Visual feedback improves bimanual force control performances at planning and execution levels

The purpose of this study was to determine the effect of different visual conditions and targeted force levels on bilateral motor synergies and bimanual force control performances. Fourteen healthy young participants performed bimanual isometric force control tasks by extending their wrists and fingers under two visual feedback conditions (i.e., vision and no-vision) and three targeted force levels (i.e., 5%, 25%, and 50% of maximum voluntary contraction: MVC). To estimate bilateral motor synergies across multiple trials, we calculated the proportion of good variability relative to bad variability using an uncontrolled manifold analysis. To assess bimanual force control performances within a trial, we used the accuracy, variability, and regularity of total forces produced by two hands. Further, analysis included correlation coefficients between forces from the left and right hands. In addition, we examined the correlations between altered bilateral motor synergies and force control performances from no-vision to vision conditions for each targeted force level. Importantly, our findings revealed that the presence of visual feedback increased bilateral motor synergies across multiple trials significantly with a reduction of bad variability as well as improved bimanual force control performances within a trial based on higher force accuracy, lower force variability, less force regularity, and decreased correlation coefficients between hands. Further, we found two significant correlations in (a) increased bilateral motor synergy versus higher force accuracy at 5% of MVC and (b) increased bilateral motor synergy versus lower force variability at 50% of MVC. Together, these results suggested that visual feedback effectively improved both synergetic coordination behaviors across multiple trials and stability of task performance within a trial across various submaximal force levels.

Motor Synergies Measurement Reveals the Relevant Role of Variability in Reward-Based Learning

Currently, it is not fully understood how motor variability is regulated to ease of motor learning processes during reward-based tasks. This study aimed to assess the potential relationship between different dimensions of motor variability (i.e., the motor variability structure and the motor synergies variability) and the learning rate in a reward-based task developed using a two-axis force sensor in a computer environment. Forty-four participants performed a pretest, a training period, a posttest, and three retests. They had to release a virtual ball to hit a target using a vertical handle attached to a dynamometer in a computer-simulated reward-based task. The participants’ throwing performance, learning ratio, force applied, variability structure (detrended fluctuation analysis, DFA), and motor synergy variability (good and bad variability ratio, GV/BV) were calculated. Participants with higher initial GV/BV displayed greater performance improvements than those with lower GV/BV. DFA did not show any relationship with the learning ratio. These results suggest that exploring a broader range of successful motor synergy combinations to achieve the task goal can facilitate further learning during reward-based tasks. The evolution of the motor variability synergies as an index of the individuals’ learning stages seems to be supported by our study.

Emerging of new bioartificial corticospinal motor synergies using a robotic additional thumb

It is likely that when using an artificially augmented hand with six fingers, the natural five plus a robotic one, corticospinal motor synergies controlling grasping actions might be different. However, no direct neurophysiological evidence for this reasonable assumption is available yet. We used transcranial magnetic stimulation of the primary motor cortex to directly address this issue during motor imagery of objects’ grasping actions performed with or without the Soft Sixth Finger (SSF). The SSF is a wearable robotic additional thumb patented for helping patients with hand paresis and inherent loss of thumb opposition abilities. To this aim, we capitalized from the solid notion that neural circuits and mechanisms underlying motor imagery overlap those of physiological voluntary actions. After a few minutes of training, healthy humans wearing the SSF rapidly reshaped the pattern of corticospinal outputs towards forearm and hand muscles governing imagined grasping actions of different objects, suggesting the possibility that the extra finger might rapidly be encoded into the user’s body schema, which is integral part of the frontal-parietal grasping network. Such neural signatures might explain how the motor system of human beings is open to very quickly welcoming emerging augmentative bioartificial corticospinal grasping strategies. Such an ability might represent the functional substrate of a final common pathway the brain might count on towards new interactions with the surrounding objects within the peripersonal space. Findings provide a neurophysiological framework for implementing augmentative robotic tools in humans and for the exploitation of the SSF in conceptually new rehabilitation settings.

