Optimal Impedance Control for Task Achievement in the Presence of Signal-Dependent Noise

Role of the Cerebellum in the Construction of Functional and Geometrical Spaces

The perceptual and motor systems appear to have a set of movement primitives that exhibit certain geometric and kinematic invariances. Complex patterns and mental representations can be produced by (re)combining some simple motor elements in various ways using basic operations, transformations, and respecting a set of laws referred to as kinematic laws of motion. For example, point-to-point hand movements are characterized by straight hand paths with single-peaked-bell-shaped velocity profiles, whereas hand speed profiles for curved trajectories are often irregular and more variable, with speed valleys and inflections extrema occurring at the peak curvature. Curvature and speed are generically related by the 2/3 power law. Mathematically, such laws can be deduced from a combination of Euclidean, affine, and equi-affine geometries, whose neural correlates have been partially detected in various brain areas including the cerebellum and the basal ganglia. The cerebellum has been found to play an important role in the control of coordination, balance, posture, and timing over the past years. It is also assumed that the cerebellum computes forward internal models in relationship with specific cortical and subcortical brain regions but its motor relationship with the perceptual space is unclear. A renewed interest in the geometrical and spatial role of the cerebellum may enable a better understanding of its specific contribution to the action-perception loop and behavior's adaptation. In this sense, we complete this overview with an innovative theoretical framework that describes a possible implementation and selection by the cerebellum of geometries adhering to different mathematical laws.

Behavioral Motor Performance

The human sensorimotor control system has exceptional abilities to perform skillful actions. We easily switch between strenuous tasks that involve brute force, such as lifting a heavy sewing machine, and delicate movements such as threading a needle in the same machine. Using a structure with different control architectures, the motor system is capable of updating its ability to perform through our daily interaction with the fluctuating environment. However, there are issues that make this a difficult computational problem for the brain to solve. The brain needs to control a nonlinear, nonstationary neuromuscular system, with redundant and occasionally undesired degrees of freedom, in an uncertain environment using a body in which information transmission is subject to delays and noise. To gain insight into the mechanisms of motor control, here we survey movement laws and invariances that shape our everyday motion. We then examine the major solutions to each of these problems in the three parts of the sensorimotor control system, sensing, planning, and acting. We focus on how the sensory system, the control architectures, and the structure and operation of the muscles serve as complementary mechanisms to overcome deviations and disturbances to motor behavior and give rise to skillful motor performance. We conclude with possible future research directions based on suggested links between the operation of the sensorimotor system across the movement stages. © 2024 American Physiological Society. Compr Physiol 14:5179-5224, 2024.

From neural noise to co-adaptability: Rethinking the multifaceted architecture of motor variability

In the last decade, the source and the functional meaning of motor variability have attracted considerable attention in behavioral and brain sciences. This construct classically combined different levels of description, variable internal robustness or coherence, and multifaceted operational meanings. We provide here a comprehensive review of the literature with the primary aim of building a precise lexicon that goes beyond the generic and monolithic use of motor variability. In the pars destruens of the work, we model three domains of motor variability related to peculiar computational elements that influence fluctuations in motor outputs. Each domain is in turn characterized by multiple sub-domains. We begin with the domains of noise and differentiation. However, the main contribution of our model concerns the domain of adaptability, which refers to variation within the same exact motor representation. In particular, we use the terms learning and (social)fitting to specify the portions of motor variability that depend on our propensity to learn and on our largely constitutive propensity to be influenced by external factors. A particular focus is on motor variability in the context of the sub-domain named co-adaptability. Further groundbreaking challenges arise in the modeling of motor variability. Therefore, in a separate pars construens, we attempt to characterize these challenges, addressing both theoretical and experimental aspects as well as potential clinical implications for neurorehabilitation. All in all, our work suggests that motor variability is neither simply detrimental nor beneficial, and that studying its fluctuations can provide meaningful insights for future research.

Muscle synergies in joystick manipulation

Extracting muscle synergies from surface electromyographic signals (sEMGs) during exercises has been widely applied to evaluate motor control strategies. This study explores the relationship between upper-limb muscle synergies and the performance of joystick manipulation tasks. Seventy-seven subjects, divided into three classes according to their maneuvering experience, were recruited to perform the left and right reciprocation of the joystick. Based on the motion encoder data, their manipulation performance was evaluated by the mean error, standard deviation, and extreme range of position of the joystick. Meanwhile, sEMG and acceleration signals from the upper limbs corresponding to the entire trial were collected. Muscle synergies were extracted from each subject's sEMG data by non-negative matrix factorization (NMF), based on which the synergy coordination index (SCI), which indicates the size of the synergy space and the variability of the center of activity (CoA), evaluated the temporal activation variability. The synergy pattern space and CoA of all participants were calculated within each class to analyze the correlation between the variability of muscle synergies and the manipulation performance metrics. The correlation level of each class was further compared. The experimental results evidenced a positive correlation between manipulation performance and maneuvering experience. Similar muscle synergy patterns were reflected between the three classes and the structure of the muscle synergies showed stability. In the class of rich maneuvering experience, the correlation between manipulation performance metrics and muscle synergy is more significant than in the classes of trainees and newbies, suggesting that long-term training and practicing can improve manipulation performance, stability of synergy compositions, and temporal activation variability but not alter the structure of muscle synergies determined by a specific task. Our approaches and findings could be applied to 1) reduce manipulation errors, 2) assist maneuvering training and evaluation to enhance transportation safety, and 3) design technical support for sports.

Optimal Feedback Control for the Proportion of Energy Cost in the Upper-Arm Reaching Movement

The minimum expected energy cost model, which has been proposed as one of the optimization principles for movement planning, can reproduce many characteristics of the human upper-arm reaching movement when signal-dependent noise and the co-contraction of the antagonist's muscles are considered. Regarding the optimization principles, discussion has been mainly based on feedforward control; however, there is debate as to whether the central nervous system uses a feedforward or feedback control process. Previous studies have shown that feedback control based on the modified linear-quadratic gaussian (LQG) control, including multiplicative noise, can reproduce many characteristics of the reaching movement. Although the cost of the LQG control consists of state and energy costs, the relationship between the energy cost and the characteristics of the reaching movement in the LQG control has not been studied. In this work, I investigated how the optimal movement based on the LQG control varied with the proportion of energy cost, assuming that the central nervous system used feedback control. When the cost contained specific proportions of energy cost, the optimal movement reproduced the characteristics of the reaching movement. This result shows that energy cost is essential in both feedforward and feedback control for reproducing the characteristics of the upper-arm reaching movement.

