Muscle coordination is habitual rather than optimal

When sharing load among multiple muscles, humans appear to select an optimal pattern of activation that minimizes costs such as the effort or variability of movement. How the nervous system achieves this behavior, however, is unknown. Here we show that contrary to predictions from optimal control theory, habitual muscle activation patterns are surprisingly robust to changes in limb biomechanics. We first developed a method to simulate joint forces in real time from electromyographic recordings of the wrist muscles. When the model was altered to simulate the effects of paralyzing a muscle, the subjects simply increased the recruitment of all muscles to accomplish the task, rather than recruiting only the useful muscles. When the model was altered to make the force output of one muscle unusually noisy, the subjects again persisted in recruiting all muscles rather than eliminating the noisy one. Such habitual coordination patterns were also unaffected by real modifications of biomechanics produced by selectively damaging a muscle without affecting sensory feedback. Subjects naturally use different patterns of muscle contraction to produce the same forces in different pronation-supination postures, but when the simulation was based on a posture different from the actual posture, the recruitment patterns tended to agree with the actual rather than the simulated posture. The results appear inconsistent with computation of motor programs by an optimal controller in the brain. Rather, the brain may learn and recall command programs that result in muscle coordination patterns generated by lower sensorimotor circuitry that are functionally "good-enough.

Biological Plausibility of Arm Postures Influences the Controllability of Robotic Arm Teleoperation

International audienceObjective: We investigated how participants controlling a humanoid robotic arm's 3D endpoint position by moving their own hand are influenced by the robot's postures. We hypothesized that control would be facilitated (impeded) by biologically plausible (implausible) postures of the robot. Background: Kinematic redundancy, whereby different arm postures achieve the same goal, is such that a robotic arm or prosthesis could theoretically be controlled with less signals than constitutive joints. However, congruency between a robot's motion and our own is known to interfere with movement production. Hence, we expect the human-likeness of a robotic arm's postures during endpoint teleoperation to influence controllability. Method: Twenty-two able-bodied participants performed a target-reaching task with a robotic arm whose endpoint's 3D position was controlled by moving their own hand. They completed a two-condition experiment corresponding to the robot displaying either biologically plausible or implausible postures. Results: Upon initial practice in the experiment's first part, endpoint trajectories were faster and shorter when the robot displayed human-like postures. However, these effects did not persist in the second part, where performance with implausible postures appeared to have benefited from initial practice with plausible ones. Conclusion: Humanoid robotic arm endpoint control is impaired by biologically implausible joint coordinations during initial familiarization but not afterwards, suggesting that the human-likeness of a robot's postures is more critical for control in this initial period. Application: These findings provide insight for the design of robotic arm teleoperation and prosthesis control schemes, in order to favor better familiarization and control from their users

Model and experiments to optimize co-adaptation in a simplified myoelectric control system

Objective. To compensate for a limb lost in an amputation, myoelectric prostheses use surface electromyography (EMG) from the remaining muscles to control the prosthesis. Despite considerable progress, myoelectric controls remain markedly different from the way we normally control movements, and require intense user adaptation. To overcome this, our goal is to explore concurrent machine co-adaptation techniques that are developed in the field of brain-machine interface, and that are beginning to be used in myoelectric controls. Approach. We combined a simplified myoelectric control with a perturbation for which human adaptation is well characterized and modeled, in order to explore co-adaptation settings in a principled manner. Results. First, we reproduced results obtained in a classical visuomotor rotation paradigm in our simplified myoelectric context, where we rotate the muscle pulling vectors used to reconstruct wrist force from EMG. Then, a model of human adaptation in response to directional error was used to simulate various co-adaptation settings, where perturbations and machine co-adaptation are both applied on muscle pulling vectors. These simulations established that a relatively low gain of machine co-adaptation that minimizes final errors generates slow and incomplete adaptation, while higher gains increase adaptation rate but also errors by amplifying noise. After experimental verification on real subjects, we tested a variable gain that cumulates the advantages of both, and implemented it with directionally tuned neurons similar to those used to model human adaptation. This enables machine co-adaptation to locally improve myoelectric control, and to absorb more challenging perturbations. Significance. The simplified context used here enabled to explore co-adaptation settings in both simulations and experiments, and to raise important considerations such as the need for a variable gain encoded locally. The benefits and limits of extending this approach to more complex and functional myoelectric contexts are discussed

Generalization of visuomotor adaptation to different muscles is less efficient: Experiment and model

