Conclusions on motor control depend on the type of model used to represent the periphery

Within the field of motor control, there is no consensus on which kinematic and kinetic aspects of movements are planned or controlled. Perturbing goal-directed movements is a frequently used tool to answer this question. To be able to draw conclusions about motor control from kinematic responses to perturbations, a model of the periphery (i.e., the skeleton, muscle-tendon complexes, and spinal reflex circuitry) is required. The purpose of the present study was to determine to what extent such conclusions depend on the level of simplification with which the dynamical properties of the periphery are modeled. For this purpose, we simulated fast goal-directed single-joint movement with four existing types of models. We tested how three types of perturbations affected movement trajectory if motor commands remained unchanged. We found that the four types of models of the periphery showed different robustness to the perturbations, leading to different predictions on how accurate motor commands need to be, i.e., how accurate the knowledge of external conditions needs to be. This means that when interpreting kinematic responses obtained in perturbation experiments the level of error correction attributed to adaptation of motor commands depends on the type of model used to describe the periphery

Modeling of equilibrium point trajectory control in human arm movements

The underlying concept of the Equilibrium Point Hypothesis (EPH) is that the CNS provides a virtual trajectory of joint motion, representing spacing and timing, with actual movement dynamics being produced by interactions of limb inertia, muscle viscosity and speed/position feedback from muscle spindles. To counter criticisms of the EPH, investigators have proposed the use of complex virtual trajectories, non-linear damping, stiffness and time varying stiffness to the EPH model. While these features allow the EPH to adequately produce human joint velocities, they conflict with the EPH’s premise of simple pre-planned monotonic control of movement trajectory. As a result, this study proposed an EPH based method, which provides a simpler mechanism in motor control without conflict with the core advantages of the original approach. This work has proposed relative damping as an addition to the EPH model to predict the single and two joint arm movements. This addition results in simulated data that not only closely match experimental angle data, but also match the experimental joint torques. In addition, it is suggested that this modified model can be used to predict the multi-joint angular trajectories with fast and normal velocities, without the need for time varying or non-linear stiffness and damping, but with simple monotonic virtual trajectories. In the following study, this relative damping model has been further enhanced with an EMG-based determination of the virtual trajectory and with physiologically realistic neuromuscular delays. The results of unobstructed voluntary movement studies suggest that the EPH models use realistic impedance values and produce desired joint trajectories and joint torques in unperturbed voluntary arm movement. A subsequent study of obstructed voluntary arm movement extended the relative damping concept, and incorporated the influential factors of the mechanical behavior of the neural, muscular and skeletal system in the control and coordination of arm posture and movement. A significant problem of the study is how this information should be used to modify control signals to achieve desired performance. This study used an EPH model to examine changes of controlling signals for arm movements in the context of adding perturbation/load in the form of forces/torques. The mechanical properties and reflex actions of muscles of the elbow joint were examined. Brief unexpected torque/force pulses of identical magnitude and time duration were introduced at different stages of the movement in a random order by a pre-programmed 3 degree of freedom (DOF) robotic arm (MOOG FCS HapticMaster). The results show that the subjects may maintain the same control parameters (virtual trajectory, stiffness and damping) regardless of added perturbations that cause substantial changes in EMG activity and kinematics

Control theoretic models of pointing

This article presents an empirical comparison of four models from manual control theory on their ability to model targeting behaviour by human users using a mouse: McRuer’s Crossover, Costello’s Surge, second-order lag (2OL), and the Bang-bang model. Such dynamic models are generative, estimating not only movement time, but also pointer position, velocity, and acceleration on a moment-to-moment basis. We describe an experimental framework for acquiring pointing actions and automatically fitting the parameters of mathematical models to the empirical data. We present the use of time-series, phase space, and Hooke plot visualisations of the experimental data, to gain insight into human pointing dynamics. We find that the identified control models can generate a range of dynamic behaviours that captures aspects of human pointing behaviour to varying degrees. Conditions with a low index of difficulty (ID) showed poorer fit because their unconstrained nature leads naturally to more behavioural variability. We report on characteristics of human surge behaviour (the initial, ballistic sub-movement) in pointing, as well as differences in a number of controller performance measures, including overshoot, settling time, peak time, and rise time. We describe trade-offs among the models. We conclude that control theory offers a promising complement to Fitts’ law based approaches in HCI, with models providing representations and predictions of human pointing dynamics, which can improve our understanding of pointing and inform design