The sensorimotor system modulates muscular co-contraction relative to visuomotor feedback responses to regulate movement variability

The naturally occurring variability in our movements often poses a significant challenge when attempting to produce precise and accurate actions, which is readily evident when playing a game of darts. Two differing, yet potentially complementary, control strategies that the sensorimotor system may use to regulate movement variability are impedance control and feedback control. Greater muscular co-contraction leads to greater impedance that acts to stabilize the hand, while visuomotor feedback responses can be used to rapidly correct for unexpected deviations when reaching toward a target. Here, we examined the independent roles and potential interplay of impedance control and visuomotor feedback control when regulating movement variability. Participants were instructed to perform a precise reaching task by moving a cursor through a narrow visual channel. We manipulated cursor feedback by visually amplifying movement variability and/or delaying the visual feedback of the cursor. We found that participants decreased movement variability by increasing muscular co-contraction, aligned with an impedance control strategy. Participants displayed visuomotor feedback responses during the task but, unexpectedly, there was no modulation between conditions. However, we did find a relationship between muscular co-contraction and visuomotor feedback responses, suggesting that participants modulated impedance control relative to feedback control. Taken together, our results highlight that the sensorimotor system modulates muscular co-contraction, relative to visuomotor feedback responses, to regulate movement variability and produce accurate actions.NEW & NOTEWORTHY The sensorimotor system has the constant challenge of dealing with the naturally occurring variability in our movements. Here, we investigated the potential roles of muscular co-contraction and visuomotor feedback responses to regulate movement variability. When we visually amplified movements, we found that the sensorimotor system primarily uses muscular co-contraction to regulate movement variability. Interestingly, we found that muscular co-contraction was modulated relative to inherent visuomotor feedback responses, suggesting an interplay between impedance and feedback control.

Passive shoulder exoskeleton support partially mitigates fatigue-induced effects in overhead work

BACKGROUND

Despite the potential of occupational passive shoulder exoskeletons (PSEs) to relieve overhead work, limited insights in overhead work precision performance impedes large-scale adoption in industry.

OBJECTIVE

To investigate the effect of PSE support on the reduction in task performance caused by physical fatigue.

METHODS

This experiment consisted of a randomized, counterbalanced cross-over design comparing Exo4Work PSE support and no support, in a physically fatigued state and a control condition. Precision performance was determined using execution speed and drilling errors. Muscle activity and shoulder joint kinematics were recorded.

RESULTS

Fatigue altered task performance, shoulder joint kinematics, muscle activity and subjective experience during overhead work. The PSE support mitigated the fatigue-induced changes in shoulder kinematics. Additionally, a part of the fatigue-induced co-activation of shoulder stabilizing muscles was avoided when working with the PSE. The PSE support also reduced the activity of the anterior and medial deltoid.

CONCLUSION

Physical fatigue provokes compensatory movements and increased co-contraction of muscles when executing overhead work. These fatigue-induced alterations are generally believed to increase the overall musculoskeletal load. The support provided by the PSE reduced muscle activity of muscles working to elevate the arm, but also partially mitigated those fatigue-induced effects.

SIGNIFICANCE

This study shows that the effect of PSE support on precision performance is limited, and suggested that, apart from the known effects of PSE support during overhead work, wearing the exoskeleton in a physically fatigued state may provide additional advantages.

Profiles of movement speed and positional variability based on extended LQG for various noises

Stochastic optimal control has been studied to explain the characteristics of human upper-arm reaching movements. The optimal movement based on an extended linear quadratic Gaussian (LQG) demonstrated that control-dependent noise is the essential factor of the speed-accuracy trade-off in the point-to-point reaching movement. Furthermore, the extended LQG reproduced the profiles of movement speed and positional variability. However, the expected value and variance were computed based on the Monte Carlo method in these studies, which is not considered efficient. In this study, I obtained update equations to efficiently compute the expected value and variance based on the extended LQG. Using the update equations, I computed the profiles of simulated movement speed and positional variability for various amplitudes of noises in a point-to-point reaching movement. The profiles of movement speed were basically bell-shaped for the noises. The speed peak was changed by the control-dependent noise and state-dependent observation noise. The positional variability changed for various noises, and the period during which the variability changed differed with the noise type. Efficient computation in stochastic optimal control based on the extended LQG would contribute to the elucidation of motor control under various noises.

Optimal reaching trajectories based on feedforward control

In human upper-arm reaching movements, the variance of the hand position increases until the middle of the movement and then decreases toward the endpoint. Such decrease in positional variance has been suggested as an evidence to support the hypothesis that our nervous system uses feedback control, rather than feedforward control, for arm reaching tasks. In this study, we computed the optimal trajectories based on feedforward control under several criteria for a one-link two-muscle arm model with considering the stochastic property of muscle activities in order to reexamine the hypothesis. The results showed that the feedforward control also represents the decrease in positional variance in the latter half of the movement when the control signal is planned under the minimum energy cost and minimum variance models. Furthermore, the optimal trajectory that minimizes energy cost represents not only the decrease in positional variance but also many other characteristics of the human reaching movements, e.g., the three-phasic activity of antagonistic muscle, bell-shaped speed curve, N-shaped equilibrium trajectory, and bimodal profile of joint stiffness. These results suggest that minimum energy cost model well expresses the characteristics of hand reaching movements, and our central nervous system would make use of not only a feedback control but also feedforward control.

An approximate stochastic optimal control framework to simulate nonlinear neuro-musculoskeletal models in the presence of noise

Optimal control simulations have shown that both musculoskeletal dynamics and physiological noise are important determinants of movement. However, due to the limited efficiency of available computational tools, deterministic simulations of movement focus on accurately modelling the musculoskeletal system while neglecting physiological noise, and stochastic simulations account for noise while simplifying the dynamics. We took advantage of recent approaches where stochastic optimal control problems are approximated using deterministic optimal control problems, which can be solved efficiently using direct collocation. We were thus able to extend predictions of stochastic optimal control as a theory of motor coordination to include muscle coordination and movement patterns emerging from non-linear musculoskeletal dynamics. In stochastic optimal control simulations of human standing balance, we demonstrated that the inclusion of muscle dynamics can predict muscle co-contraction as minimal effort strategy that complements sensorimotor feedback control in the presence of sensory noise. In simulations of reaching, we demonstrated that nonlinear multi-segment musculoskeletal dynamics enables complex perturbed and unperturbed reach trajectories under a variety of task conditions to be predicted. In both behaviors, we demonstrated how interactions between task constraint, sensory noise, and the intrinsic properties of muscle influence optimal muscle coordination patterns, including muscle co-contraction, and the resulting movement trajectories. Our approach enables a true minimum effort solution to be identified as task constraints, such as movement accuracy, can be explicitly imposed, rather than being approximated using penalty terms in the cost function. Our approximate stochastic optimal control framework predicts complex features, not captured by previous simulation approaches, providing a generalizable and valuable tool to study how musculoskeletal dynamics and physiological noise may alter neural control of movement in both healthy and pathological movements.

