Brownian processes in human motor control support descending neural velocity commands.

The motor neuroscience literature suggests that the central nervous system may encode some motor commands in terms of velocity. In this work, we tackle the question: what consequences would velocity commands produce at the behavioral level? Considering the ubiquitous presence of noise in the neuromusculoskeletal system, we predict that velocity commands affected by stationary noise would produce "random walks", also known as Brownian processes, in position. Brownian motions are distinctively characterized by a linearly growing variance and a power spectral density that declines in inverse proportion to frequency. This work first shows that these Brownian processes are indeed observed in unbounded motion tasks e.g., rotating a crank. We further predict that such growing variance would still be present, but bounded, in tasks requiring a constant posture e.g., maintaining a static hand position or quietly standing. This hypothesis was also confirmed by experimental observations. A series of descriptive models are investigated to justify the observed behavior. Interestingly, one of the models capable of accounting for all the experimental results must feature forward-path velocity commands corrupted by stationary noise. The results of this work provide behavioral support for the hypothesis that humans plan the motion components of their actions in terms of velocity.

Dynamic primitives in constrained action: systematic changes in the zero-force trajectory.

Humans substantially outperform robotic systems in tasks that require physical interaction, despite seemingly inferior muscle bandwidth and slow neural information transmission. The control strategies that enable this performance remain poorly understood. To bridge that gap, this study examined kinematically constrained motion as an intermediate step between the widely studied unconstrained motions and sparsely studied physical interactions. Subjects turned a horizontal planar crank in two directions (clockwise and counterclockwise) at three constant target speeds (fast, medium, and very slow) as instructed via visual display. With the hand constrained to move in a circle, nonzero forces against the constraint were measured. This experiment exposed two observations that could not result from mechanics alone but may be attributed to neural control composed of dynamic primitives. A plausible mathematical model of interactive dynamics (mechanical impedance) was assumed and used to "subtract" peripheral neuromechanics. This method revealed a summary of the underlying neural control in terms of motion, a zero-force trajectory. The estimated zero-force trajectories were approximately elliptical and their orientation differed significantly with turning direction; that is consistent with control using oscillations to generate an elliptical zero-force trajectory. However, for periods longer than 2-5 s, motion can no longer be perceived or executed as periodic. Instead, it decomposes into a sequence of submovements, manifesting as increased variability. These quantifiable performance limitations support the hypothesis that humans simplify this constrained-motion task by exploiting at least three primitive dynamic actions: oscillations, submovements, and mechanical impedance.NEW & NOTEWORTHY Control using primitive dynamic actions may explain why human performance is superior to robots despite seemingly inferior "wetware"; however, this also implies limitations. For a crank-turning task, this work quantified two such informative limitations. Force was exerted even though it produced no mechanical work, the underlying zero-force trajectory was roughly elliptical, and its orientation differed with turning direction, evidence of oscillatory control. At slow speeds, speed variability increased substantially, indicating intermittent control via submovements.

The Study of Complex Manipulation via Kinematic Hand Synergies: The Effects of Data Pre-Processing.

The study of kinematic hand synergies through matrix decomposition techniques, such as singular value decomposition, supports the theory that humans might control a subspace of predefined motions during manipulation tasks. These subspaces are often referred to as synergies. However, different data pre-processing methods lead to quantitatively different conclusions about these synergies. In this work, we shed light on the role of data pre-processing on the study of hand synergies by analyzing both numerical simulation and real kinematic data from a complex manipulation task, i.e., piano playing. The results obtained suggest that centering the data, by removing the mean, appears to be the most appropriate preprocessing technique for studying kinematic hand synergies.

Learning to manipulate a whip with simple primitive actions - A simulation study.

This simulation study investigated whether a 4-degrees-of-freedom (DOF) arm could strike a target with a 50-DOF whip using a motion profile similar to discrete human movements. The interactive dynamics of the multi-joint arm was modeled as a constant joint-space mechanical impedance, with values derived from experimental measurement. Targets at various locations could be hit with a single maximally smooth motion in joint-space coordinates. The arm movements that hit the targets were identified with fewer than 250 iterations. The optimal actions were essentially planar arm motions in extrinsic task-space coordinates, predominantly oriented along the most compliant direction of both task-space and joint-space mechanical impedances. Of the optimal movement parameters, striking a target was most sensitive to movement duration. This result suggests that the elementary actions observed in human motor behavior may support efficient motor control in interaction with a dynamically complex object.

