Evidence for muscle synergies from virtual surgeries

A fundamental challenge in neuroscience is understanding how the central nervous system (CNS) succeeds in controlling motor skills that require the coordination of many degrees-of-freedom. A long-standing hypothesis is that the CNS relies on muscle synergies, coordinated activations of groups of muscles, to simplify motor control. Evidence that the combinations of a small number of muscle synergies underlies the generation of muscle activation patterns has come from several studies performed in the last two decades with different species and experimental tasks. Muscle synergies, extracted from multi-muscle EMG recordings using multidimensional decomposition algorithms such as non-negative matrix factorization, capture regularities in the spatial, temporal, and spatiotemporal organization of the muscle patterns. However, whether muscle synergies are only a parsimonious description of the regularities of the motor commands rather than a key feature of their neural organization is still debated. Stronger evidence for a neural organization of muscle synergies would come from testing a prediction of how muscle synergies affect the difficulty in learning or adapting motor skills. An experiment with human subjects using myoelectric control to move a mass in a virtual environment has tested the prediction that it must be harder to adapt to perturbations that require new or modified synergies than to adapt to perturbations that can be compensated by recombining existing synergies. Novel perturbations were generated by altering the mapping between recorded EMG and simulated force applied on the mass, as in a complex surgical rearrangement of the tendons. After identifying muscle synergies, two types of virtual surgeries were performed. After compatible virtual surgeries, a full range of movements could still be achieved recombining the synergies, whereas after incompatible virtual surgeries new or modified synergies were required. In contrast, both types of surgeries could be compensated with similar changes in the recruitment of individual muscles. As predicted, adaptation after compatible surgeries was faster than after incompatible ones. These results suggest that muscle synergies are key elements organized by the CNS for controlling our complex musculoskeletal system by directly mapping task goals into a small number of synergy combination parameters

Welding Defect Detection with Deep Learning Architectures

Welding automation is a fundamental process in manufacturing industries. Production lines integrate welding quality controls to reduce wastes and optimize the production chain. Early detection is fundamental as defects at any stage could determine the rejection of the entire product. In the last years, following the industry 4.0 paradigm, industrial automation lines have seen the introduction of modern technologies. Although the majority of the inspection systems still rely on traditional sensing and data processing, especially in the computer vision domain, some initiatives have been taken toward the employment of machine learning architectures. This chapter introduces deep neural networks in the context of welding defect detection, starting by analyzing common problems in the industrial applications of such technologies and discussing possible solutions in the specific case of quality checks in fuel injectors welding during the production stage

In silico case studies of compliant robots: AMARSI deliverable 3.3

In the deliverable 3.2 we presented how the morphological computing ap- proach can significantly facilitate the control strategy in several scenarios, e.g. quadruped locomotion, bipedal locomotion and reaching. In particular, the Kitty experimental platform is an example of the use of morphological computation to allow quadruped locomotion. In this deliverable we continue with the simulation studies on the application of the different morphological computation strategies to control a robotic system

Functional synergies applied to a publicly available dataset of hand grasps show evidence of kinematic-muscular synergistic control

Hand grasp patterns are the results of complex kinematic-muscular coordination and synergistic control might help reducing the dimensionality of the motor control space at the hand level. Kinematic-muscular synergies combining muscle and kinematic hand grasp data have not been investigated before. This paper provides a novel analysis of kinematic-muscular synergies from kinematic and EMG data of 28 subjects, performing 20 hand grasps. Kinematic-muscular synergies were extracted from combined kinematic and muscle data with the recently introduced Mixed Matrix Factorization (MMF) algorithm. Seven synergies were first extracted from each subject, accounting on average for >75 % of the data variation. Then, cluster analysis was used to group synergies across subjects, with the aim of summarizing the coordination patterns available for hand grasps, and investigating relevant aspects of synergies such as inter-individual variability. Twenty-one clusters were needed to group the entire set of synergies extracted from 28 subjects, revealing high inter-individual variability. The number of kinematic-muscular motor modules required to perform the motor tasks is a reduced subset of the degrees of freedom to be coordinated; however, probably due to the variety of tasks, poor constraints and the large number of variables considered, we noted poor inter-individual repeatability. The results generalize the description of muscle and hand kinematics, better clarifying several limits of the field and fostering the development of applications in rehabilitation and assistive robotics

A unifying framework for the identification of motor primitives

Chiovetto E, d’Avella A, Giese MA. A unifying framework for the identification of motor primitives. Plos One. Submitted

A Synergistic Behavior Underpins Human Hand Grasping Force Control During Environmental Constraint Exploitation

Despite the complex nature of human hands, neuroscientific studies suggested a simplified kinematic control underpinning motion generation, resulting in principal joint angle co-variation patterns, usually called postural hand synergies. Such a low dimensional description was observed in common grasping tasks, and was proven to be preserved also for grasps performed by exploiting the external environment (e.g., picking up a key by sliding it on a table). In this paper, we extend this analysis to the force domain. To do so, we performed experiments with six subjects, who were asked to grasp objects from a flat surface while force/torque measures were acquired at fingertip level through wearable sensors. The set of objects was chosen so that participants were forced to interact with the table to achieve a successful grasp. Principal component analysis was applied to force measurements to investigate the existence of co-variation schemes, i.e. a synergistic behavior. Results show that one principal component explains most of the hand force distribution. Applications to clinical assessment and robotic sensing are finally discussed

Evaluation of Matrix Factorisation Approaches for Muscle Synergy Extraction

The muscle synergy concept provides a widely-accepted paradigm to break down the complexity of motor control. In order to identify the synergies, different matrix factorisation techniques have been used in a repertoire of fields such as prosthesis control and biomechanical and clinical studies. However, the relevance of these matrix factorisation techniques is still open for discussion since there is no ground truth for the underlying synergies. Here, we evaluate factorisation techniques and investigate the factors that affect the quality of estimated synergies. We compared commonly used matrix factorisation methods: Principal component analysis (PCA), Independent component analysis (ICA), Non-negative matrix factorization (NMF) and second-order blind identification (SOBI). Publicly available real data were used to assess the synergies extracted by each factorisation method in the classification of wrist movements. Synthetic datasets were utilised to explore the effect of muscle synergy sparsity, level of noise and number of channels on the extracted synergies. Results suggest that the sparse synergy model and a higher number of channels would result in better-estimated synergies. Without dimensionality reduction, SOBI showed better results than other factorisation methods. This suggests that SOBI would be an alternative when a limited number of electrodes is available but its performance was still poor in that case. Otherwise, NMF had the best performance when the number of channels was higher than the number of synergies. Therefore, NMF would be the best method for muscle synergy extraction.Comment: Keywords: Muscle synergy; Matrix factorisation; Surface electromyogram; Non-negative matrix factorisation; Second-order blind identification; Principal component analysis; Independent component analysi