Dynamic Primitives Limit Human Force Regulation during Motion

Humans excel at physical interaction despite long feedback delays and low-bandwidth actuators. Yet little is known about how humans manage physical interaction. A quantitative understanding of how they do is critical for designing machines that can safely and effectively interact with humans, e.g. amputation prostheses, assistive exoskeletons, therapeutic rehabilitation robots, and physical human-robot collaboration. To facilitate applications, this understanding should be in the form of a simple mathematical model that not only describes humans' capabilities but also their limitations. In robotics, hybrid control allows simultaneous, independent control of both motion and force and it is often assumed that humans can modulate force independent of motion as well. This paper experimentally tested that assumption. Participants were asked to apply a constant 5N force on a robot manipulandum that moved along an elliptical path. After initial improvement, force errors quickly plateaued, despite practice and visual feedback. Within-trial analyses revealed that force errors varied with position on the ellipse, rejecting the hypothesis that humans have independent control of force and motion. The findings are consistent with a feed-forward motion command composed of two primitive oscillations acting through mechanical impedance to evoke force.

The neural mechanisms of manual dexterity

The hand endows us with unparalleled precision and versatility in our interactions with objects, from mundane activities such as grasping to extraordinary ones such as virtuoso pianism. The complex anatomy of the human hand combined with expansive and specialized neuronal control circuits allows a wide range of precise manual behaviours. To support these behaviours, an exquisite sensory apparatus, spanning the modalities of touch and proprioception, conveys detailed and timely information about our interactions with objects and about the objects themselves. The study of manual dexterity provides a unique lens into the sensorimotor mechanisms that endow the nervous system with the ability to flexibly generate complex behaviour.

Costs of position, velocity, and force requirements in optimal control induce triphasic muscle activation during reaching movement

The nervous system activates a pair of agonist and antagonist muscles to determine the muscle activation pattern for a desired movement. Although there is a problem with redundancy, it is solved immediately, and movements are generated with characteristic muscle activation patterns in which antagonistic muscle pairs show alternate bursts with a triphasic shape. To investigate the requirements for deriving this pattern, this study simulated arm movement numerically by adopting a musculoskeletal arm model and an optimal control. The simulation reproduced the triphasic electromyogram (EMG) pattern observed in a reaching movement using a cost function that considered three terms: end-point position, velocity, and force required; the function minimised neural input. The first, second, and third bursts of muscle activity were generated by the cost terms of position, velocity, and force, respectively. Thus, we concluded that the costs of position, velocity, and force requirements in optimal control can induce triphasic EMG patterns. Therefore, we suggest that the nervous system may control the body by using an optimal control mechanism that adopts the costs of position, velocity, and force required; these costs serve to initiate, decelerate, and stabilise movement, respectively.

On the encoding capacity of human motor adaptation

Primitive-based models of motor learning suggest that adaptation occurs by tuning the responses of motor primitives. Based on this idea, we consider motor learning as an information encoding procedure, that is, a procedure of encoding a motor skill into primitives. The capacity of encoding is determined by the number of recruited primitives, which depends on how many primitives are "visited" by the movement, and this leads to a rather counterintuitive prediction that faster movement, where a larger number of motor primitives are involved, allows learning more complicated motor skills. Here, we provide a set of experimental results that support this hypothesis. First, we show that learning occurs only with movement, that is, only with nonzero encoding capacity. When participants were asked to counteract a rotating force applied to a robotic handle, they were unable to do so when maintaining a static posture but were able to adapt when making small circular movements. Our second experiment further investigated how adaptation is affected by movement speed. When adapting to a simple (low-information-content) force field, fast (high-capacity) movement did not have an advantage over slow (low-capacity) movement. However, for a complex (high-information-content) force field, the fast movement showed a significant advantage over slow movement. Our final experiment confirmed that the observed benefit of high-speed movement is only weakly affected by mechanical factors. Taken together, our results suggest that the encoding capacity is a genuine limiting factor of human motor adaptation.NEW & NOTEWORTHY We propose a novel concept called "encoding capacity" of motor adaptation, which describes an inherent limiting-factor of our brain's ability to learn new motor skills, just like any other storage system. By reinterpreting the existing primitive-based models of motor learning, we hypothesize that the encoding capacity is determined by the size of the movement, and present a set of experimental evidence suggesting that such limiting effect of encoding capacity does exist in human motor adaptation.

Modulation of finger muscle activation patterns across postures is coordinated across all muscle groups

Successful grasp requires that grip forces be properly directed between the fingertips and the held object. Changes in digit posture significantly affect the mapping between muscle force and fingertip force. Joint torques must subsequently be altered to maintain the desired force direction at the fingertips. Our current understanding of the roles of hand muscles in force production remains incomplete, as past studies focused on a limited set of postures or force directions. To thoroughly examine how hand muscles adapt to changing external (force direction) and internal (posture) conditions, activation patterns of six index finger muscles were examined with intramuscular electrodes in 10 healthy subjects. Participants produced submaximal isometric forces in each of six orthogonal directions at nine different finger postures. Across force directions, participants significantly altered activation patterns to accommodate postural changes in the interphalangeal joint angles but not changes in the metacarpophalangeal joint angles. Modulation of activation levels of the extrinsic hand muscles, particularly the extensors, were as great as those of intrinsic muscles, suggesting that both extrinsic and intrinsic muscles were involved in creating the desired forces. Despite considerable between-subject variation in the absolute activation patterns, principal component analysis revealed that participants used similar strategies to accommodate the postural changes. The changes in muscle coordination also helped increase joint impedance in order to stabilize the end-point force direction. This effect counteracts the increased signal-dependent motor noise that arises with greater magnitude of muscle activation as interphalangeal joint flexion is increased. These results highlight the role of the extrinsic muscles in controlling fingertip force direction across finger postures.NEW & NOTEWORTHY We examined how hand muscles adapt to changing external (force direction) and internal (posture) conditions. Muscle activations, particularly of the extrinsic extensors, were significantly affected by postural changes of the interphalangeal, but not metacarpophalangeal, joints. Joint impedance was modulated so that the effects of the signal-dependent motor noise on the force output were reduced. Comparisons with theoretical solutions showed that the chosen activation patterns occupied a small portion of the possible solution space, minimizing the maximum activation of any one muscle.

Role of muscle coactivation in adaptation of standing posture during arm reaching

The ability to maintain stable, upright standing in the face of perturbations is a critical component of daily life. A common strategy for resisting perturbations and maintaining stability is muscle coactivation. Although arm muscle coactivation is often used during adaptation of seated reaching movements, little is known about postural muscle activation during concurrent adaptation of arm and standing posture to novel perturbations. In this study we investigate whether coactivation strategies are employed during adaptation of standing postural control, and how these strategies are prioritized for adaptation of standing posture and arm reaching, in two different postural stability conditions. Healthy adults practiced planar reaching movements while grasping the handle of a robotic arm and standing on a force plate; the robotic arm generated a velocity-dependent force field that created novel perturbations in the forward (more stable) or backward (less stable) direction. Surprisingly, the degree of arm and postural adaptation was not influenced by stability, with similar adaptation observed between conditions in the control of both arm movement and standing posture. We found that an early coactivation strategy can be used in postural adaptation, similar to what is observed in adaptation of arm reaching movements. However, the emergence of a coactivation strategy was dependent on perturbation direction. Despite similar adaptation in both directions, postural coactivation was largely specific to forward perturbations. Backward perturbations led to less coactivation and less modulation of postural muscle activity. These findings provide insight into how postural stability can affect prioritization of postural control objectives and movement adaptation strategies.NEW & NOTEWORTHY Muscle coactivation is a key strategy for modulating movement stability; this is centrally important in the control of standing posture. Our study investigates the little-known role of coactivation in adaptation of whole body standing postural control. We demonstrate that an early coactivation strategy can be used in postural adaptation, but muscle activation strategies may differ depending on postural stability conditions.