Reaching to visual targets engages the nervous system in a series of transformations between sensory information and motor commands to muscles. We recently showed that visuomotor adaptation requiring modulation of the activity of the same muscles is more efficient than adaptation requiring a transition to different muscles. Here I specifically tested for adaptation at the level of the final transformation into muscle activation by assessing generalization to unpracticed areas of the workspace, and propose a computational model with modulation of muscle synergies. In the experiment, a visuomotor rotation was applied during a center-out isometric torque production task carefully configured such that adaptation and generalization could be achieved either by only rescaling the contribution of the same muscles, or by additionally requiring the recruitment of different muscles. Consistent with our previous finding, the time course of directional errors revealed that the degree of adaptation was substantially lower (by 28.1%) for the latter case. More importantly, directional error obtained for generalization that required, in principle, to recruit different muscles from these implicated in the adaptation was more than twice that of other generalization areas. Taken together, these results suggest that modulation within an original muscle synergy contributed to visuomotor adaptation, and that synergy recomposition imposed a limitation on both adaptation and generalization. I reproduced these results with a model of the sensorimotor transformation which includes two population codes, one for the sensory network and one for the motor network. Muscle synergies are defined as linear combination of muscles by connections of the motor network, and modulation of these synergies are elicited by adaptation of the weight of these connections. Finally, I speculate that the limitation imposed on synergy recomposition originates in the balance of inhibitory and excitatory mechanisms that operate at different levels of the nervous system, and that contribute to the functional organization of muscle recruitment by focusing activity on relevant muscles. © 2010 Elsevier B.V

Interaction between discrete and rhythmic movements: reaction time and phase of discrete movement initiation during oscillatory movements

This study investigates a task in which discrete and rhythmic movements are combined in a single-joint elbow rotation. Previous studies reported a tendency for the EMG burst associated with the discrete movement to occur around the expected burst associated with the rhythmic movement (e.g., [Exp. Brain Res. 99 (1994) 325; J. Neurol. Neurosurg. Psychiatry 40 (1977) 1129; Hum. Mov. Sci. 19 (2000) 627]). We document this interaction between discrete and rhythmic movements in different task variations and suggest a model consisting of rhythmic and discrete pattern generators that reproduces the major results. In the experiment, subjects performed single-joint elbow oscillatory movements (2 Hz). Upon a signal, they initiated a movement that consisted of a shift in the midpoint of the oscillation (MID), a shift in the amplitude of the oscillation (AMP), or a combination of both (MID + AMP). These shifting movements were performed either in a reaction time or in a self-paced fashion. The tendency for the EMG bursts associated with the discrete and rhythmic movements to synchronize was found similarly in all three tasks and instruction conditions, but the synchronization was most pronounced in the self-initiated discrete movement. Reaction time was increased for the combined task (MID+ AMP), indicating higher control demands due to a combination of discrete and rhythmic components. This EMG burst synchronization was reproduced in a model based on a half-center oscillator with activation signals that produce either rhythmic or discrete activity. This activity was interpreted as torques driving a simple limb model. Summation of discrete and rhythmic activation signals of the pattern generators was sufficient to simulate the EMG burst synchronization. Further, simulation data reproduced the modulation of the reaction time as a function of the phase of the discrete movement. (C) 2003 Elsevier B.V. All rights reserved

Performance and Usability of Various Robotic Arm Control Modes from Human Force Signals

Elaborating an efficient and usable mapping between input commands and output movements is still a key challenge for the design of robotic arm prostheses. In order to address this issue, we present and compare three different control modes, by assessing them in terms of performance as well as general usability. Using an isometric force transducer as the command device, these modes convert the force input signal into either a position or a velocity vector, whose magnitude is linearly or quadratically related to force input magnitude. With the robotic arm from the open source 3D-printed Poppy Humanoid platform simulating a mobile prosthesis, an experiment was carried out with eighteen able-bodied subjects performing a 3-D target-reaching task using each of the three modes. The subjects were given questionnaires to evaluate the quality of their experience with each mode, providing an assessment of their global usability in the context of the task. According to performance metrics and questionnaire results, velocity control modes were found to perform better than position control mode in terms of accuracy and quality of control as well as user satisfaction and comfort. Subjects also seemed to favor quadratic velocity control over linear (proportional) velocity control, even if these two modes did not clearly distinguish from one another when it comes to performance and usability assessment. These results highlight the need to take into account user experience as one of the key criteria for the design of control modes intended to operate limb prostheses

Couplage perception-action au cours du pointage locomoteur

AIX-MARSEILLE2-BU Sci.Luminy (130552106) / SudocSudocFranceF

Changes in muscle directional tuning parallel feedforward adaptation to a visuomotor rotation

When people learn to reach in a novel sensorimotor environment, there are changes in the muscle activity required to achieve task goals. Here, we assessed the time course of changes in muscle directional tuning during acquisition of a new mapping between visual information and isometric force production in the absence of feedback-based error corrections. We also measured the influence of visuomotor adaptation on corticospinal excitability, to test whether any changes in muscle directional tuning are associated with adaptations in the final output components of the sensorimotor control system. Nine right-handed subjects performed a ballistic, center-out isometric target acquisition task with the right wrist (16 targets spaced every 22.5 degrees in the joint space). Surface electromyography was recorded from four major wrist muscles, and motor evoked potentials induced by transcranial magnetic stimulation were measured at baseline, after task execution in the absence of the rotation (A1), after adaptation to the rotation (B), and after a final block of trials without rotation (A2). Changes in the directional tuning of muscles closely matched the rotation of the directional error in force, indicating that the functional contribution of muscles remained consistent over the adaptation period. In contrast to previous motor learning studies, we found only minor changes in the amount of muscular activity and no increase in corticospinal excitability. These results suggest that increased muscle co-activation occurs only when the dynamics of the limb are perturbed and/or that online error corrections or altered force requirements are necessary to elicit a component of the adaptation in the final steps of the transformation between motor goal and muscle activation