Role of path information in visual perception of joint stiffness.

Humans have an astonishing ability to extract hidden information from the movement of others. In previous work, subjects observed the motion of a simulated stick-figure, two-link planar arm and estimated its stiffness. Fundamentally, stiffness is the relation between force and displacement. Given that subjects were unable to physically interact with the simulated arm, they were forced to make their estimates solely based on observed kinematic information. Remarkably, subjects were able to correctly correlate their stiffness estimates with changes in the simulated stiffness, despite the lack of force information. We hypothesized that subjects were only able to do this because the controller used to produce the simulated arm's movement, composed of oscillatory motions driving mechanical impedances, resembled the controller humans use to produce their own movement. However, it is still unknown what motion features subjects used to estimate stiffness. Human motion exhibits systematic velocity-curvature patterns, and it has previously been shown that these patterns play an important role in perceiving and interpreting motion. Thus, we hypothesized that manipulating the velocity profile should affect subjects' ability to estimate stiffness. To test this, we changed the velocity profile of the simulated two-link planar arm while keeping the simulated joint paths the same. Even with manipulated velocity signals, subjects were still able to estimate changes in simulated joint stiffness. However, when subjects were shown the same simulated path with different velocity profiles, they perceived motions that followed a veridical velocity profile to be less stiff than that of a non-veridical profile. These results suggest that path information (displacement) predominates over temporal information (velocity) when humans use visual observation to estimate stiffness.

Motor control beyond reach-how humans hit a target with a whip.

Humans are strikingly adept at manipulating complex objects, from tying shoelaces to cracking a bullwhip. These motor skills have highly nonlinear interactive dynamics that defy reduction into parts. Yet, despite advances in data recording and processing, experiments in motor neuroscience still prioritize experimental reduction over realistic complexity. This study embraced the fully unconstrained behaviour of hitting a target with a 1.6-m bullwhip, both in rhythmic and discrete fashion. Adopting an object-centered approach to test the hypothesis that skilled movement simplifies the whip dynamics, the whip's evolution was characterized in relation to performance error and hand speed. Despite widely differing individual strategies, both discrete and rhythmic styles featured a cascade-like unfolding of the whip. Whip extension and orientation at peak hand speed predicted performance error, at least in the rhythmic style, suggesting that humans accomplished the task by setting initial conditions. These insights may inform further studies on human and robot control of complex objects.

Mechanical effects of canes on standing posture: beyond perceptual information.

BACKGROUND: Numerous studies showed that postural balance improves through light touch on a stable surface highlighting the importance of haptic information, seemingly downplaying the mechanical contributions of the support. The present study examined the mechanical effects of canes for assisting balance in healthy individuals challenged by standing on a beam. METHODS: Sixteen participants supported themselves with two canes, one in each hand, and applied minimal, preferred, or maximum force onto the canes. They positioned the canes in the frontal plane or in a tripod configuration. Statistical analysis used a linear mixed model to evaluate the effects on the center of pressure and the center of mass. RESULTS: The canes significantly reduced the variability of the center of pressure and the center of mass to the same level as when standing on the ground. Increasing the exerted force beyond the preferred level yielded no further benefits, although in the preferred force condition, participants exploited the altered mechanics by resting their arms on the canes. The tripod configuration allowed for larger variability of the center of pressure in the task-irrelevant anterior-posterior dimension. High forces had a destabilizing effect on the canes: the displacement of the hand on the cane handle increased with the force. CONCLUSIONS: Given this static instability, these results show that using canes can provide not only mechanical benefits but also challenges. From a control perspective, effort can be reduced by resting the arms on the canes and by channeling noise in the task-irrelevant dimensions. However, larger forces exerted onto the canes can also have destabilizing effects and the instability of the canes needs to be counteracted, possibly by arm and shoulder stiffness. Insights into the variety of mechanical effects is important for the design of canes and the instructions of how to use them.

