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

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

On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation

Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas

Muscle Synergies Facilitate Computational Prediction of Subject-Specific Walking Motions.

Researchers have explored a variety of neurorehabilitation approaches to restore normal walking function following a stroke. However, there is currently no objective means for prescribing and implementing treatments that are likely to maximize recovery of walking function for any particular patient. As a first step toward optimizing neurorehabilitation effectiveness, this study develops and evaluates a patient-specific synergy-controlled neuromusculoskeletal simulation framework that can predict walking motions for an individual post-stroke. The main question we addressed was whether driving a subject-specific neuromusculoskeletal model with muscle synergy controls (5 per leg) facilitates generation of accurate walking predictions compared to a model driven by muscle activation controls (35 per leg) or joint torque controls (5 per leg). To explore this question, we developed a subject-specific neuromusculoskeletal model of a single high-functioning hemiparetic subject using instrumented treadmill walking data collected at the subject's self-selected speed of 0.5 m/s. The model included subject-specific representations of lower-body kinematic structure, foot-ground contact behavior, electromyography-driven muscle force generation, and neural control limitations and remaining capabilities. Using direct collocation optimal control and the subject-specific model, we evaluated the ability of the three control approaches to predict the subject's walking kinematics and kinetics at two speeds (0.5 and 0.8 m/s) for which experimental data were available from the subject. We also evaluated whether synergy controls could predict a physically realistic gait period at one speed (1.1 m/s) for which no experimental data were available. All three control approaches predicted the subject's walking kinematics and kinetics (including ground reaction forces) well for the model calibration speed of 0.5 m/s. However, only activation and synergy controls could predict the subject's walking kinematics and kinetics well for the faster non-calibration speed of 0.8 m/s, with synergy controls predicting the new gait period the most accurately. When used to predict how the subject would walk at 1.1 m/s, synergy controls predicted a gait period close to that estimated from the linear relationship between gait speed and stride length. These findings suggest that our neuromusculoskeletal simulation framework may be able to bridge the gap between patient-specific muscle synergy information and resulting functional capabilities and limitations

The effects of robotic assistance on upper limb spatial muscle synergies in healthy people during planar upper-limb training

Background Robotic rehabilitation is a commonly adopted technique used to restore motor functionality of neurological patients. However, despite promising results were achieved, the effects of human-robot interaction on human motor control and the recovery mechanisms induced with robot assistance can be further investigated even on healthy subjects before translating to clinical practice. In this study, we adopt a standard paradigm for upper-limb rehabilitation (a planar device with assistive control) with linear and challenging curvilinear trajectories to investigate the effect of the assistance in human-robot interaction in healthy people. Methods Ten healthy subjects were instructed to perform a large set of radial and curvilinear movements in two interaction modes: 1) free movement (subjects hold the robot handle with no assistance) and 2) assisted movement (with a force tunnel assistance paradigm). Kinematics and EMGs from representative upper-limb muscles were recorded to extract phasic muscle synergies. The free and assisted interaction modes were compared assessing the level of assistance, error, and muscle synergy comparison between the two interaction modes. Results It was found that in free movement error magnitude is higher than with assistance, proving that task complexity required assistance also on healthy controls. Moreover, curvilinear tasks require more assistance than standard radial paths and error is higher. Interestingly, while assistance improved task performance, we found only a slight modification of phasic synergies when comparing assisted and free movement. Conclusions We found that on healthy people, the effect of assistance was significant on task performance, but limited on muscle synergies. The findings of this study can find applications for assessing human-robot interaction and to design training to maximize motor recovery

Neuromuscular Control Strategy during Object Transport while Walking: Adaptive Integration of Upper and Lower Limb Movements