Explanation of Fitts' law in Reaching Movement based on Human Arm Dynamics

Why does Fitts’ law fit various human behavioural data well even though it is not a model based on human physical dynamics? To clarify this, we derived the relationships among the factors applied in Fitts’ law—movement duration and spatial endpoint error—based on a multi-joint forward- and inverse-dynamics models in the presence of signal-dependent noise. As a result, the relationship between them was modelled as an inverse proportion. To validate whether the endpoint error calculated by the model can represent the endpoint error of actual movements, we conducted a behavioural experiment in which centre-out reaching movements were performed under temporal constraints in four directions using the shoulder and elbow joints. The result showed that the distributions of model endpoint error closely expressed the observed endpoint error distributions. Furthermore, the model was found to be nearly consistent with Fitts’ law. Further analysis revealed that the coefficients of Fitts’ law could be expressed by arm dynamics and signal-dependent noise parameters. Consequently, our answer to the question above is: Fitts’ law for reaching movements can be expressed based on human arm dynamics; thus, Fitts’ law closely fits human’s behavioural data under various conditions.

Increasing muscle co-contraction speeds up internal model acquisition during dynamic motor learning

During reaching movements in the presence of novel dynamics, participants initially co-contract their muscles to reduce kinematic errors and improve task performance. As learning proceeds, muscle co-contraction decreases as an accurate internal model develops. The initial co-contraction could affect the learning of the internal model in several ways. By ensuring the limb remains close to the target state, co-contraction could speed up learning. Conversely, by reducing kinematic errors, a key training signal, it could slow down learning. Alternatively, given that the effects of muscle co-contraction on kinematic errors are predictable and could be discounted when assessing the internal model error, it could have no effect on learning. Using a sequence of force pulses, we pretrained two groups to either co-contract (stiff group) or relax (relaxed group) their arm muscles in the presence of dynamic perturbations. A third group (control group) was not pretrained. All groups performed reaching movements in a velocity-dependent curl field. We measured adaptation using channel trials and found greater adaptation in the stiff group during early learning. We also found a positive correlation between muscle co-contraction, as measured by surface electromyography, and adaptation. These results show that muscle co-contraction accelerates the rate of dynamic motor learning.

Submovement control processes in discrete aiming as a function of space-time constraints

There is preliminary evidence that there are several types of submovements in movement aiming that reflect different processes of control and can result from particular task constraints. The purpose of the study was to investigate the effect of movement space and time task criteria on the prevalence of different submovement control characteristics in discrete aiming. Twelve participants completed 3 distance x 5 time conditions each with 100 trials in a target-aiming movement task. The kinematic structure of the trajectory determined the prevalence of 5 submovement types (none; pre-peak, post-peak movement velocity; undershoot, overshoot). The findings showed that the overall number of submovements increased in the slower space-time conditions and was predominantly characterized by post-peak trajectory submovements rather than discrete overshoot submovements. Overshoot submovements were more frequent in the high average movement velocity and short time duration conditions. We concluded that there are qualitatively different distributional patterns of submovement types in discrete aiming tasks that are organized by the quantitative scaling of the average movement velocity arising from multiple control processes to meet the specific space-time task constraints.

Optimal feedback control to describe multiple representations of primary motor cortex neurons

Primary motor cortex (M1) neurons are tuned in response to several parameters related to motor control, and it was recently reported that M1 is important in feedback control. However, it remains unclear how M1 neurons encode information to control the musculoskeletal system. In this study, we examined the underlying computational mechanisms of M1 based on optimal feedback control (OFC) theory, which is a plausible hypothesis for neuromotor control. We modelled an isometric torque production task that required joint torque to be regulated and maintained at desired levels in a musculoskeletal system physically constrained by muscles, which act by pulling rather than pushing. Then, a feedback controller was computed using an optimisation approach under the constraint. In the presence of neuromotor noise, known as signal-dependent noise, the sensory feedback gain is tuned to an extrinsic motor output, such as the hand force, like a population response of M1 neurons. Moreover, a distribution of the preferred directions (PDs) of M1 neurons can be predicted via feedback gain. Therefore, we suggest that neural activity in M1 is optimised for the musculoskeletal system. Furthermore, if the feedback controller is represented in M1, OFC can describe multiple representations of M1, including not only the distribution of PDs but also the response of the neuronal population.

Skilful force control in expert pianists

Dexterous object manipulation in skilful behaviours such as surgery, craft making, and musical performance involves fast, precise, and efficient control of force with the fingers. A challenge in playing musical instruments is the requirement of independent control of the magnitude and rate of force production, which typically vary in relation to loudness and tempo. However, it is unknown how expert musicians skilfully control finger force to elicit tones with a wide range of loudness and tempi. Here, we addressed this issue by comparing the variation of spatiotemporal characteristics of force during repetitive and simultaneous piano keystrokes in relation to the loudness and tempo between pianists and musically untrained individuals. While the peak key-descending velocity varied with loudness but not with tempo in both groups, the peak and impulse of the key-depressing force were smaller in pianists than in the non-musicians, specifically when eliciting loud tones, suggesting superior energetic efficiency in the trained individuals. The key-depressing force was more consistent across strikes in pianists than in the non-musicians at all loudness levels but only at slow tempi, confirming expertise-dependency of precise force control. A regression analysis demonstrated that individual differences in the keystroke rates when playing at the fastest tempo across the trained pianists were negatively associated with the force impulse during the key depression but not with the peak force only at the loudest tone. This suggests that rapid reductions of force following the key depression plays a role in considerably fast performance of repetitive piano keystrokes.

Using noise to shape motor learning

Each of our movements is selected from any number of alternative movements. Some studies have shown evidence that the central nervous system (CNS) chooses to make the specific movements that are least affected by motor noise. Previous results showing that the CNS has a natural tendency to minimize the effects of noise make the direct prediction that if the relationship between movements and noise were to change, the specific movements people learn to make would also change in a predictable manner. Indeed, this has been shown for well-practiced movements such as reaching. Here, we artificially manipulated the relationship between movements and visuomotor noise by adding noise to a motor task in a novel redundant geometry such that there arose a single control policy that minimized the noise. This allowed us to see whether, for a novel motor task, people could learn the specific control policy that minimized noise or would need to employ other compensation strategies to overcome the added noise. As predicted, subjects were able to learn movements that were biased toward the specific ones that minimized the noise, suggesting not only that the CNS can learn to minimize the effects of noise in a novel motor task but also that artificial visuomotor noise can be a useful tool for teaching people to make specific movements. Using noise as a teaching signal promises to be useful for rehabilitative therapies and movement training with human-machine interfaces.