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.

Gait Entrainment to Torque Pulses From a Hip Exoskeleton Robot.

Robot-aided locomotor rehabilitation has proven challenging. To facilitate progress, it is important to first understand the neuro-mechanical dynamics and control of unimpaired human locomotion. Our previous studies found that human gait entrained to periodic torque pulses at the ankle when the pulse period was close to preferred stride duration. Moreover, synchronized gait exhibited a constant phase relation with the pulses so that the robot provided mechanical assistance. To test the generality of mechanical gait entrainment, this study characterized unimpaired human subjects' responses to periodic torque pulses during overground walking. The intervention was applied by a hip exoskeleton robot, Samsung GEMS-H. Gait entrainment was assessed based on the time-course of the phase at which torque pulses occurred within each stride. Experiments were conducted for two consecutive days to evaluate whether the second day elicited more entrainment. Whether entrainment was affected by the difference between pulse period and preferred stride duration was also assessed. Results indicated that the intervention evoked gait entrainment that occurred more often when the period of perturbation was closer to subjects' preferred stride duration, but the difference between consecutive days was insignificant. Entrainment was accompanied by convergence of pulse phase to a similar value across all conditions, where the robot maximized mechanical assistance. Clear evidence of motor adaptation indicated the potential of the intervention for rehabilitation. This study quantified important aspects of the nonlinear neuro-mechanical dynamics underlying unimpaired human walking, which will inform the development of effective approaches to robot-aided locomotor rehabilitation, exploiting natural dynamics in a minimally-encumbering way.

Preparing to move: Setting initial conditions to simplify interactions with complex objects.

Humans dexterously interact with a variety of objects, including those with complex internal dynamics. Even in the simple action of carrying a cup of coffee, the hand not only applies a force to the cup, but also indirectly to the liquid, which elicits complex reaction forces back on the hand. Due to underactuation and nonlinearity, the object's dynamic response to an action sensitively depends on its initial state and can display unpredictable, even chaotic behavior. With the overarching hypothesis that subjects strive for predictable object-hand interactions, this study examined how subjects explored and prepared the dynamics of an object for subsequent execution of the target task. We specifically hypothesized that subjects find initial conditions that shorten the transients prior to reaching a stable and predictable steady state. Reaching a predictable steady state is desirable as it may reduce the need for online error corrections and facilitate feed forward control. Alternative hypotheses were that subjects seek to reduce effort, increase smoothness, and reduce risk of failure. Motivated by the task of 'carrying a cup of coffee', a simplified cup-and-ball model was implemented in a virtual environment. Human subjects interacted with this virtual object via a robotic manipulandum that provided force feedback. Subjects were encouraged to first explore and prepare the cup-and-ball before initiating a rhythmic movement at a specified frequency between two targets without losing the ball. Consistent with the hypotheses, subjects increased the predictability of interaction forces between hand and object and converged to a set of initial conditions followed by significantly decreased transients. The three alternative hypotheses were not supported. Surprisingly, the subjects' strategy was more effortful and less smooth, unlike the observed behavior in simple reaching movements. Inverse dynamics of the cup-and-ball system and forward simulations with an impedance controller successfully described subjects' behavior. The initial conditions chosen by the subjects in the experiment matched those that produced the most predictable interactions in simulation. These results present first support for the hypothesis that humans prepare the object to minimize transients and increase stability and, overall, the predictability of hand-object interactions.

Applying Hip Stiffness With an Exoskeleton to Compensate Gait Kinematics.

