each side to quantify changes in neuromuscular control. We found that motor adaptations are present in response to temporal asymmetries and are obtained by progressively shifting the activation patterns of two synergies on the right leg. Spatial asymmetries, on the other hand, appear to trigger a feedback-driven response that does not present an aftereffect. This response is characterized by a steplike decrease in activity in the right gastrocnemius when the asymmetry is present and likely reflects the altered task demands.

NEW & NOTEWORTHY The processes driving lower limb motor adaptations are not fully clear, and previous research appears to indicate that adaptations are mainly driven by stability. We show that lower limb adaptations can be obtained also in the absence of an explicit balance threat. We also show that adaptations are present when kinematic error cannot be compensated for, suggesting the presence of intrinsic error measures regulating the timing of activation of the two legs.

Muscle patterns underlying voluntary modulation of co-contraction

Manipulative actions involving unstable interactions with the environment require controlling mechanical impedance through muscle co-contraction. While much research has focused on how the central nervous system (CNS) selects the muscle patterns underlying a desired movement or end-point force, the coordination strategies used to achieve a desired end-point impedance have received considerably less attention. We recorded isometric forces at the hand and electromyographic (EMG) signals in subjects performing a reaching task with an external disturbance. In a virtual environment, subjects displaced a cursor by applying isometric forces and were instructed to reach targets in 20 spatial locations. The motion of the cursor was then perturbed by disturbances whose effects could be attenuated by increasing co-contraction. All subjects could voluntarily modulate co-contraction when disturbances of different magnitudes were applied. For most muscles, activation was modulated by target direction according to a cosine tuning function with an offset and an amplitude increasing with disturbance magnitude. Co-contraction was characterized by projecting the muscle activation vector onto the null space of the EMG-to-force mapping. Even in the baseline the magnitude of the null space projection was larger than the minimum magnitude required for non-negative muscle activations. Moreover, the increase in co-contraction was not obtained by scaling the baseline null space projection, scaling the difference between the null space projections in any block and the projection of the non-negative minimum-norm muscle vector, or scaling the difference between the null space projections in the perturbed blocks and the baseline null space projection. However, the null space projections in the perturbed blocks were obtained by linear combination of the baseline null space projection and the muscle activation used to increase co-contraction without generating any force. The failure of scaling rules in explaining voluntary modulation of arm co-contraction suggests that muscle pattern generation may be constrained by muscle synergies.

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.

Consistent visuomotor adaptations and generalizations can be achieved through different rotations of robust motor modules

Humans can adapt their motor commands in response to alterations in the movement environment. This is achieved by tuning different motor primitives, generating adaptations that can be generalized also to relevant untrained scenarios. A theory of motor primitives has shown that natural movements can be described as combinations of muscle synergies. Previous studies have shown that motor adaptations are achieved by tuning the recruitment of robust synergy modules. Here we tested if: 1) different synergistic tunings can be achieved in response to the same perturbations applied with different orders of exposure; 2) different synergistic tunings can explain different patterns of generalization of adaptation. We found that exposing healthy individuals to two visuomotor rotation perturbations covering different parts of the same workspace in a different order resulted in different tunings of the activation of the same set of synergies. Nevertheless, these tunings resulted in the same net biomechanical adaptation patterns. We also show that the characteristics of the different tunings correlate with the presence and extent of generalization of adaptation to untrained portions of the workspace. Our results confirm synergies as invariant motor primitives whose recruitment is dynamically tuned during motor adaptations.

Effect of Task Failure on Intermuscular Coherence Measures in Synergistic Muscles

The term “task failure” describes the point when a person is not able to maintain the level of force required by a task. As task failure approaches, the corticospinal command to the muscles increases to maintain the required level of force in the face of a decreased mechanical efficacy. Nevertheless, most motor tasks require the synergistic recruitment of several muscles. How this recruitment is affected by approaching task failure is still not clear. The increase in the corticospinal drive could be due to an increase in synergistic recruitment or to overlapping commands sent to the muscles individually. Herein, we investigated these possibilities by combining intermuscular coherence and synergy analysis on signals recorded from three muscles of the quadriceps during dynamic leg extension tasks. We employed muscle synergy analysis to investigate changes in the coactivation of the muscles. Three different measures of coherence were used. Pooled coherence was used to estimate the command synchronous to all three muscles, pairwise coherence the command shared across muscle pairs and residual coherence the command peculiar to each couple of muscles. Our analysis highlights an overall decrease in synergistic command at task failure and an intensification of the contribution of the nonsynergistic shared command.

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.