NEW & NOTEWORTHY

Many theories argue that we choose to make the specific movements that minimize motor noise. Here, by changing the relationship between movements and noise, we show that people actively learn to make movements that minimize noise. This not only provides direct evidence for the theories of noise minimization but presents a way to use noise to teach specific movements to improve rehabilitative therapies and human-machine interface control.

The duration of reaching movement is longer than predicted by minimum variance

Whether the central nervous system minimizes variability or effort in planning arm movements can be tested by measuring the preferred movement duration and end-point variability. Here we conducted an experiment in which subjects performed arm reaching movements without visual feedback in fast-, medium-, slow-, and preferred-duration conditions. Results show that 1) total end-point variance was smallest in the medium-duration condition and 2) subjects preferred to carry out movements that were slower than this medium-duration condition. A parsimonious explanation for the overall pattern of end-point errors across fast, medium, preferred, and slow movement durations is that movements are planned to minimize effort as well as end-point error due to both signal-dependent and constant noise.

Changes in motor synergies for tracking movement and responses to perturbations depend on task-irrelevant dimension constraints

We investigated the changes in the motor synergies of target-tracking movements of hands and the responses to perturbation when the dimensionalities of target positions were changed. We used uncontrolled manifold (UCM) analyses to quantify the motor synergies. The target was changed from one to two dimensions, and the direction orthogonal to the movement direction was switched from task-irrelevant directions to task-relevant directions. The movement direction was task-relevant in both task conditions. Hence, we evaluated the effects of constraints on the redundant dimensions on movement tracking. Moreover, we could compare the two types of responses to the same directional perturbations in one- and two-dimensional target tasks. In the one-dimensional target task, the perturbation along the movement direction and the orthogonal direction were task-relevant and -irrelevant perturbations, respectively. In the two-dimensional target task, the both perturbations were task-relevant perturbations. The results of the experiments showed that the variabilities of the hand positions in the two-dimensional target-tracking task decreased, but the variances of the joint angles did not significantly change. For the task-irrelevant perturbations, the variances of the joint angles within the UCM that did not affect hand position (UCM component) increased. For the task-relevant perturbations, the UCM component tended to increase when the available UCM was large. These results suggest that humans discriminate whether the perturbations were task-relevant or -irrelevant and then adjust the responses of the joints by utilizing the available UCM.

The curse of motor expertise: Use-dependent focal dystonia as a manifestation of maladaptive changes in body representation

Focal task-specific dystonia (FTSD) impairs not only motor dexterity, but also somatosensory perception involved in well-trained behavioral tasks. Occupations that carry a risk of developing FTSD include musician, writer, painter, surgeon, and golfer, which are characterized by repetitive and precise motor actions over a prolonged period. Behavioral studies have uncovered various undesirable effects of FTSD on sensorimotor functions, such as a loss of independent movement control, unintended muscular co-activation, awkward limb posture, and impairment of fine discrimination of tactile and proprioceptive sensations. Studies using neuroimaging and noninvasive brain stimulation techniques have related such sensorimotor malfunctions to maladaptive neuroplastic changes in the sensorimotor system, including the primary motor and somatosensory areas, premotor area, cerebellum, and basal ganglia. In this review, we summarize recent empirical findings regarding phenomenological and pathophysiological abnormalities associated with the development of FTSD. We particularly focused on maladaptive alterations of body representations underlying the degradation of fine motor control and somatosensory perception in FTSD patients.

Practice reduces task relevant variance modulation and forms nominal trajectory

Humans are capable of achieving complex tasks with redundant degrees of freedom. Much attention has been paid to task relevant variance modulation as an indication of online feedback control strategies to cope with motor variability. Meanwhile, it has been discussed that the brain learns internal models of environments to realize feedforward control with nominal trajectories. Here we examined trajectory variance in both spatial and temporal domains to elucidate the relative contribution of these control schemas. We asked subjects to learn reaching movements with multiple via-points, and found that hand trajectories converged to stereotyped trajectories with the reduction of task relevant variance modulation as learning proceeded. Furthermore, variance reduction was not always associated with task constraints but was highly correlated with the velocity profile. A model assuming noise both on the nominal trajectory and motor command was able to reproduce the observed variance modulation, supporting an expression of nominal trajectories in the brain. The learning-related decrease in task-relevant modulation revealed a reduction in the influence of optimal feedback around the task constraints. After practice, the major part of computation seems to be taken over by the feedforward controller around the nominal trajectory with feedback added only when it becomes necessary.

Secrets of virtuoso: neuromuscular attributes of motor virtuosity in expert musicians

Musical performance requires extremely fast and dexterous limb movements. The underlying biological mechanisms have been an object of interest among scientists and non-scientists for centuries. Numerous studies of musicians and non-musicians have demonstrated that neuroplastic adaptations through early and deliberate musical training endowed superior motor skill. However, little has been unveiled about what makes inter-individual differences in motor skills among musicians. Here we determined the attributes of inter-individual differences in the maximum rate of repetitive piano keystrokes in twenty-four pianists. Among various representative factors of neuromuscular functions, anatomical characteristics, and training history, a stepwise multiple regression analysis and generalized linear model identified two predominant predictors of the maximum rate of repetitive piano keystrokes; finger tapping rate and muscular strength of the elbow extensor. These results suggest a non-uniform role of individual limb muscles in the production of extremely fast repetitive multi-joint movements. Neither age of musical training initiation nor the amount of extensive musical training before age twenty was a predictor. Power grip strength was negatively related to the maximum rate of piano keystrokes only during the smallest tone production. These findings highlight the importance of innate biological nature and explicit training for motor virtuosity.

Optimality of Upper-Arm Reaching Trajectories Based on the Expected Value of the Metabolic Energy Cost

When we move our body to perform a movement task, our central nervous system selects a movement trajectory from an infinite number of possible trajectories under constraints that have been acquired through evolution and learning. Minimization of the energy cost has been suggested as a potential candidate for a constraint determining locomotor parameters, such as stride frequency and stride length; however, other constraints have been proposed for a human upper-arm reaching task. In this study, we examined whether the minimum metabolic energy cost model can also explain the characteristics of the upper-arm reaching trajectories. Our results show that the optimal trajectory that minimizes the expected value of energy cost under the effect of signal-dependent noise on motor commands expresses not only the characteristics of reaching movements of typical speed but also those of slower movements. These results suggest that minimization of the energy cost would be a basic constraint not only in locomotion but also in upper-arm reaching.