Neurological disorders and aging induce impaired gait kinematics. Despite recent advances, effective methods using lower-limb exoskeleton robots to restore gait kinematics are as yet limited. In this study, applying virtual stiffness using a hip exoskeleton was investigated as a possible method to guide users to change their gait kinematics. With a view to applications in locomotor rehabilitation, either to provide assistance or promote recovery, this study assessed whether imposed stiffness induced changes in the gait pattern during walking; and whether any changes persisted upon removal of the intervention, which would indicate changes in central neuro-motor control. Both positive and negative stiffness induced immediate and persistent changes of gait kinematics. However, the results showed little behavioral evidence of persistent changes in neuro-motor control, not even short-lived aftereffects. In addition, stride duration was little affected, suggesting that at least two dissociable layers exist in the neuro-motor control of human walking. The lack of neuro-motor adaptation suggests that, within broad limits, the central nervous system is surprisingly indifferent to the details of lower limb kinematics. The lack of neuro-motor adaptation also suggests that alternative methods may be required to implement a therapeutic technology to promote recovery. However, the immediate, significant, and reproducible changes in kinematics suggest that applying hip stiffness with an exoskeleton may be an effective assistive technology for compensation.

Frequency-dependent force direction elucidates neural control of balance.

BACKGROUND: Maintaining upright posture is an unstable task that requires sophisticated neuro-muscular control. Humans use foot-ground interaction forces, characterized by point of application, magnitude, and direction to manage body accelerations. When analyzing the directions of the ground reaction forces of standing humans in the frequency domain, previous work found a consistent pattern in different frequency bands. To test whether this frequency-dependent behavior provided a distinctive signature of neural control or was a necessary consequence of biomechanics, this study simulated quiet standing and compared the results with human subject data. METHODS: Aiming to develop the simplest competent and neuromechanically justifiable dynamic model that could account for the pattern observed across multiple subjects, we first explored the minimum number of degrees of freedom required for the model. Then, we applied a well-established optimal control method that was parameterized to maximize physiologically-relevant insight to stabilize the balancing model. RESULTS: If a standing human was modeled as a single inverted pendulum, no controller could reproduce the experimentally observed pattern. The simplest competent model that approximated a standing human was a double inverted pendulum with torque-actuated ankle and hip joints. A range of controller parameters could stabilize this model and reproduce the general trend observed in experimental data; this result seems to indicate a biomechanical constraint and not a consequence of control. However, details of the frequency-dependent pattern varied substantially across tested control parameter values. The set of parameters that best reproduced the human experimental results suggests that the control strategy employed by human subjects to maintain quiet standing was best described by minimal control effort with an emphasis on ankle torque. CONCLUSIONS: The findings suggest that the frequency-dependent pattern of ground reaction forces observed in quiet standing conveys quantitative information about human control strategies. This study's method might be extended to investigate human neural control strategies in different contexts of balance, such as with an assistive device or in neurologically impaired subjects.

Identifying human postural dynamics and control from unperturbed balance.

BACKGROUND: Upright standing requires control of an inherently unstable multi-joint human body within a small base of support, despite biological motor and / or sensory noise which challenge balance. Without applying perturbations, system identification methods have been regarded as inadequate, because the relevant internal biological noise processes are not accessible to direct measurement. As a result, unperturbed balance studies have been limited to investigation of behavioral patterns rather than possible underlying control strategies. METHODS: In this paper, we present a mathemathically rigorous system identification method that is applicable to study the dynamics and control of unperturbed balance. The method is derived from autocorrelation matrices with non-zero time lags and identifies the system matrix of a discrete-time dynamic system in the presence of unknown noise processes, without requiring any information about the strength of the noise. RESULTS: Unlike reasonable 'least-squares' approaches, the performance of the new method is consistent across a range of different combinations of internal and measurement noise strengths, even when measurement noise is substantial. We present a numerical example of a model that simulates human upright balancing and show that its dynamics can be identified accurately. With a biomechanically reasonable choice of state and input variables, a state feedback controller can also be identified. CONCLUSIONS: This study provides a new method to correctly identify the dynamics of human standing without the need for known external perturbations. The method was numerically validated using simulation that included realistic features of human balance. This method avoids potential issues of adaptation or possible reflex responses evoked by external perturbations, and does not require expensive in-lab, high-precision measurement equipment. It may eventually enable diagnosis and treatment of individuals with impaired balance, and the development of safe and effective assistive and / or rehabilitative technologies.

Head-Mounted Display-Based Therapies for Adults Post-Stroke: A Systematic Review and Meta-Analysis.

