Age-related changes in multifinger synergies in accurate moment of force production tasks.

The purpose of this investigation was to document and quantify age-related differences in the coordination of fingers during a task that required production of an accurate time profile of the total moment of force by the four fingers of a hand. We hypothesized that elderly subjects would show a decreased ability to stabilize a time profile of the total moment of force, leading to larger indexes of moment variability compared with young subjects. The subjects followed a trapezoidal template on a computer screen by producing a time profile of the total moment of force while pressing down on force sensors with the four fingers of the right (dominant) hand. To quantify synergies, we used the framework of the uncontrolled manifold hypothesis. The elderly subjects produced larger total force, larger variance of both total force and total moment of force, and larger involvement of fingers that produced moment of force against the required moment direction (antagonist moment). This was particularly prominent during supination efforts. Young subjects showed covariation of commands to fingers across trials that stabilized the moment of total force (moment-stabilizing synergy), while elderly subjects failed to do so. Both subject groups showed similar indexes of covariation of commands to the fingers that stabilized the time profile of the total force. The lack of moment-stabilizing synergies may be causally related to the documented impairment of hand function with age.     

Evidence of muscle synergies during human grasping

Motor synergies have been investigated since the 1980s as a simplifying representation of motor control by the nervous system. This way of representing finger positional data is in particular useful to represent the kinematics of the human hand. Whereas, so far, the focus has been on kinematic synergies, that is common patterns in the motion of the hand and fingers, we hereby also investigate their force aspects, evaluated through surface electromyography (sEMG). We especially show that force-related motor synergies exist, i.e. that muscle activation during grasping, as described by the sEMG signal, can be grouped synergistically; that these synergies are largely comparable to one another across human subjects notwithstanding the disturbances and inaccuracies typical of sEMG; and that they are physiologically feasible representations of muscular activity during grasping. Potential applications of this work include force control of mechanical hands, especially when many degrees of freedom must be simultaneously controlled.     

Feed-forward control of a redundant motor system

We describe a model of feed-forward control of a redundant motor system and validate it using, as examples, tasks of multi-finger force production. The model assumes the existence of two input signals at an upper level of the control hierarchy, related and unrelated to a task variable. Knowledge of the Jacobian of the system is assumed at the level of generation of elemental variables (variables at the level of effectors). Variance at the level of elemental variables is considered as the sum of two components, related and unrelated to variability in the task variable. An index of stabilization of the task variable is similarly introduced as to how it was done in several studies using the framework of the uncontrolled manifold hypothesis. Several phenomena have been simulated including data point distributions corresponding to presence and absence of force-stabilizing synergies in two-finger tasks, changes in synergies with practice, and changes in synergy indices in preparation to a fast action. The model is discussed in comparison to other models of control of multi-element systems based on feedback processes. It shows that patterns of structured variability in the space of elemental variables can result from feed-forward processes. Relations of the model to the equilibrium-point hypothesis are also discussed.     

The Force Synergy of Human Digits in Static and Dynamic Cylindrical Grasps

This study explores the force synergy of human digits in both static and dynamic cylindrical grasping conditions. The patterns of digit force distribution, error compensation, and the relationships among digit forces are examined to quantify the synergetic patterns and coordination of multi-finger movements. This study recruited 24 healthy participants to perform cylindrical grasps using a glass simulator under normal grasping and one-finger restricted conditions. Parameters such as the grasping force, patterns of digit force distribution, and the force coefficient of variation are determined. Correlation coefficients and principal component analysis (PCA) are used to estimate the synergy strength under the dynamic grasping condition. Specific distribution patterns of digit forces are identified for various conditions. The compensation of adjacent fingers for the force in the normal direction of an absent finger agrees with the principle of error compensation. For digit forces in anti-gravity directions, the distribution patterns vary significantly by participant. The forces exerted by the thumb are closely related to those exerted by other fingers under all conditions. The index-middle and middle-ring finger pairs demonstrate a significant relationship. The PCA results show that the normal forces of digits are highly coordinated. This study reveals that normal force synergy exists under both static and dynamic cylindrical grasping conditions.     