Empirical Evaluation of Voluntarily Activatable Muscle Synergies

The muscle synergy hypothesis assumes that individual muscle synergies are independent of each other and voluntarily controllable. However, this assumption has not been empirically tested. This study tested if human subjects can voluntarily activate individual muscle synergies extracted by non-negative matrix factorization (NMF), the standard mathematical method for synergy extraction. We defined the activation of a single muscle synergy as the generation of a muscle activity pattern vector parallel to the single muscle synergy vector. Subjects performed an isometric force production task with their right hand, and the 13 muscle activity patterns associated with their elbow and shoulder movements were measured. We extracted muscle synergies during the task using electromyogram (EMG) data and the NMF method with varied numbers of muscle synergies. The number (N) of muscle synergies was determined by using the variability accounted for (VAF, N_VAF) and the coefficient of determination (CD, N_CD). An additional muscle synergy model with N_AD was also considered. We defined a conventional muscle synergy as the muscle synergy extracted by the N_VAF, N_CD, and N_AD. We also defined an extended muscle synergy as the muscle synergy extracted by the N_EX> N_AD. To examine whether the individual muscle synergy was voluntarily activatable or not, we calculated the index of independent activation, which reflects similarities between a selected single muscle synergy and the current muscle activation pattern of the subject. Subjects were visually feed-backed the index of independent activation, then instructed to generate muscle activity patterns similar to the conventional and extended muscle synergies. As a result, an average of 90.8% of the muscle synergy extracted by the N_VAF was independently activated. However, the proportion of activatable muscle synergies extracted by N_CD and N_AD was lower. These results partly support the assumption of the muscle synergy hypothesis, i.e., that the conventional method can extract voluntarily and independently activatable muscle synergies by using the appropriate index of reconstruction. Moreover, an average of 25.5% of the extended muscle synergy was significantly activatable. This result suggests that the CNS can use extended muscle synergies to perform voluntary movements.

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.

Data sample size needed for analysis of kinematic and muscle synergies in healthy and stroke populations

Multiple studies have suggested the central nervous system (CNS) generates motions by using modular control of muscles and joints (synergies). However, the synergies reported by these studies are task dependent and might not reflect the true control strategies adopted by the CNS. Studying exploratory motions (EMs) can reveal biomechanical constraints and motor control strategies in healthy and clinical populations. The first logical step to consider EMs in study of motor synergies is to determine how much data is required to reliably and fully profile the motion patterns of an individual. Here we present how the quality of motor synergies analysis depends on the amount of EM data included in the analysis. We recruited 10 healthy and 10 post-stroke participants and collected electromyography (EMG) and joint motion data of their arms as they completed a motor exploration task. We compared the effects of clinical status and limb strength/dominance on the amount of data required to identify synergies. Clinical status had a significant elïect on the required amount of data for both datasets. Limb strength had a significant effect only for kinematic data. We determined the upper bound 95% confidence interval to set the amount of data required for synergy analysis in both populations: 235 sec for EMG data and 265 sec for kinematic data. Our results provide an important step toward using motor exploration in the study of healthy motor synergies and how stroke alters them.

On identifying kinematic and muscle synergies: a comparison of matrix factorization methods using experimental data from the healthy population

Human motor behavior is highly goal directed, requiring the central nervous system to coordinate different aspects of motion generation to achieve the motion goals. The concept of motor synergies provides an approach to quantify the covariation of joint motions and of muscle activations, i.e., elemental variables, during a task. To analyze goal-directed movements, factorization methods can be used to reduce the high dimensionality of these variables while accounting for much of the variance in large data sets. Three factorization methods considered in this paper are principal component analysis (PCA), nonnegative matrix factorization (NNMF), and independent component analysis (ICA). Bilateral human reaching data sets are used to compare the methods, and advantages of each are presented and discussed. PCA and NNMF had a comparable performance on both EMG and joint motion data and both outperformed ICA. However, NNMF's nonnegativity condition for activation of basis vectors is a useful attribute in identifying physiologically meaningful synergies, making it a more appealing method for future studies. A simulated data set is introduced to clarify the approaches and interpretation of the synergy structures returned by the three factorization methods.

NEW & NOTEWORTHY

Literature on comparing factorization methods in identifying motor synergies using numerically generated, simulation, and muscle activation data from animal studies already exists. We present an empirical evaluation of the performance of three of these methods on muscle activation and joint angles data from human reaching motion: principal component analysis, nonnegative matrix factorization, and independent component analysis. Using numerical simulation, we also studied the meaning and differences in the synergy structures returned by each method. The results can be used to unify approaches in identifying and interpreting motor synergies.

Changes in muscle coordination patterns induced by exposure to a viscous force field

Background

Robotic neurorehabilitation aims at promoting the recovery of lost function after neurological injury by leveraging strategies of motor learning. One important aspect of the rehabilitation process is the improvement of muscle coordination patterns, which can be drastically altered after stroke. However, it is not fully understood if and how robotic therapy can address these deficits. The aim of our study was to find how muscle coordination, analyzed from the perspective of motor modules, could change during motor adaptation to a dynamic environment generated by a haptic interface.