Immersive virtual reality techniques have been applied to the rehabilitation of patients after stroke, but evidence of its clinical effectiveness is scarce. The present review aims to find studies that evaluate the effects of immersive virtual reality (VR) therapies intended for motor function rehabilitation compared to conventional rehabilitation in people after stroke and make recommendations for future studies. Data from different databases were searched from inception until October 2020. Studies that investigated the effects of immersive VR interventions on post-stroke adult subjects via a head-mounted display (HMD) were included. These studies included a control group that received conventional therapy or another non-immersive VR intervention. The studies reported statistical data for the groups involved in at least the posttest as well as relevant outcomes measuring functional or motor recovery of either lower or upper limbs. Most of the studies found significant improvements in some outcomes after the intervention in favor of the virtual rehabilitation group. Although evidence is limited, immersive VR therapies constitute an interesting tool to improve motor learning when used in conjunction with traditional rehabilitation therapies, providing a non-pharmacological therapeutic pathway for people after stroke.

Torque Estimation Using Neural Drive for a Concentric Contraction.

The scope and relevance of wearable robotics spans across a number of research fields with a variety of applications. A challenge across these research areas is improving user-interface control. One established approach is using neural control interfaces derived from surface electromyography (sEMG). Although there has been some success with sEMG controlled prosthetics, the coarse nature of traditional sEMG processing has limited the development of fully functional prosthetics and wearable robotics. To solve this problem, blind source separation (BSS) techniques have been implemented to extract the user's movement intent from high-density sEMG (HDsEMG) measurements; however, current methods have only been well validated during static, low-level muscle contractions, and it is unclear how they will perform during movement. In this paper we present a neural drive based method for predicting output torque during a constant force, concentric contraction. This was achieved by modifying an existing HDsEMG decomposition algorithm to decompose 1 sec. overlapping windows. The neural drive profile was computed using both rate coding and kernel smoothing. Neither rate coding nor kernel smoothing performed as well as HDsEMG amplitude estimation, indicating that there are still significant limitations in adapting current methods to decompose dynamic contractions, and that sEMG amplitude estimation methods still remain highly reliable estimators.

Separating neural influences from peripheral mechanics: the speed-curvature relation in mechanically constrained actions.

While the study of unconstrained movements has revealed important features of neural control, generalizing those insights to more sophisticated object manipulation is challenging. Humans excel at physical interaction with objects, even when those objects introduce complex dynamics and kinematic constraints. This study examined humans turning a horizontal planar crank (radius 10.29 cm) at their preferred and three instructed speeds (with visual feedback), both in clockwise and counterclockwise directions. To explore the role of neuromechanical dynamics, the instructed speeds covered a wide range: fast (near the limits of performance), medium (near preferred speed), and very slow (rendering dynamic effects negligible). Because kinematically constrained movements involve significant physical interaction, disentangling neural control from the influences of biomechanics presents a challenge. To address it, we modeled the interactive dynamics to "subtract off" peripheral biomechanics from observed force and kinematic data, thereby estimating aspects of underlying neural action that may be expressed in terms of motion. We demonstrate the value of this method: remarkably, an approximately elliptical path emerged, and speed minima coincided with curvature maxima, similar to what is seen in unconstrained movements, even though the hand moved at nearly constant speed along a constant-curvature path. These findings suggest that the neural controller takes advantage of peripheral biomechanics to simplify physical interaction. As a result, patterns seen in unconstrained movements persist even when physical interaction prevents their expression in hand kinematics. The reemergence of a speed-curvature relation indicates that it is due, at least in part, to neural processes that emphasize smoothness and predictability.NEW & NOTEWORTHY Physically interacting with kinematic constraints is commonplace in everyday actions. We report a study of humans turning a crank, a circular constraint that imposes constant hand path curvature and hence should suppress variations of hand speed due to the power-law speed-curvature relation widely reported for unconstrained motions. Remarkably, we found that, when peripheral biomechanical factors are removed, a speed-curvature relation reemerges, indicating that it is, at least in part, of neural origin.

Visual perception of joint stiffness from multijoint motion.