Generalization in Adaptation to Stable and Unstable Dynamics

Humans skillfully manipulate objects and tools despite the inherent instability. In order to succeed at these tasks, the sensorimotor control system must build an internal representation of both the force and mechanical impedance. As it is not practical to either learn or store motor commands for every possible future action, the sensorimotor control system generalizes a control strategy for a range of movements based on learning performed over a set of movements. Here, we introduce a computational model for this learning and generalization, which specifies how to learn feedforward muscle activity in a function of the state space. Specifically, by incorporating co-activation as a function of error into the feedback command, we are able to derive an algorithm from a gradient descent minimization of motion error and effort, subject to maintaining a stability margin. This algorithm can be used to learn to coordinate any of a variety of motor primitives such as force fields, muscle synergies, physical models or artificial neural networks. This model for human learning and generalization is able to adapt to both stable and unstable dynamics, and provides a controller for generating efficient adaptive motor behavior in robots. Simulation results exhibit predictions consistent with all experiments on learning of novel dynamics requiring adaptation of force and impedance, and enable us to re-examine some of the previous interpretations of experiments on generalization.     

Coordinated force production in multi-finger tasks: finger interaction and neural network modeling

During maximal voluntary contraction (MVC) with several fingers, the following three phenomena are observed: (1) the total force produced by all the involved fingers is shared among the fingers in a specific manner (sharing); (2) the force produced by a given finger in a multi-finger task is smaller than the force generated by this finger in a single-finger task (force deficit); (3) the fingers that are not required to produce any force by instruction are involuntary activated (enslaving). We studied involuntary force production by individual fingers (enslaving effects, EE) during tasks when (an)other finger(s) of the hand generated maximal voluntary pressing force in isometric conditions. The subjects (n = 10) were instructed to press as hard as possible on the force sensors with one, two, three and four fingers acting in parallel in all possible combinations. The EE were (A) large, the slave fingers always producing a force ranging from 10.9% to 54.7% of the maximal force produced by the finger in the single-finger task; (B) nearly symmetrical; (C) larger for the neighboring fingers; and (D) non-additive. In most cases, the EE from two or three fingers were smaller than the EE from at least one finger (this phenomenon was coined occlusion). The occlusion cannot be explained only by anatomical musculo-tendinous connections. Therefore, neural factors contribute substantially to the EE. A neural network model that accounts for all the three effects has been developed. The model consists of three layers: the input layer that models a central neural drive; the hidden layer modeling transformation of the central drive into an input signal to the muscles serving several fingers simultaneously (multi-digit muscles); and the output layer representing finger force output. The output of the hidden layer is set inversely proportional to the number of fingers involved. In addition, direct connections between the input and output layers represent signals to the hand muscles serving individual fingers (uni-digit muscles). The network was validated using three different training sets. Single digit muscles contributed from 25% to 50% of the total finger force. The master matrix and the enslaving matrix were computed; they characterize the ability of a given finger to enslave other fingers and its ability to be enslaved. Overall, the neural network modeling suggests that no direct correspondence exists between neural command to an individual finger and finger force. To produce a desired finger force, a command sent to an intended finger should be scaled in accordance with the commands sent to the other fingers. Received: 17 October 1997 / Accepted in revised form: 12 May 1998     

Inverse dynamic investigation of voluntary trunk movements in weightlessness: a new microgravity-specific strategy

Present investigation faces the question of quantitative assessment of exchanged forces and torques at the restraints during whole body posture exercises in long-term microgravity. Inverse dynamic modelling and total angular momentum at the ankle joint were used in order to reconstruct movement dynamics at the restraining point, represented by the ankle joint. The hypothesis is that the minimisation of the torques at the interface point assumes a key role in movement planning in 0 g. This hypothesis would respond to an optimisation of muscles activity, a minimisation of energy expenditure and therefore an accurate control of body movement. Results show that the 0 g movement strategy adopted ensures that the integral of the net ankle moment between the beginning and the end of the movement is zero. This expected mechanical constraint is not satisfied when 0 g movement dynamics is simulated using terrestrial kinematics. This accounts for a significant imposed change of movement strategy. Particularly, the efficient compensation of the inertial effects of the segments in terms of total angular momentum at the ankle joint was evidenced. These results explain the exaggerated axial synergies, observed on kinematics and which moved centre of mass (CM) backward from its already backward initial positioning, as a tool for enhancing the compensation and achieving the desired minimisation of the torques exchanges at the restraints.     

