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

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

A quantitative taxonomy of human hand grasps

Background: A proper modeling of human grasping and of hand movements is fundamental for robotics, prosthetics, physiology and rehabilitation. The taxonomies of hand grasps that have been proposed in scientific literature so far are based on qualitative analyses of the movements and thus they are usually not quantitatively justified. Methods: This paper presents to the best of our knowledge the first quantitative taxonomy of hand grasps based on biomedical data measurements. The taxonomy is based on electromyography and kinematic data recorded from 40 healthy subjects performing 20 unique hand grasps. For each subject, a set of hierarchical trees are computed for several signal features. Afterwards, the trees are combined, first into modality-specific (i.e. muscular and kinematic) taxonomies of hand grasps and then into a general quantitative taxonomy of hand movements. The modality-specific taxonomies provide similar results despite describing different parameters of hand movements, one being muscular and the other kinematic. Results: The general taxonomy merges the kinematic and muscular description into a comprehensive hierarchical structure. The obtained results clarify what has been proposed in the literature so far and they partially confirm the qualitative parameters used to create previous taxonomies of hand grasps. According to the results, hand movements can be divided into five movement categories defined based on the overall grasp shape, finger positioning and muscular activation. Part of the results appears qualitatively in accordance with previous results describing kinematic hand grasping synergies. Conclusions: The taxonomy of hand grasps proposed in this paper clarifies with quantitative measurements what has been proposed in the field on a qualitative basis, thus having a potential impact on several scientific fields

Optimal Reconstruction of Human Motion From Scarce Multimodal Data

Wearable sensing has emerged as a promising solution for enabling unobtrusive and ergonomic measurements of the human motion. However, the reconstruction performance of these devices strongly depends on the quality and the number of sensors, which are typically limited by wearability and economic constraints. A promising approach to minimize the number of sensors is to exploit dimensionality reduction approaches that fuse prior information with insufficient sensing signals, through minimum variance estimation. These methods were successfully used for static hand pose reconstruction, but their translation to motion reconstruction has not been attempted yet. In this work, we propose the usage of functional principal component analysis to decompose multimodal, time-varying motion profiles in terms of linear combinations of basis functions. Functional decomposition enables the estimation of the a priori covariance matrix, and hence the fusion of scarce and noisy measured data with a priori information. We also consider the problem of identifying which elemental variables to measure as the most informative for a given class of tasks. We applied our method to two different datasets of upper limb motion D1 (joint trajectories) and D2 (joint trajectories + EMG data) considering an optimal set of measures (four joints for D1 out of seven, three joints, and eight EMGs for D2 out of seven and twelve, respectively). We found that our approach enables the reconstruction of upper limb motion with a median error of $0.013 \pm 0.006$ rad for D1 (relative median error 0.9%), and $0.038 \pm 0.023$ rad and $0.003 \pm 0.002$ mV for D2 (relative median error 2.9% and 5.1%, respectively)

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

Dynamic primitives of motor behavior

We present in outline a theory of sensorimotor control based on dynamic primitives, which we define as attractors. To account for the broad class of human interactive behaviors—especially tool use—we propose three distinct primitives: submovements, oscillations, and mechanical impedances, the latter necessary for interaction with objects. Owing to the fundamental features of the neuromuscular system—most notably, its slow response—we argue that encoding in terms of parameterized primitives may be an essential simplification required for learning, performance, and retention of complex skills. Primitives may simultaneously and sequentially be combined to produce observable forces and motions. This may be achieved by defining a virtual trajectory composed of submovements and/or oscillations interacting with impedances. Identifying primitives requires care: in principle, overlapping submovements would be sufficient to compose all observed movements but biological evidence shows that oscillations are a distinct primitive. Conversely, we suggest that kinematic synergies, frequently discussed as primitives of complex actions, may be an emergent consequence of neuromuscular impedance. To illustrate how these dynamic primitives may account for complex actions, we brieflyreviewthree typesof interactivebehaviors: constrained motion, impact tasks, and manipulation of dynamic objects.United States. National Institutes of Health (T32GM008334)American Heart Association (11SDG7270001)National Science Foundation (U.S.) (NSF DMS-0928587

Robust muscle synergies for postural control

The musculoskeletal structure of the human and animal body provides multiple solutions for performing any single motor behavior. The long-term goal of the work presented here is to determine the neuromechanical strategies used by the nervous system to appropriately coordinate muscles in order to achieve the performance of daily motor tasks. The overall hypothesis is that the nervous system simplifies muscle coordination by the flexible activation of muscle synergies, defined as a group of muscles activated as a unit, that perform task-level biomechanical functions. To test this hypothesis we investigated whether muscle synergies can be robustly used as building blocks for constructing the spatiotemporal muscle coordination patterns in human and feline postural control under a variety of biomechanical contexts. We demonstrated the generality and robustness of muscle synergies as a simplification strategy for both human and animal postural control. A few robust muscle synergies were able to reproduce the spatial and temporal variability in human and cat postural responses, regardless of stance configuration and perturbation type. In addition inter-trial variability in human postural responses was also accounted for by these muscle synergies. Finally, the activation of each muscle synergy in cat produced a specific stabilizing force vector, suggesting that muscle synergies control task-level variables. The identified muscle synergies may represent general modules of motor output underlying muscle coordination in posture that can be activated in different sensory contexts to achieve different postural goals. Therefore muscle synergies represents a simplifying mechanism for muscle coordination in natural behaviors not only because it is a strategy for reducing the number of variables to be controlled, but because it represents a mechanism for simply controlling multi-segmental task-level variables.Ph.D.Committee Chair: Ting, Lena H.; Committee Member: Chang, Young-Hui; Committee Member: Lee, Robert H.; Committee Member: Nichols, T. Richard; Committee Member: Wolf, Steve L

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