On the intrinsic control properties of muscle and relexes: exploring the interaction between neural and musculoskeletal dynamics in the framework of the equilbrium-point hypothesis

The aim of this thesis is to examine the relationship between the intrinsic dynamics of the body and its neural control. Specifically, it investigates the influence of musculoskeletal properties on the control signals needed for simple goal-directed movements in the framework of the equilibriumpoint (EP) hypothesis. To this end, muscle models of varying complexity are studied in isolation and when coupled to feedback laws derived from the EP hypothesis. It is demonstrated that the dynamical landscape formed by non-linear musculoskeletal models features a stable attractor in joint space whose properties, such as position, stiffness and viscosity, can be controlled through differential- and co-activation of antagonistic muscles. The emergence of this attractor creates a new level of control that reduces the system’s degrees of freedom and thus constitutes a low-level motor synergy. It is described how the properties of this stable equilibrium, as well as transient movement dynamics, depend on the various modelling assumptions underlying the muscle model. The EP hypothesis is then tested on a chosen musculoskeletal model by using an optimal feedback control approach: genetic algorithm optimisation is used to identify feedback gains that produce smooth single- and multijoint movements of varying amplitude and duration. The importance of different feedback components is studied for reproducing invariants observed in natural movement kinematics. The resulting controllers are demonstrated to cope with a plausible range of reflex delays, predict the use of velocity-error feedback for the fastest movements, and suggest that experimentally observed triphasic muscle bursts are an emergent feature rather than centrally planned. Also, control schemes which allow for simultaneous control of movement duration and distance are identified. Lastly, it is shown that the generic formulation of the EP hypothesis fails to account for the interaction torques arising in multijoint movements. Extensions are proposed which address this shortcoming while maintaining its two basic assumptions: control signals in positional rather than force-based frames of reference; and the primacy of control properties intrinsic to the body over internal models. It is concluded that the EP hypothesis cannot be rejected for single- or multijoint reaching movements based on claims that predicted movement kinematics are unrealistic

A Continuous Grasp Representation for the Imitation Learning of Grasps on Humanoid Robots

Models and methods are presented which enable a humanoid robot to learn reusable, adaptive grasping skills. Mechanisms and principles in human grasp behavior are studied. The findings are used to develop a grasp representation capable of retaining specific motion characteristics and of adapting to different objects and tasks. Based on the representation a framework is proposed which enables the robot to observe human grasping, learn grasp representations, and infer executable grasping actions

Cerebral spasticity modeled as disorded equilibrium point control

Spasticity is a highly complex phenomenon, which has not been defined in precise and quantifiable terms. Although the muscle stretch reflex is thought to play an important role in spasticity generation, the pathophysiologic basis of spasticity is not completely understood. A valid measure of spasticity is one that is chosen within the context of a theory describing the physiological mechanisms underlying the control of posture and movement in healthy individuals and possible impairments of these mechanisms leading to motor disorders. This research’s goal was to determine the role of stretch reflex threshold in the regulation of impaired motor control through the exploration of the following research questions: Can experimental measures be produced leading to the development of a model of spasticity that can be interpreted within the framework of a general theory of motor control? Can the underlying motor control framework provide unique parameters capable of describing both normal and altered/abnormal movement? Can the model be robust enough to explain active as well as passive movement? The research method outlined in this dissertation takes the novel approach of incorporating the equilibrium point hypothesis into a trajectory-based analysis of pendulum knee motion. The Equilibrium Point Hypothesis (EPH) of motor control theorizes that the central nervous system (CNS) provides a virtual trajectory of joint motion, representing space and time. A forward dynamic model has been developed that can reproduce kinematic data through the using optimized model parameters. The incorporation of the equilibrium point hypothesis in forward model was not only recognition that examination of the entire trajectory of the limb, rather than just the first amplitude of swing, was necessary, but also, that movement can be characterized by the simple extraction of three parameters: a relative damping coefficient, relative stiffness coefficient and mathematical function which can act as an approximation CNS the virtual trajectory described in the EPH. This research produced a model of passive motion with the ability to produce parameter values that not only differentiate subjects with spasticity from subjects with no clinical signs of spasticity but that can separate subjects based on severity of spastic condition. Research which began as an endeavor to model the passive motion of the pendulum knee test, led to the development of a unifying model of motor control that is robust enough to describe both active and passive movements

Neuro-musculoskeletal Models: A Tool to Study the Contribution of Muscle Dynamics to Biological Motor Control