Motor unit recruitment patterns during reflex compensation of muscle yield investigated by computer simulations

 An important function of the stretch reflex in the soleus muscle in the decerebrate cat preparation is to compensate for the tendency of muscle suddenly to yield during ramp increases in length. As the level of background (i.e. pre-stretch) force increases, there is a systematic change in the curvature of the force trajectory during this reflex compensation, from concave to convex with respect to increasing force. The hypothesis that this change in curvature was due to background force-dependent changes in the recruitment pattern of motor units was investigated with a combined computer simulation/ experimental technique. The simulation consisted of 20 model motor units for the soleus muscle, each based on a distributed moment muscle model. The timing of recruitment of the motor units was optimized to allow the simulation outputs to fit a set of experimental data records on the reflex response to stretch initiated at five different levels of pre-stretch force. The resulting recruitment patterns showed that a tendency for recruitment to be concentrated progressively in the early portion of the stretch as pre-stretch force increased could account for the changes in reflex force curvature. These results are consistent with the skewed distribution of intrinsic electrical thresholds of motoneurons, in which low-threshold units are much more frequent than high-threshold ones. Therefore the changes in recruitment pattern and reflex force curvature may be due primarily to the intrinsic properties of motoneurons. Received: 18 September 1995/Accepted in revised form: 21 May 1996     

Schema-based learning of adaptable and flexible prey-catching in anurans I. The basic architecture

A motor action often involves the coordination of several motor synergies and requires flexible adjustment of the ongoing execution based on feedback signals. To elucidate the neural mechanisms underlying the construction and selection of motor synergies, we study prey-capture in anurans. Experimental data demonstrate the intricate interaction between different motor synergies, including the interplay of their afferent feedback signals (Weerasuriya 1991; Anderson and Nishikawa 1996). Such data provide insights for the general issues concerning two-way information flow between sensory centers, motor circuits and periphery in motor coordination. We show how different afferent feedback signals about the status of the different components of the motor apparatus play a critical role in motor control as well as in learning. This paper, along with its companion paper, extend the model by Liaw et al. (1994) by integrating a number of different motor pattern generators, different types of afferent feedback, as well as the corresponding control structure within an adaptive framework we call Schema-Based Learning. We develop a model of the different MPGs involved in prey-catching as a vehicle to investigate the following questions: What are the characteristic features of the activity of a single muscle? How can these features be controlled by the premotor circuit? What are the strategies employed to generate and synchronize motor synergies? What is the role of afferent feedback in shaping the activity of a MPG? How can several MPGs share the same underlying circuitry and yet give rise to different motor patterns under different input conditions? In the companion paper we also extend the model by incorporating learning components that give rise to more flexible, adaptable and robust behaviors. To show these aspects we incorporate studies on experiments on lesions and the learning processes that allow the animal to recover its proper functioning     

Motor control theories improve biomechanical model of the hand for finger pressing tasks

BackgroundBiomechanical models are a useful tool to estimate tendon tensions. Unfortunately, in previous fingers' models, each finger acts independently from the others. This is contradictory with hand motor control theories which show that fingers are functionally linked in order to balance the wrist/forearm joint with minimal tendon tensions. (i.e. principle of minimization of the secondary moments). We propose to adapt a hand biomechanical model according to this principle by including the wrist joint. We will determine whether the finger tendon tensions changed with the wrist joint added to the model.MethodsTwo models have been tested: one considering fingers independently (model A) and one with the fingers mechanically linked by the inclusion of the wrist balance (model B). A single set of data, additional results from the literature and in-vivo values have been used to compare the results.ResultsModel A corroborates previous results in the literature. Contrast results were obtained with model B, especially for the Ring and Little fingers. Different tendon tensions were obtained, particularly, in finger extensor muscles critical to balance the wrist.DiscussionWe discuss the biomechanical results in accordance with the hand/finger motor control theories. It appears that the wrist joint balance is critical for finger tendon tension estimation. When including the wrist joint into finger models, the tendon tension estimations agree well with the minimization of secondary moments and the force deficit.     