Control model for dampening hand vibrations using information of internal and external coordinates

In the present study, we investigate a control mechanism that dampens hand vibrations. Here, we propose a control method with two components to suppress hand vibrations. The first is a passive suppression method that lowers the joint stiffness to passively dampen the hand vibrations. The second is an active suppression method that adjusts an equilibrium point based on skyhook control to actively dampen the hand vibrations. In a simulation experiment, we applied these two methods to dampen hand vibrations during the shoulder's horizontal oscillation. We also conducted a measurement experiment wherein a subject's shoulder was sinusoidally oscillated by a platform that generated horizontal oscillations. The results of the measurement experiments showed that the jerk of each part of the arm in a task using a cup filled with water was smaller than the shoulder jerk and that in a task with a cup filled with stones was larger than the shoulder jerk. Moreover, the amplitude of the hand trajectory in both horizontal and vertical directions was smaller in a task using a cup filled with water than in a task using a cup filled with stones. The results of the measurement experiments were accurately reproduced by the active suppression method based on skyhook control. These results suggest that humans dampen hand vibrations by controlling the equilibrium point through the information of the external workspace and the internal body state rather than by lowering joint stiffness only by using internal information.

Comparative Electromyography Analysis of the Upper Extremity between Inexperienced and Elite Water Polo Players during an Overhead Shot

The aim of this study was to compare muscle activity patterns between inexperienced and experienced water polo players while taking an overhead shot. The study was carried out with a group of 12 water polo players and an inexperienced group of 10 healthy participants. Signals were recorded by surface electromyography from six different muscles. The average and standard deviation of the normalized electrical activity, time to peak, time broadness, and muscle sequencing during the overhead shot were determined for each muscle in both groups and compared with each other. In water polo players, the normalized electrical activities of triceps brachii, pectoralis major, and wrist flexors were greater than other muscles, while in the inexperienced group the triceps brachii specifically played an important role. There was minimal activation of the middle deltoid and biceps brachii in water polo players. Increased times to peak and time broadness of muscles were found in the inexperienced group compared to experienced water polo players; this difference may be explained by different neuromuscular proprioception. Only experienced water polo players activated the observed muscles in a specific sequence, from proximal to distal. Therefore, coaches should emphasize smooth and quick transitions from proximal to distal segments, with less importance placed on individual muscle strengthening.

Task complexity and maximal isometric strength gains through motor learning

This study compared the effects of a simple versus complex contraction pattern on the acquisition, retention, and transfer of maximal isometric strength gains and reductions in force variability. A control group (N = 12) performed simple isometric contractions of the wrist flexors. An experimental group (N = 12) performed complex proprioceptive neuromuscular facilitation (PNF) contractions consisting of maximal isometric wrist extension immediately reversing force direction to wrist flexion within a single trial. Ten contractions were completed on three consecutive days with a retention and transfer test 2‐weeks later. For the retention test, the groups performed their assigned contraction pattern followed by a transfer test that consisted of the other contraction pattern for a cross‐over design. Both groups exhibited comparable increases in strength (20.2%, P < 0.01) and reductions in mean torque variability (26.2%, P < 0.01), which were retained and transferred. There was a decrease in the coactivation ratio (antagonist/agonist muscle activity) for both groups, which was retained and transferred (35.2%, P < 0.01). The experimental group exhibited a linear decrease in variability of the torque‐ and sEMG‐time curves, indicating transfer to the simple contraction pattern (P < 0.01). The control group underwent a decrease in variability of the torque‐ and sEMG‐time curves from the first day of training to retention, but participants returned to baseline levels during the transfer condition (P < 0.01). However, the difference between torque RMS error versus the variability in torque‐ and sEMG‐time curves suggests the demands of the complex task were transferred, but could not be achieved in a reproducible way.

This study examines the effect of task complexity on the acquisition, retention, and transfer of increases in maximal strength and decreases in force variability, which is novel. Simple agonist‐only contractions are compared to a more complex reversal contraction pattern as used during proprioceptive neuromuscular facilitation (PNF). The goal was to determine if the more complex contraction pattern interferes with the strength gains and reduced variability by impeding the development of agonist‐antagonist coordination.

A multi-level approach to investigate the control of an input device: application to a realistic pointing task

This study investigates the subjects' performance during realistic conditions of control of a joystick. An adapted reciprocal aiming task consisting in driving a virtual vehicle along a slalom course as fast as possible was performed while accuracy constraints were manipulated. Realistic dynamical Interface Screen Relationship between the joystick displacements and the displacements of the vehicle was simulated. Vehicle displacements and motor activity (muscle activity and joint kinematics) were recorded. The results highlighted the applicability of the Fitts' law to more realistic conditions where the use of an input device is performed in an intensive control situation. Besides, biomechanical results suggested that neuromuscular responses were different regarding the direction of movement, whereas the performance at a behavioural level were not affected. Thus, this study demonstrates the interest in considering two different aspects of the user's performance (behavioural and biomechanical ones) to make a better agreement between the device design and users' needs.

PRACTITIONER SUMMARY

This study considered two different aspects of the subject’s performance in a realistic situation of speed–accuracy trade-off: the behavioural and motor activity. The necessity for the design of the future ergonomics pointing devices to meet the expectations of the neuromuscular system in order to facilitate their uses is highlighted.

Motor inhibition affects the speed but not accuracy of aimed limb movements in an insect

When reaching toward a target, human subjects use slower movements to achieve higher accuracy, and this can be accompanied by increased limb impedance (stiffness, viscosity) that stabilizes movements against motor noise and external perturbation. In arthropods, the activity of common inhibitory motor neurons influences limb impedance, so we hypothesized that this might provide a mechanism for speed and accuracy control of aimed movements in insects. We recorded simultaneously from excitatory leg motor neurons and from an identified common inhibitory motor neuron (CI1) in locusts that performed natural aimed scratching movements. We related limb movement kinematics to recorded motor activity and demonstrate that imposed alterations in the activity of CI1 influenced these kinematics. We manipulated the activity of CI1 by injecting depolarizing or hyperpolarizing current or killing the cell using laser photoablation. Naturally higher levels of inhibitory activity accompanied faster movements. Experimentally biasing the firing rate downward, or stopping firing completely, led to slower movements mediated by changes at several joints of the limb. Despite this, we found no effect on overall movement accuracy. We conclude that inhibitory modulation of joint stiffness has effects across most of the working range of the insect limb, with a pronounced effect on the overall velocity of natural movements independent of their accuracy. Passive joint forces that are greatest at extreme joint angles may enhance accuracy and are not affected by motor inhibition.

Environmental consistency determines the rate of motor adaptation

BACKGROUND

The motor system has the remarkable ability not only to learn but also to learn how fast it should learn. However, the mechanisms behind this ability are not well understood. Previous studies have posited that the rate of adaptation in a given environment is determined by Bayesian sensorimotor integration based on the amount of variability in the state of the environment. However, experimental results have failed to support several predictions of this theory.