Das Verständnis der Prinzipien, die menschlichen Bewegungen zugrunde liegen, ist die Basis für die Untersuchung der Entstehung gesunder Bewegungen und, was noch wichtiger ist, der Entstehung motorischer Störungen aufgrund neurodegenerativer Erkrankungen oder anderer pathologischer Zustände. Dieses Verständnis zu erlangen ist jedoch herausfordernd, da menschliche Bewegung das Ergebnis eines komplexen, dynamischen Zusammenspiels von biochemischen und biophysikalischen Prozessen im Bewegungsapparat und den hierarchisch organisierten neuronalen Kontrollstrukturen ist. Um die Wechselwirkungen dieser Strukturen zu untersuchen, bieten Computersimulationen, die mathematische Modelle des muskuloskelettalen Systems mit Modellen seiner neuronalen Kontrolle kombinieren, ein nützliches Werkzeug. In diesen Simulationen können einzelne Prozesse oder ganze Funktionseinheiten deaktiviert oder gestört werden, um die Auswirkungen dieser Veränderungen auf die vorhergesagten Bewegungen zu untersuchen. Die Plausibilität der zugrundeliegenden Modelle kann durch den Vergleich der Simulationen mit Daten aus Humanexperimenten und biologisch inspirierten Robotermodellen beurteilt werden. Das Ziel dieser Arbeit war es, neuro-muskuloskelettale Modelle als Hilfsmittel zur Untersuchung von Konzepten der biologischen Bewegungskontrolle zu verwenden. Von besonderem Interesse war der Beitrag der Muskeldynamik zur Kontrolle, d.h. wie die intrinsischen muskuloskelettalen Eigenschaften die motorische Kontrolle vereinfachen, ohne die motorische Genauigkeit zu beeinträchtigen. Zusätzlich wurde der Einfluss propriozeptiver Reflexmechanismen in verschiedenen Szenarien getestet. Die verwendeten neuro-muskuloskelettalen Modelle sind eine Kombination von Mehrkörpermodellen der Muskel-Skelett-Struktur des Armes oder des ganzen Körpers mit einem biologisch inspirierten hybriden Gleichgewichtspunkt-Kontrollmodell. In einer Simulationsstudie stellten wir fest, dass unser Armmodell realistische Reaktionen auf externe mechanische Störungen für zielgerichtete Bewegungen mit einem Freiheitsgrad vorhersagt. Auf dieser Grundlage simulierten wir die Anwendung von tragbaren Assistenzgeräten zur Kompensation unerwünschter Hypermetrie, d.h. einer überschießenden Reaktion bei zielgerichteten Bewegungen im Zusammenhang mit zerebellärer Ataxie und anderen neurodegenerativen Erkrankungen. Wir fanden heraus, dass einfache mechanische Hilfsmittel ausreichend sein können, um die Hypermetrien auf ein normales Niveau zu reduzieren. Wir stellten jedoch auch fest, dass die Größe des Drehmoments und der Kraft, die zur Kompensation der Störung erforderlich sind, möglicherweise deutlich unterschätzt wird, wenn die Muskel-Sehnen-Eigenschaften im Modell nicht berücksichtigt werden. Die Ergebnisse dieser beiden Studien bestätigten die Hypothese aus der Literatur, dass die Morphologie des Muskel-Skelett-Systems signifikant zur Bewegung beiträgt und somit deren Kontrolle vereinfacht. Deshalb haben wir einen informationstheoretischen Ansatz verwendet, um diesen Beitrag für zielgerichtete und oszillatorische Armbewegungen mit zwei Freiheitsgraden zu charakterisieren. Die Ergebnisse bestätigten, dass die unteren Kontrollebenen, einschließlich der Muskeln und ihrer Aktivierungsdynamik, wichtige Beiträge zur gesamten Kontrollhierarchie leisten. Beispielsweise führt ein einfaches, stückweise konstantes Muskelstimulationssignal, das nur wenig Information enthält, zu einer geschmeidigen Bewegung. Der physiologische Detailgrad, der in unseren Muskel-Skelett-Modellen enthalten ist, ermöglicht nicht nur die Untersuchung von Theorien zur motorischen Kontrolle, sondern auch die Untersuchung von Größen wie inneren Kräften in Muskeln und Gelenken, die experimentell normalerweise nicht zugänglich sind. Diese Größen sind zum Beispiel in der Ergonomie und für die Entwicklung von Assistenzgeräten von Bedeutung. In einer Ganzkörpersimulationsstudie untersuchten wir den Beitrag des Dehnungsreflexes zu den resultierenden Muskelkräften bei einer aktiven externen Repositionierung des Hüftgelenkes für einen großen Bereich von Bewegungsgeschwindigkeiten. Wir fanden heraus, dass der relative Kraftbeitrag des Feedback-Mechanismus vom modellierten kognitiven Zustand abhängig ist und einen nicht vernachlässigbaren Beitrag leistet, insbesondere bei hohen Repositionsgeschwindigkeiten. Die Gesamtheit unserer Ergebnisse zeigt, dass die Eigenschaften des Bewegungsapparates signifikant zur Erzeugung und Kontrolle von Bewegung beitragen und es daher wichtig ist, sie bei der Modellierung der menschlichen Bewegung zu berücksichtigen. Daher sprechen die Ergebnisse für die Kombination eines physiologisch fundierten biomechanischen und biochemischen Modells des Bewegungsapparates mit biologisch inspirierten Konzepten der motorischen