Entrapment of adult fingers between window glass and seal entry of a motor vehicle side door: an experimental study for investigation of the force at the subjective pain threshold

In modern motor vehicles with automatic power windows, a potential hazard exists for jam events of fingers between the window glass and seal entry. This study determined entrapment forces acting on adult fingers at the subjective maximum pain threshold during entrapment in such windows. The length and the girth of the proximal and distal interphalangeal joints of the triphalangeal fingers of the right hands of 109 participants (60 men, 49 women) were measured; the diameter was calculated from girth, which was assumed to be circular. The automatic power window system of a motor vehicle side door was changed to a mechanical system. During entrapment the force distributed across the four proximal interphalangeal joints (PIPs), and separately on the proximal interphalangeal (iPIP) and then the distal interphalangeal (iDIP) joints of the index finger was measured using a customized force sensor. The maximum bearable entrapment force was 97.2 ± 51.8 N for the PIPs, 43.4 ± 19.9 N for the iPIP, and 36.9 ± 17.8 N for the iDIP. The positive correlation between finger diameter and maximum entrapment force was significant. Particularly with regard to the risk to children's fingers, the 100 N statutory boundary value for closing force of electronic power windows should be reduced.     

Compensation for inertial and gravity effects in a moving force platform

Force plates for human movement analysis provide accurate measurements when mounted rigidly on an inertial reference frame. Large measurement errors occur, however, when the force plate is accelerated, or tilted relative to gravity. This prohibits the use of force plates in human perturbation studies with controlled surface movements, or in conditions where the foundation is moving or not sufficiently rigid. Here we present a linear model to predict the inertial and gravitational artifacts using accelerometer signals. The model is first calibrated with data collected from random movements of the unloaded system and then used to compensate for the errors in another trial. The method was tested experimentally on an instrumented force treadmill capable of dynamic mediolateral translation and sagittal pitch. The compensation was evaluated in five experimental conditions, including platform motions induced by actuators, by motor vibration, and by human ground reaction forces. In the test that included all sources of platform motion, the root-mean-square (RMS) errors were 39.0 N and 15.3 N m in force and moment, before compensation, and 1.6 N and 1.1 N m, after compensation. A sensitivity analysis was performed to determine the effect on estimating joint moments during human gait. Joint moment errors in hip, knee, and ankle were initially 53.80 N m, 32.69 N m, and 19.10 N m, and reduced to 1.67 N m, 1.37 N m, and 1.13 N m with our method. It was concluded that the compensation method can reduce the inertial and gravitational artifacts to an acceptable level for human gait analysis.     

Understanding finger coordination through analysis of the structure of force variability

 Most common motor acts involve highly redundant effector systems. Understanding how such systems are controlled by the nervous system is a long-standing scientific challenge. Most proposals for solving this problem are based on the assumption that a particular solution, which optimizes additional constraints, is selected by the nervous system out of the many possible solutions. This study attempts to address this question in the context of coordinating individual finger forces to produce a controlled total force oscillation between 5% and 35% of each subject's maximum force of voluntary contraction, under two different combinations of four fingers. The structure of variability of individual finger forces was evaluated with respect to hypotheses that, at each instance in time, subjects attempt to: (1) stabilize the value of total force and (2) stabilize the total moment created by the fingers about the long axis passing through the forearm and midline of the hand. The results provide evidence that a range of goal-equivalent finger force combinations is generated to stabilize the values of total force and the total moment. The control of total force was specified explicitly by the task. However, it was stabilized only near the time of peak force. In contrast, the total moment was stabilized throughout most of the force cycle. The results lead to the suggestion that successful task performance is achieved, not by selecting a single optimal solution, but by discovering an appropriate control law that selectively stabilizes certain combinations of degrees of freedom relevant to the task while releasing from control other combinations. Received: 2 February 2001 / Accepted in revised form: 21 June 2001     

Can altered muscle synergies control unimpaired gait?