Methods

In our experiment we employed the traditional paradigm of exposure to a viscous force field to subjects that grasped the handle of an actuated joystick during a reaching movement (participants moved directly forward and back by 30 cm). EMG signals of ten muscles of the tested arm were recorded. We extracted motor modules from the pooled EMG data of all subjects and analyzed the muscle coordination patterns.

Results

We found that the participants reacted by using a coordination strategy that could be explained by a change in the activation of motor modules used during free motion and by two complementary modules. These complementary modules aggregated changes in muscle coordination, and evolved throughout the experiment eventually maintaining a comparable structure until the late phase of re-adaptation.

Conclusions

This result suggests that motor adaptation induced by the interaction with a robotic device can lead to changes in the muscle coordination patterns of the subject.

Modularity for Motor Control and Motor Learning

How the central nervous system (CNS) overcomes the complexity of multi-joint and multi-muscle control and how it acquires or adapts motor skills are fundamental and open questions in neuroscience. A modular architecture may simplify control by embedding features of both the dynamic behavior of the musculoskeletal system and of the task into a small number of modules and by directly mapping task goals into module combination parameters. Several studies of the electromyographic (EMG) activity recorded from many muscles during the performance of different tasks have shown that motor commands are generated by the combination of a small number of muscle synergies, coordinated recruitment of groups of muscles with specific amplitude balances or activation waveforms, thus supporting a modular organization of motor control. Modularity may also help understanding motor learning. In a modular architecture, acquisition of a new motor skill or adaptation of an existing skill after a perturbation may occur at the level of modules or at the level of module combinations. As learning or adapting an existing skill through recombination of modules is likely faster than learning or adapting a skill by acquiring new modules, compatibility with the modules predicts learning difficulty. A recent study in which human subjects used myoelectric control to move a mass in a virtual environment has tested this prediction. By altering the mapping between recorded muscle activity and simulated force applied on the mass, as in a complex surgical rearrangement of the tendons, it has been possible to show that it is easier to adapt to a perturbation that is compatible with the muscle synergies used to generate hand force than to a similar but incompatible perturbation. This result provides direct support for a modular organization of motor control and motor learning.

Learning and generalization in an isometric visuomotor task

Adaptation is a prominent feature of the human motor system and has been studied extensively in reaching movements. This study characterizes adaptation and generalization during isometric reaching in which the arm remains stationary and the participant controls a virtual cursor via force applied by the hand. We measured how learning of a visual cursor rotation generalizes across workspace 1) to determine the coordinate system that predominates visual rotation learning, and 2) to ascertain whether mapping type, namely position or velocity control, influences transfer. Participants performed virtual reaches to one of two orthogonal training targets with the applied rotation. In a new workspace, participants reached to a single target, similar to the training target in either hand or joint space. Furthermore, a control experiment measured within-workspace generalization to an orthogonal target. Across position and velocity mappings, learning transferred predominantly in intrinsic (joint) space, although the transfer was incomplete. The velocity mapping resulted in significantly larger aftereffects and broader within-workspace generalization than the position mapping, potentially due to slower peak speeds, longer trial times, greater target overshoot, or other factors. Although we cannot rule out a mixed reference frame in our task, the predominance of intrinsic coding of cursor kinematics in the isometric environment opposes the extrinsic coding of arm kinematics in real reaching but matches the intrinsic coding of dynamics found in prior work. These findings have implications for the design of isometric control systems in human-machine interaction or in rehabilitation when coordinated multi-degree-of-freedom movement is difficult to achieve.

Effective force control by muscle synergies

Muscle synergies have been proposed as a way for the central nervous system (CNS) to simplify the generation of motor commands and they have been shown to explain a large fraction of the variation in the muscle patterns across a variety of conditions. However, whether human subjects are able to control forces and movements effectively with a small set of synergies has not been tested directly. Here we show that muscle synergies can be used to generate target forces in multiple directions with the same accuracy achieved using individual muscles. We recorded electromyographic (EMG) activity from 13 arm muscles and isometric hand forces during a force reaching task in a virtual environment. From these data we estimated the force associated to each muscle by linear regression and we identified muscle synergies by non-negative matrix factorization. We compared trajectories of a virtual mass displaced by the force estimated using the entire set of recorded EMGs to trajectories obtained using 4–5 muscle synergies. While trajectories were similar, when feedback was provided according to force estimated from recorded EMGs (EMG-control) on average trajectories generated with the synergies were less accurate. However, when feedback was provided according to recorded force (force-control) we did not find significant differences in initial angle error and endpoint error. We then tested whether synergies could be used as effectively as individual muscles to control cursor movement in the force reaching task by providing feedback according to force estimated from the projection of the recorded EMGs into synergy space (synergy-control). Human subjects were able to perform the task immediately after switching from force-control to EMG-control and synergy-control and we found no differences between initial movement direction errors and endpoint errors in all control modes. These results indicate that muscle synergies provide an effective strategy for motor coordination.

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.