Visual Feedback Improves Bimanual Force Control Performances at Planning and Execution Levels

The purpose of this study was to determine whether altered interlimb coordination patterns across trials improved bimanual force control capabilities within a trial. Fourteen healthy young participants completed bimanual force control tasks at 5%, 25%, and 50% of maximum voluntary contraction with and without visual feedback. To estimate synergetic coordination patterns between hands across multiple trials, we analyzed our primary outcome measure by performing an uncontrolled manifold analysis. In addition, we calculated force accuracy, variability, and regularity within a trial to quantify task stabilization. Using Pearson’s correlation analyses, we determined the relation between the changes in bilateral motor synergies (i.e., a proportion of good variability relative to bad variability) and bimanual force control performance from no-vision to vision conditions. The findings revealed that the presence of visual feedback significantly increased bilateral motor synergies with a reduction of bad variability components across multiple trials, and decreased force error, variability, and regularity within a trial. Further, we observed significant positive correlations between higher bilateral motor synergies and increased improvements in force control capabilities. These findings suggested that bimanual synergetic coordination behaviors at the planning level modulated by external sensory feedback may be related to advanced task stabilization patterns at the execution level.

Data-Driven Personalization of Body-Machine Interfaces to Control Diverse Robot Types

Body-Machine Interfaces (BoMIs) for robotic teleoperation can improve a user’s experience and performance. However, the implementation of such systems needs to be optimized on each robot independently, as a general approach has not been proposed to date. Here, we present a novel machine learning method to generate personalized BoMIs from an operator’s spontaneous body movements. The method captures individual motor synergies that can be used for the teleoperation of robots. The proposed algorithm applies to people with diverse behavioral patterns to control robots with diverse morphologies and degrees of freedom, such as a fixed-wing drone, a quadrotor, and a robotic manipulator.

Number of Trials Necessary to Apply Analysis within the Framework of the Uncontrolled Manifold Hypothesis at Different Levels of Hierarchical Synergy Control

The uncontrolled manifold hypothesis is a method used to quantify motor synergies, defined as a specific central nervous system organization that maintains the task-specific stability of motor actions. The UCM allows for inter-trial variance analysis between consecutive trials. However, despite the large body of literature within this framework, there is no report on the number of movement repetitions required for reliable results. Based on the hypothetical hierarchical control of motor synergies, this study aims to determine the minimum number of trials necessary to achieve a good to excellent level of reliability. Thirteen young, healthy participants performed fifteen bilateral isometric contractions of elbow flexion when visual feedback was provided. The force and electromyography data were recorded to investigate synergies at different levels of hierarchical control. The intraclass correlation coefficient was used to determine the reliability of the variance indices. Based on the obtained results, at least twelve trials are required to analyze the inter-trial variance in both force and muscle synergies within the UCM framework.

Unveiling the neuromechanical mechanisms underlying the synergistic interactions in human sensorimotor system

Motor synergies are neural organizations of a set of redundant motor effectors that interact with one another to compensate for each other’s error and ensure the stabilization of a performance variable. Recent studies have demonstrated that central nervous system synergistically coordinates its numerous motor effectors through Bayesian multi-sensory integration. Deficiency in sensory synergy weakens the synergistic interaction between the motor effectors. Here, we scrutinize the neuromechanical mechanism underlying this phenomenon through spectral analysis and modeling. We validate our model-generated results using experimental data reported in the literature collected from participants performing a finger force production task with and without tactile feedback (manipulated through injection of anesthetic in fingers). Spectral analysis reveals that the error compensation feature of synergies occurs only at low frequencies. Modeling suggests that the neurophysiological structures involving short-latency back-coupling loops similar to the well-known Renshaw cells explain the deterioration of synergy due to sensory deprivation.

Shoulder kinematics plus contextual target information enable control of multiple distal joints of a simulated prosthetic arm and hand

Background Prosthetic restoration of reach and grasp function after a trans-humeral amputation requires control of multiple distal degrees of freedom in elbow, wrist and fingers. However, such a high level of amputation reduces the amount of available myoelectric and kinematic information from the residual limb. Methods To overcome these limits, we added contextual information about the target’s location and orientation such as can now be extracted from gaze tracking by computer vision tools. For the task of picking and placing a bottle in various positions and orientations in a 3D virtual scene, we trained artificial neural networks to predict postures of an intact subject’s elbow, forearm and wrist (4 degrees of freedom) either solely from shoulder kinematics or with additional knowledge of the movement goal. Subjects then performed the same tasks in the virtual scene with distal joints predicted from the context-aware network. Results Average movement times of 1.22s were only slightly longer than the naturally controlled movements (0.82 s). When using a kinematic-only network, movement times were much longer (2.31s) and compensatory movements from trunk and shoulder were much larger. Integrating contextual information also gave rise to motor synergies closer to natural joint coordination. Conclusions Although notable challenges remain before applying the proposed control scheme to a real-world prosthesis, our study shows that adding contextual information to command signals greatly improves prediction of distal joint angles for prosthetic control.