RESULTS

We show that the rate at which the motor system adapts to changes in the environment is primarily determined not by the degree to which environmental change occurs but by the degree to which the changes that do occur persist from one movement to the next, i.e., the consistency of the environment. We demonstrate a striking double dissociation whereby feedback response strength is predicted by environmental variability rather than consistency, whereas adaptation rate is predicted by environmental consistency rather than variability. We proceed to elucidate the role of stimulus repetition in speeding up adaptation and find that repetition can greatly potentiate the effect of consistency, although unlike consistency, repetition alone does not increase adaptation rate. By leveraging this understanding, we demonstrate that the rate of motor adaptation can be modulated over a range that encompasses a 20-fold increase from lowest to highest.

CONCLUSIONS

Understanding the mechanisms that determine the rate of motor adaptation could lead to the principled design of improved procedures for motor training and rehabilitation. Regimens designed to control environmental consistency and repetition during training might yield faster, more robust motor learning.

Strengths and limitations of a musculoskeletal model for an analysis of simulated meat cutting tasks

This study assessed the capacity of a musculoskeletal model to predict the relative muscle activation changes as a function of the workbench height and the movement direction during a simulated meat cutting task. Seven subjects performed a cutting task alternating two cutting directions for 20 s at four different workbench heights. Kinematics, electromyography (EMG), and cutting force data were collected and used to drive a musculoskeletal model of the shoulder girdle. The model predicted the muscle forces exerted during the task. Both the recorded and computed activation of the muscles was then compared by means of cross-correlation and by comparison of muscle activation trends with respect to the workstation parameters, i.e. cutting direction and workbench height. The results indicated that cutting movements involving arm flexion are preferable to movement requiring internal arm rotation and abduction. The optimal bench height for meat cutting tasks should be between 20 and 30 cm below the worker's elbow height. The present study underlines a beneficial use of musculoskeletal models for adjusting workstation parameters.

Comparing functional EMG characteristics between zero-order and first-order interface dynamics

The optimal control scheme for powered prostheses can be determined using simulation experiments, for which an accurate model of prosthesis control is essential. This paper focuses on electromyographic (EMG) control signal characteristics across two different control schemes. We constructed a functional EMG model comprising three EMG signal characteristics-standard deviation, kurtosis, and median power frequency-using data collected under realistic conditions for prosthesis control (closed-loop, dynamic, anisometric contractions). We examined how the model changed when subjects used zero-order or first-order control. Control order had a statistically significant effect on EMG characteristics, but the effect size was small and generally did not exceed inter-subject variability. Therefore, we suggest that this functional EMG model remains valid across different control schemes.

Martial arts striking hand peak acceleration, accuracy and consistency

The goal of this paper was to investigate the possible trade-off between peak hand acceleration and accuracy and consistency of hand strikes performed by martial artists of different training experiences. Ten male martial artists with training experience ranging from one to nine years volunteered to participate in the experiment. Each participant performed 12 maximum effort goal-directed strikes. Hand acceleration during the strikes was obtained using a tri-axial accelerometer block. A pressure sensor matrix was used to determine the accuracy and consistency of the strikes. Accuracy was estimated by the radial distance between the centroid of each subject's 12 strikes and the target, whereas consistency was estimated by the square root of the 12 strikes mean squared distance from their centroid. We found that training experience was significantly correlated to hand peak acceleration prior to impact (r(2)=0.456, p =0.032) and accuracy (r(2)=0. 621, p=0.012). These correlations suggest that more experienced participants exhibited higher hand peak accelerations and at the same time were more accurate. Training experience, however, was not correlated to consistency (r(2)=0.085, p=0.413). Overall, our results suggest that martial arts training may lead practitioners to achieve higher striking hand accelerations with better accuracy and no change in striking consistency.

Influence of task constraints and device properties on motor patterns in a realistic control situation

The influences of task difficulty (index difficulty: 2-4), input device of different length, range of motion and mode of resistance (joystick or rotorcraft stick), and directions of movement (leftward rightward) on motor patterns in a realistic control situation were examined with a multilevel analysis (joint kinematics and muscular variables, and global task performance). Eight subjects controlled the displacements of a virtual object during a slalom task characterized by a realistic inertial model. Pilots adapted the endpoint kinematic organization to increasing accuracy constraints to preserve task success whatever the device and the direction. However, the rotorcraft stick manipulation remains highly complex in comparison to the joystick due to poorer proprioceptive information, higher inertial constraints, and an asymmetrical muscle control.

Two-phase strategy of neural control for planar reaching movements: II--relation to spatiotemporal characteristics of movement trajectory

In the companion paper utilizing a quantitative model of optimal motor coordination (Part I, Rand and Shimansky, in Exp Brain Res 225:55-73, 2013), we examined coordination between X and Y movement directions (XYC) during reaching movements performed under three prescribed speeds, two movement amplitudes, and two target sizes. The obtained results indicated that the central nervous system (CNS) utilizes a two-phase strategy, where the initial and the final phases correspond to lower and higher precision of information processing, respectively, for controlling goal-directed reach-type movements to optimize the total cost of task performance including the cost of neural computations. The present study investigates how two different well-known concepts used for describing movement performance relate to the concepts of optimal XYC and two-phase control strategy. First, it is examined to what extent XYC is equivalent to movement trajectory straightness. The data analysis results show that the variability, the movement trajectory's deviation from the straight line, increases with an increase in prescribed movement speed. In contrast, the dependence of XYC strength on movement speed is opposite (in total agreement with an assumption of task performance optimality), suggesting that XYC is a feature of much higher level of generality than trajectory straightness. Second, it is tested how well the ballistic and the corrective components described in the traditional concept of two-component model of movement performance match with the initial and the final phase of the two-phase control strategy, respectively. In fast reaching movements, the percentage of trials with secondary corrective submovement was smaller under larger-target shorter-distance conditions. In slower reaching movements, meaningful parsing was impossible due to massive fluctuations in the kinematic profile throughout the movement. Thus, the parsing points determined by the conventional submovement analysis did not consistently reflect separation between the ballistic and error-corrective components. In contrast to the traditional concept of two-component movement performance, the concept of two-phase control strategy is applicable to a wide variety of experimental conditions.

Movement trajectory smoothness is not associated with the endpoint accuracy of rapid multi-joint arm movements in young and older adults

The minimum variance theory proposes that motor commands are corrupted by signal-dependent noise and smooth trajectories with low noise levels are selected to minimize endpoint error and endpoint variability. The purpose of the study was to determine the contribution of trajectory smoothness to the endpoint accuracy and endpoint variability of rapid multi-joint arm movements. Young and older adults performed arm movements (4 blocks of 25 trials) as fast and as accurately as possible to a target with the right (dominant) arm. Endpoint accuracy and endpoint variability along with trajectory smoothness and error were quantified for each block of trials. Endpoint error and endpoint variance were greater in older adults compared with young adults, but decreased at a similar rate with practice for the two age groups. The greater endpoint error and endpoint variance exhibited by older adults were primarily due to impairments in movement extent control and not movement direction control. The normalized jerk was similar for the two age groups, but was not strongly associated with endpoint error or endpoint variance for either group. However, endpoint variance was strongly associated with endpoint error for both the young and older adults. Finally, trajectory error was similar for both groups and was weakly associated with endpoint error for the older adults. The findings are not consistent with the predictions of the minimum variance theory, but support and extend previous observations that movement trajectories and endpoints are planned independently.