When carrying an object while walking, a significant challenge for the central nervous system (CNS) is to preserve the object’s stability against the inter-segmental interaction torques and ground reaction forces. Studies documented several strategies used by the CNS: modulation of grip force (GF), alterations in upper limb kinematics, and gait adaptations. However, the question of how the CNS organizes the multi-segmental joint and muscle coordination patterns to deal with gait-induced perturbations remains poorly understood. This dissertation aimed to explore the neuromuscular control strategy utilized by the CNS to transport an object during walking successfully. Study 1 examined the inter-limb coordination patterns of the upper limbs when carrying a cylinder-shaped object while walking on a treadmill. It was predicted that transporting an object in one hand would affect the movement pattern of the contralateral arm to maintain the overall angular momentum. The results showed that transporting an object caused a decreased anti-phase coordination, but it did not induce significant kinematic and muscle activation changes in the unconstrained arm. Study 2 examined muscle synergy patterns for upper limb damping behavior by using non-negative matrix factorization (NNMF) method. Four synergies were identified, showing a proximal-to-distal pattern of activation preceding heel contacts. Study 3 examined the effect of different precision demands (carrying a cup with or without a ball) and altered visual information (looking forward vs. looking at an object) on the upper limb damping behavior and muscle synergies. Increasing precision demand induced stronger damping behavior and increased the electromyography (EMG) activation of wrist/hand flexors and extensors. The NNMF results replicated Study 2 in that the stabilization of proximal joints occurred before the distal joints. The results indicated that the damping incorporates tonic and phasic muscle activation to ensure object stabilization. Overall, three experiments showed that the CNS adopts a similar synergy pattern regardless of task constraint or altered gaze direction while modulating the amount of muscle activation for object stabilization. Kinematic changes can differ depending on the different levels of constraint, as shown in the smaller movement amplitude of the shoulder joint in the transverse plane during the task with higher precision demand

The effects of robotic assistance on upper limb spatial muscle synergies in healthy people during planar upper-limb training

BackgroundRobotic rehabilitation is a commonly adopted technique used to restore motor functionality of neurological patients. However, despite promising results were achieved, the effects of human-robot interaction on human motor control and the recovery mechanisms induced with robot assistance can be further investigated even on healthy subjects before translating to clinical practice. In this study, we adopt a standard paradigm for upper-limb rehabilitation (a planar device with assistive control) with linear and challenging curvilinear trajectories to investigate the effect of the assistance in human-robot interaction in healthy people.MethodsTen healthy subjects were instructed to perform a large set of radial and curvilinear movements in two interaction modes: 1) free movement (subjects hold the robot handle with no assistance) and 2) assisted movement (with a force tunnel assistance paradigm). Kinematics and EMGs from representative upper-limb muscles were recorded to extract phasic muscle synergies. The free and assisted interaction modes were compared assessing the level of assistance, error, and muscle synergy comparison between the two interaction modes.ResultsIt was found that in free movement error magnitude is higher than with assistance, proving that task complexity required assistance also on healthy controls. Moreover, curvilinear tasks require more assistance than standard radial paths and error is higher. Interestingly, while assistance improved task performance, we found only a slight modification of phasic synergies when comparing assisted and free movement.ConclusionsWe found that on healthy people, the effect of assistance was significant on task performance, but limited on muscle synergies. The findings of this study can find applications for assessing human-robot interaction and to design training to maximize motor recovery

Upper Limb Sensory-Motor Control During Exposure to Different Mechanical Environments in Multiple Sclerosis Subjects With No Clinical Disability

Multiple sclerosis (MS) is an autoimmune and neurodegenerative disease resulting in motor impairments associated with muscle weakness and lack of movement coordination. The goal of this work was to quantify upper limb motor deficits in asymptomatic MS subjects with a robot-based assessment including performance and muscle synergies analysis. A total of 7 subjects (MS: 3 M-4 F; 42 +/- 10 years) with clinically definite MS according to McDonald criteria, but with no clinical disability, and 7 age- and sex-matched subjects without a history of neurological disorders participated in the study. All subjects controlled a cursor on the computer screen by moving their hand or applying forces in 8 coplanar directions at their self-selected speed. They grasped the handle of a robotic planar manipulandum that generated four different environments: null, assistive or resistive forces, and rigid constraint. Simultaneously, the activity of 15 upper body muscles was recorded. Asymptomatic MS subjects generated less smooth and less accurate cursor trajectories than control subjects in controlling a force profile, while the end-point error was significantly different also in the other environments. The EMG analysis revealed different muscle activation patterns in MS subjects when exerting isometric forces or when moving in presence of external forces generated by a robot. While the two populations had the same number and similar structure of muscle synergies, they had different activation profiles. These results suggested that a task requiring to control forces against a rigid environment allows better than movement tasks to detect early sensory-motor signs related to the onset of symptoms of multiple sclerosis and to differentiate between stages of the disease

Biceps brachii synergy and its contribution to target reaching tasks within a virtual cube