Recent studies have postulated that the human motor control system recruits groups of muscles through low-dimensional motor commands, or muscle synergies. This scheme simplifies the neural control problem associated with the high-dimensional structure of the neuromuscular system. Several lines of evidence have suggested that neurological injuries, such as stroke or cerebral palsy, may reduce the dimensions that are available to the motor control system, and these altered dimensions or synergies are thought to contribute to impaired walking patterns. However, no study has investigated whether impaired low-dimensional control spaces necessarily lead to impaired walking patterns. In this study, using a two-dimensional model of walking, we developed a synergy-based control framework that can simulate the dynamics of walking. The simulation analysis showed that a synergy-based control scheme can produce well-coordinated movements of walking matching unimpaired gait. However, when the dimensions available to the controller were reduced, the simplified emergent pattern deviated from unimpaired gait. A system with two synergies, similar to those seen after neurological injury, could not produce an unimpaired walking pattern. These findings provide further evidence that altered muscle synergies can contribute to impaired gait patterns and may need to be directly addressed to improve gait after neurological injury.     

Analogous mechanisms compensate for neural delays in the sensory and the motor pathways: evidence from motor flash-lag

Motor behaviors require animals to coordinate neural activity across different areas within their motor system. In particular, the significant processing delays within the motor system must somehow be compensated for. Internal models of the motor system, in particular the forward model, have emerged as important potential mechanisms for compensation. For motor responses directed at moving visual objects, there is, additionally, a problem of delays within the sensory pathways carrying crucial position information. The visual phenomenon known as the flash-lag effect has led to a motion-extrapolation model for compensation of sensory delays. In the flash-lag effect, observers see a flashed item colocalized with a moving item as lagging behind the moving item. Here, we explore the possibility that the internal forward model and the motion-extrapolation model are analogous mechanisms compensating for neural delays in the motor and the visual system, respectively. In total darkness, observers moved their right hand gripping a rod while a visual flash was presented at various positions in relation to the rod. When the flash was aligned with the rod, observers perceived it in a position lagging behind the instantaneous felt position of the invisible rod. These results suggest that compensation of neural delays for time-varying motor behavior parallels compensation of delays for time-varying visual stimulation.     

Modular neuromuscular control of human locomotion by central pattern generator

The central pattern generators (CPG) in the spinal cord are thought to be responsible for producing the rhythmic motor patterns during rhythmic activities. For locomotor tasks, this involves much complexity, due to a redundant system of muscle actuators with a large number of highly nonlinear muscles. This study proposes a reduced neural control strategy for the CPG, based on modular organization of the co-active muscles, i.e., muscle synergies. Four synergies were extracted from the EMG data of the major leg muscles of two subjects, during two gait trials each, using non-negative matrix factorization algorithm. A Matsuoka׳s four-neuron CPG model with mutual inhibition, was utilized to generate the rhythmic activation patterns of the muscle synergies, using the hip flexion angle and foot contact force information from the sensory afferents as inputs. The model parameters were tuned using the experimental data of one gait trial, which resulted in a good fitting accuracy (RMSEs between 0.0491 and 0.1399) between the simulation and experimental synergy activations. The model׳s performance was then assessed by comparing its predictions for the activation patterns of the individual leg muscles during locomotion with the relevant EMG data. Results indicated that the characteristic features of the complex activation patterns of the muscles were well reproduced by the model for different gait trials and subjects. In general, the CPG- and muscle synergy-based model was promising in view of its simple architecture, yet extensive potentials for neuromuscular control, e.g., resolving redundancies, distributed and fast control, and modulation of locomotion by simple control signals.     

Challenges and new approaches to proving the existence of muscle synergies of neural origin

Muscle coordination studies repeatedly show low-dimensionality of muscle activations for a wide variety of motor tasks. The basis vectors of this low-dimensional subspace, termed muscle synergies, are hypothesized to reflect neurally-established functional muscle groupings that simplify body control. However, the muscle synergy hypothesis has been notoriously difficult to prove or falsify. We use cadaveric experiments and computational models to perform a crucial thought experiment and develop an alternative explanation of how muscle synergies could be observed without the nervous system having controlled muscles in groups. We first show that the biomechanics of the limb constrains musculotendon length changes to a low-dimensional subspace across all possible movement directions. We then show that a modest assumption--that each muscle is independently instructed to resist length change--leads to the result that electromyographic (EMG) synergies will arise without the need to conclude that they are a product of neural coupling among muscles. Finally, we show that there are dimensionality-reducing constraints in the isometric production of force in a variety of directions, but that these constraints are more easily controlled for, suggesting new experimental directions. These counter-examples to current thinking clearly show how experimenters could adequately control for the constraints described here when designing experiments to test for muscle synergies--but, to the best of our knowledge, this has not yet been done.     