Motor-sensory confluence in tactile perception

Perception involves motor control of sensory organs. However, the dynamics underlying emergence of perception from motor-sensory interactions are not yet known. Two extreme possibilities are as follows: (1) motor and sensory signals interact within an open-loop scheme in which motor signals determine sensory sampling but are not affected by sensory processing and (2) motor and sensory signals are affected by each other within a closed-loop scheme. We studied the scheme of motor-sensory interactions in humans using a novel object localization task that enabled monitoring the relevant overt motor and sensory variables. We found that motor variables were dynamically controlled within each perceptual trial, such that they gradually converged to steady values. Training on this task resulted in improvement in perceptual acuity, which was achieved solely by changes in motor variables, without any change in the acuity of sensory readout. The within-trial dynamics is captured by a hierarchical closed-loop model in which lower loops actively maintain constant sensory coding, and higher loops maintain constant sensory update flow. These findings demonstrate interchangeability of motor and sensory variables in perception, motor convergence during perception, and a consistent hierarchical closed-loop perceptual model.

A computational model for optimal muscle activity considering muscle viscoelasticity in wrist movements

To understand the mechanism of neural motor control, it is important to clarify how the central nervous system organizes the coordination of redundant muscles. Previous studies suggested that muscle activity for step-tracking wrist movements are optimized so as to reduce total effort or end-point variance under neural noise. However, since the muscle dynamics were assumed as a simple linear system, some characteristic patterns of experimental EMG were not seen in the simulated muscle activity of the previous studies. The biological muscle is known to have dynamic properties in which its elasticity and viscosity depend on activation level. The motor control system is supposed to consider the viscoelasticity of the muscles when generating motor command signals. In this study, we present a computational motor control model that can control a musculoskeletal system with nonlinear dynamics. We applied the model to step-tracking wrist movements actuated by five muscles with dynamic viscoelastic properties. To solve the motor redundancy, we designed the control model to generate motor commands that maximize end-point accuracy under signal-dependent noise, while minimizing the squared sum of them. Here, we demonstrate that the muscle activity simulated by our model exhibits spatiotemporal features of experimentally observed muscle activity of human and nonhuman primates. In addition, we show that the movement trajectories resulting from the simulated muscle activity resemble experimentally observed trajectories. These results suggest that, by utilizing inherent viscoelastic properties of the muscles, the neural system may optimize muscle activity to improve motor performance.

Proximal versus distal control of two-joint planar reaching movements in the presence of neuromuscular noise

Determining how the human nervous system contends with neuro-motor noise is vital to understanding how humans achieve accurate goal-directed movements. Experimentally, people learning skilled tasks tend to reduce variability in distal joint movements more than in proximal joint movements. This suggests that they might be imposing greater control over distal joints than proximal joints. However, the reasons for this remain unclear, largely because it is not experimentally possible to directly manipulate either the noise or the control at each joint independently. Therefore, this study used a 2 degree-of-freedom torque driven arm model to determine how different combinations of noise and/or control independently applied at each joint affected the reaching accuracy and the total work required to make the movement. Signal-dependent noise was simultaneously and independently added to the shoulder and elbow torques to induce endpoint errors during planar reaching. Feedback control was then applied, independently and jointly, at each joint to reduce endpoint error due to the added neuromuscular noise. Movement direction and the inertia distribution along the arm were varied to quantify how these biomechanical variations affected the system performance. Endpoint error and total net work were computed as dependent measures. When each joint was independently subjected to noise in the absence of control, endpoint errors were more sensitive to distal (elbow) noise than to proximal (shoulder) noise for nearly all combinations of reaching direction and inertia ratio. The effects of distal noise on endpoint errors were more pronounced when inertia was distributed more toward the forearm. In contrast, the total net work decreased as mass was shifted to the upper arm for reaching movements in all directions. When noise was present at both joints and joint control was implemented, controlling the distal joint alone reduced endpoint errors more than controlling the proximal joint alone for nearly all combinations of reaching direction and inertia ratio. Applying control only at the distal joint was more effective at reducing endpoint errors when more of the mass was more proximally distributed. Likewise, controlling the distal joint alone required less total net work than controlling the proximal joint alone for nearly all combinations of reaching distance and inertia ratio. It is more efficient to reduce endpoint error and energetic cost by selectively applying control to reduce variability in the distal joint than the proximal joint. The reasons for this arise from the biomechanical configuration of the arm itself.

Agonistic and antagonistic interaction in speed/accuracy tradeoff: a delta-lognormal perspective

This paper reports the results of a model-based analysis of movements gathered in a 4×4 experimental design of speed/accuracy tradeoffs with variable target distances and width. Our study was performed on a large (120 participants) and varied sample (both genders, wide age range, various health conditions). The delta-lognormal equation was used for data modeling to investigate the interaction between the output of the agonist and the antagonist neuromuscular systems. Empirical observations show that the subjects must correlate more tightly the impulse commands sent to both neuromuscular systems in order to achieve good performances as the difficulty of the task increases whereas the correlation in the timing of the neuromuscular action co-varies with the size of the geometrical properties of the task. These new phenomena are discussed under the paradigm provided by the Kinematic Theory and new research hypotheses are proposed for further investigation of the speed/accuracy tradeoffs.

Precision control of an upright trunk posture in low back pain patients

BACKGROUND

Low back pain appears to be associated with impaired trunk postural control, which could be caused by proprioceptive deficits. We assessed control of trunk posture in conditions requiring high and low precision, with and without disturbance of proprioception by lumbar muscle vibration.

METHODS

Twenty a-specific low back pain patients and 13 healthy controls maintained a self-chosen upright trunk posture. Initial frontal and sagittal plane angles of an opto-electronic marker on the 12th thoracic spinous process defined the center of a target area on a monitor. Subjects were instructed to stay within that target and visual feedback was provided when they left the target. The precision demand was manipulated by changing target size. The standard deviation of trunk angle quantified precision and mean Euclidian distance to target center quantified accuracy. Ratios of antagonistic co-activation were calculated from trunk muscle electromyography recordings.

FINDINGS

With the small target, visual feedback was present intermittently and patients controlled their trunk as accurately and precisely as healthy controls. For the large target, subjects mostly stayed within the target, and patients were on average 0.18° (31%) less accurate than healthy controls (P=0.025), due to a larger postural drift. Lumbar muscle vibration deteriorated control over trunk posture in both groups and ratios of antagonistic co-activation did not differ between groups or conditions.

INTERPRETATION

These results indicate that the weighting of proprioceptive feedback from lumbar muscle spindles did not differ between groups and that low back pain patients were less able to detect low frequency drift in posture.