Ces dernières années, des travaux importants ont été observés dans le développement du contrôle prothétique afin d'aider les personnes amputées du membre supérieur à améliorer leur qualité de vie au quotidien. Certaines prothèses myoélectriques modernes des membres supérieurs disponibles dans le commerce ont de nombreux degrés de liberté et nécessitent de nombreux signaux de contrôle pour réaliser plusieurs tâches fréquemment utilisées dans la vie quotidienne. Pour obtenir plusieurs signaux de contrôle, de nombreux muscles sont requis mais pour les personnes ayant subi une amputation du membre supérieur, le nombre de muscles disponibles est plus ou moins réduit selon le niveau de l’amputation. Pour accroître le nombre de signaux de contrôle, nous nous sommes intéressés au biceps brachial, vu qu’anatomiquement il est formé de 2 chefs et que de la présence de compartiments a été observée sur sa face interne. Physiologiquement, il a été trouvé que les unités motrices du biceps sont activées à différents endroits du muscle lors de la production de diverses tâches fonctionnelles. De plus, il semblerait que le système nerveux central puisse se servir de la synergie musculaire pour arriver à facilement produire plusieurs mouvements. Dans un premier temps on a donc identifié que la synergie musculaire était présente chez le biceps de sujets normaux et on a montré que les caractéristiques de cette synergie permettaient d’identifier la posture statique de la main lorsque les signaux du biceps avaient été enregistrés. Dans un deuxième temps, on a réussi à démontrer qu’il était possible, dans un cube présenté sur écran, à contrôler la position d’une sphère en vue d’atteindre diverses cibles en utilisant la synergie musculaire du biceps. Les techniques de classification utilisées pourraient servir à faciliter le contrôle des prothèses myoélectriques.In recent years, important work has been done in the development of prosthetic control to help upper limb amputees improve their quality of life on a daily basis. Some modern commercially available upper limb myoelectric prostheses have many degrees of freedom and require many control signals to perform several tasks commonly used in everyday life. To obtain several control signals, many muscles are required, but for people with upper limb amputation, the number of muscles available is more or less reduced, depending on the level of amputation. To increase the number of control signals, we were interested in the biceps brachii, since it is anatomically composed of 2 heads and the presence of compartments was observed on its internal face. Physiologically, it has been found that the motor units of the biceps are activated at different places of the muscle during production of various functional tasks. In addition, it appears that the central nervous system can use muscle synergy to easily produce multiple movements. In this research, muscle synergy was first identified to be present in the biceps of normal subjects, and it was shown that the characteristics of this synergy allowed the identification of static posture of the hand when the biceps signals had been recorded. In a second investigation, we demonstrated that it was possible in a virtual cube presented on a screen to control online the position of a sphere to reach various targets by using muscle synergy of the biceps. Classification techniques have been used to improve the classification of muscular synergy features, and these classification techniques can be integrated with control algorithm that produces dynamic movement of myoelectric prostheses to facilitate the training of prosthetic control

On the Time-Invariance Properties of Upper Limb Synergies

In this paper, we present a novel approach to dynamically describe human upper limb trajectories, addressing the question on whether and to which extent synergistic multi-joint behavior is observed and preserved over time evolution and across subjects. To this goal, we performed experiments to collect human upper limb joint angle trajectories and organized them in a dataset of daily living tasks. We then characterized the upper limb poses at each time frame through a technique that we named repeated-principal component analysis (R-PCA). We found that, although there is no strong evidence on the predominance of one principal component (PC) over the others, the subspace identified by the first three PCs takes into account most of the motion variability. We evaluated the stability of these results over time, showing that during the reaching phase, there is a strong consistency of these findings across participants. In other words, our results suggest that there is a time-invariant low-dimensional approximation of upper limb kinematics, which can be used to define a suitable reduced dimensionality control space for upper limb robotic devices in motion phases

The Investigation of Motor Primitives During Human Reaching Movements and the Quantification of Post-Stroke Motor Impairment

Movement is a complex task, requiring precise and coordinated muscle contractions. The forces and torques produced during multi-segmental movement of the upper limbs in humans, must be controlled, in order for movement to be achieved successfully. Although a critical aspect of everyday life, there remain questions regarding the specific controller used by the central nervous system to govern movement. Furthermore, how this system is affected by neurological injuries such as stroke also remains in question. It was the goal of this thesis to examine the neurological control of movement in healthy individuals and apply these findings to the further investigation of chronically motor impaired stroke patients. Additionally, this work aimed at providing clinicians with a more reliable, easy to use, and inexpensive approach to quantify post-stroke motor impairment