Functional muscle synergies constrain force production during postural tasks

We recently demonstrated that a set of five functional muscle synergies were sufficient to characterize both hindlimb muscle activity and active forces during automatic postural responses in cats standing at multiple postural configurations. This characterization depended critically upon the assumption that the endpoint force vector (synergy force vector) produced by the activation of each muscle synergy rotated with the limb axis as the hindlimb posture varied in the sagittal plane. Here, we used a detailed, 3D static model of the hindlimb to confirm that this assumption is biomechanically plausible: as we varied the model posture, simulated synergy force vectors rotated monotonically with the limb axis in the parasagittal plane (r2=0.94+/-0.08). We then tested whether a neural strategy of using these five functional muscle synergies provides the same force-generating capability as controlling each of the 31 muscles individually. We compared feasible force sets (FFSs) from the model with and without a muscle synergy organization. FFS volumes were significantly reduced with the muscle synergy organization (F=1556.01, p<0.01), and as posture varied, the synergy-limited FFSs changed in shape, consistent with changes in experimentally measured active forces. In contrast, nominal FFS shapes were invariant with posture, reinforcing prior findings that postural forces cannot be predicted by hindlimb biomechanics alone. We propose that an internal model for postural force generation may coordinate functional muscle synergies that are invariant in intrinsic limb coordinates, and this reduced-dimension control scheme reduces the set of forces available for postural control.     

Dissociating Variability and Effort as Determinants of Coordination

When coordinating movements, the nervous system often has to decide how to distribute work across a number of redundant effectors. Here, we show that humans solve this problem by trying to minimize both the variability of motor output and the effort involved. In previous studies that investigated the temporal shape of movements, these two selective pressures, despite having very different theoretical implications, could not be distinguished; because noise in the motor system increases with the motor commands, minimization of effort or variability leads to very similar predictions. When multiple effectors with different noise and effort characteristics have to be combined, however, these two cost terms can be dissociated. Here, we measure the importance of variability and effort in coordination by studying how humans share force production between two fingers. To capture variability, we identified the coefficient of variation of the index and little fingers. For effort, we used the sum of squared forces and the sum of squared forces normalized by the maximum strength of each effector. These terms were then used to predict the optimal force distribution for a task in which participants had to produce a target total force of 4–16 N, by pressing onto two isometric transducers using different combinations of fingers. By comparing the predicted distribution across fingers to the actual distribution chosen by participants, we were able to estimate the relative importance of variability and effort of 17, with the unnormalized effort being most important. Our results indicate that the nervous system uses multi-effector redundancy to minimize both the variability of the produced output and effort, although effort costs clearly outweighed variability costs.     

Can co-activation reduce kinematic variability? A simulation study

Impedance modulation has been suggested as a means to suppress the effects of internal ‘noise’ on movement kinematics. We investigated this hypothesis in a neuro-musculo-skeletal model. A prerequisite is that the muscle model produces realistic force variability. We found that standard Hill-type models do not predict realistic force variability in response to variability in stimulation. In contrast, a combined motor-unit pool model and a pool of parallel Hill-type motor units did produce realistic force variability as a function of target force, largely independent of how the force was transduced to the tendon. To test the main hypothesis, two versions of the latter model were simulated as an antagonistic muscle pair, controlling the position of a frictionless hinge joint, with a distal segment having realistic inertia relative to the muscle strength. Increasing the impedance through co-activation resulted in less kinematic variability, except for the lowest levels of co-activation. Model behavior in this region was affected by the noise amplitude and the inertial properties of the model. Our simulations support the idea that muscular co-activation is in principle an effective strategy to meet accuracy demands.