Kontrolle. Diese Computersimulationen haben sich als ein nützliches Werkzeug zum Verständnis der Prozesse erwiesen, die der Erzeugung gesunder und pathologisch beeinträchtigter menschlicher Bewegungen zugrunde liegen.Understanding the principles underlying human movement is the basis for investigating the generation of healthy movements and, more importantly, the origins of motor disorders due to neurodegenerative diseases or other pathological conditions. However, gaining this understanding is challenging since human motion is the result of a complex, dynamic interplay of biochemical and biophysical processes in the musculoskeletal system and the hierarchically organized neuronal control structures. To study the interactions of these structures, computer simulations that combine mathematical models of the musculoskeletal system with models of its neuronal control provide a useful tool. In these simulations, single processes or whole functional units can be disabled or perturbed to study the effects of these changes on the predicted movements. The plausibility of the underlying models can be assessed by comparing the simulations with data from human experiments and biologically inspired robotic models. The purpose of this work was to use neuro-musculoskeletal models as tools to study concepts of biological motor control. Of particular interest was the contribution of muscle dynamics to the control, i.e. how the intrinsic musculoskeletal properties simplify motor control without compromising motor accuracy. Additionally, the influence of proprioceptive reflex mechanisms was tested in different scenarios. The neuro-musculoskeletal models that were used are a combination of multibody musculoskeletal models of the arm or the whole body with a biologically inspired hybrid equilibrium-point controller. In a simulation study, we found that our arm model predicts realistic reactions to external mechanical perturbations while performing one-degree-of-freedom goal-directed movements. Based on this, we simulated the application of wearable assistive devices to compensate for unwanted hypermetria, i.e. an overshooting response in goal-directed movements associated with cerebellar ataxia and other neurodegenerative disorders. We found that simple mechanical devices may be sufficient to reduce the hypermetria to a normal level. However, we also observed that the magnitude of torque and power that is required to compensate for the disorder may be significantly underestimated if muscle-tendon characteristics are not considered in the computational model. The results of these two studies confirmed the hypothesis from literature that the morphology of musculoskeletal systems significantly contributes to the movement and thus simplifies its control. Therefore, we made use of the information-theoretic approach of quantifying morphological computation to characterize this contribution for goal-directed and oscillatory arm movements with two degrees of freedom. The results asserted that the lower levels of control, including the muscles and their activation dynamics, make important contributions to the overall control hierarchy. For example, a simple piecewise constant muscle stimulation signal that contains only little information results in a smooth movement. The level of physiological detail that is included in our musculoskeletal models does not only allow for the examination of motor control theories but also makes it possible to study quantities like internal forces in muscles and joints, usually not experimentally accessible. These quantities are relevant, for example, in ergonomics and for the development of assistive devices. In a whole-body simulation study, we investigated the contribution of the stretch reflex to the resulting muscle forces during active external repositioning of the hip joint for a large range of movement velocities. We found that, depending on the modeled cognitive state, the relative force contribution of the feedback mechanism is not negligible, especially for high repositioning velocities. The entirety of our results shows that the properties of the musculoskeletal system significantly contribute to the generation and control of movement and, thus, it is important to take them into account when modeling human movement. Therefore, the results advocate the combination of a physiologically well-founded biomechanical and biochemical model of the musculoskeletal system with biologically inspired concepts of motor control. These computer simulations have proven to be a useful tool towards the comprehension of the processes underlying the generation of healthy and pathologically impaired human movements

A robotic model to investigate human motor control

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