Humans have an astonishing ability to extract hidden information from the movements of others. For example, even with limited kinematic information, humans can distinguish between biological and nonbiological motion, identify the age and gender of a human demonstrator, and recognize what action a human demonstrator is performing. It is unknown, however, whether they can also estimate hidden mechanical properties of another's limbs simply by observing their motions. Strictly speaking, identifying an object's mechanical properties, such as stiffness, requires contact. With only motion information, unambiguous measurements of stiffness are fundamentally impossible, since the same limb motion can be generated with an infinite number of stiffness values. However, we show that humans can readily estimate the stiffness of a simulated limb from its motion. In three experiments, we found that participants linearly increased their rating of arm stiffness as joint stiffness parameters in the arm controller increased. This was remarkable since there was no physical contact with the simulated limb. Moreover, participants had no explicit knowledge of how the simulated arm was controlled. To successfully map nontrivial changes in multijoint motion to changes in arm stiffness, participants likely drew on prior knowledge of human neuromotor control. Having an internal representation consistent with the behavior of the controller used to drive the simulated arm implies that this control policy competently captures key features of veridical biological control. Finding that humans can extract latent features of neuromotor control from kinematics also provides new insight into how humans interpret the motor actions of others. NEW & NOTEWORTHY Humans can visually perceive another's overt motion, but it is unknown whether they can also perceive the hidden dynamic properties of another's limbs from their motions. Here, we show that humans can correctly infer changes in limb stiffness from nontrivial changes in multijoint limb motion without force information or explicit knowledge of the underlying limb controller. Our findings suggest that humans presume others control motor behavior in such a way that limb stiffness influences motion.

Stability and predictability in human control of complex objects.

Previous research on movement control suggested that humans exploit stability to reduce vulnerability to internal noise and external perturbations. For interactions with complex objects, predictive control based on an internal model of body and environment is needed to preempt perturbations and instabilities due to delays. We hypothesize that stability can serve as means to render the complex dynamics of the body and the task more predictable and thereby simplify control. However, the assessment of stability in complex interactions with nonlinear and underactuated objects is challenging, as for existent stability analyses the system needs to be close to a (known) attractor. After reviewing existing methods for stability analysis of human movement, we argue that contraction theory provides a suitable approach to quantify stability or convergence in complex transient behaviors. To test its usefulness, we examined the task of carrying a cup of coffee, an object with internal degrees of freedom. A simplified model of the task, a cart with a suspended pendulum, was implemented in a virtual environment to study human control strategies. The experimental task was to transport this cart-and-pendulum on a horizontal line from rest to a target position as fast as possible. Each block of trials presented a visible perturbation, which either could be in the direction of motion or opposite to it. To test the hypothesis that humans exploit stability to overcome perturbations, the dynamic model of the free, unforced system was analyzed using contraction theory. A contraction metric was obtained by numerically solving a partial differential equation, and the contraction regions with respect to that metric were computed. Experimental results showed that subjects indeed moved through the contraction regions of the free, unforced system. This strategy attenuated the perturbations, obviated error corrections, and made the dynamics more predictable. The advantages and shortcomings of contraction analysis are discussed in the context of other stability analyses.

Velocity-curvature patterns limit human-robot physical interaction.

Physical human-robot collaboration is becoming more common, both in industrial and service robotics. Cooperative execution of a task requires intuitive and efficient interaction between both actors. For humans, this means being able to predict and adapt to robot movements. Given that natural human movement exhibits several robust features, we examined whether human-robot physical interaction is facilitated when these features are considered in robot control. The present study investigated how humans adapt to biological and non-biological velocity patterns in robot movements. Participants held the end-effector of a robot that traced an elliptic path with either biological (two-thirds power law) or non-biological velocity profiles. Participants were instructed to minimize the force applied on the robot end-effector. Results showed that the applied force was significantly lower when the robot moved with a biological velocity pattern. With extensive practice and enhanced feedback, participants were able to decrease their force when following a non-biological velocity pattern, but never reached forces below those obtained with the 2/3 power law profile. These results suggest that some robust features observed in natural human movements are also a strong preference in guided movements. Therefore, such features should be considered in human-robot physical collaboration.