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PubMed2000

Vestibular, visual, and somatosensory contributions to human control of upright stance.

We investigated the changes of human posture control of upright stance which occur when vestibular cues (VEST) are absent and visual and somatosensory orientation cues (VIS, SOM) are removed. Postural responses to sinusoidal tilts of a motion platform in the sagittal plane (+/-2 degrees, f=0.05, 0.1, 0.2 and 0.4 Hz) were studied in normal subjects (Ns) and patients with bilateral vestibular loss (Ps). We found that absence of VEST (Ps, visual reference) and removal of VIS (Ns, no visual reference) had little effect on stabilization of upright body posture in space. In the absence of both VEST and VIS (Ps, no visual reference) somatosensory graviception still provided some information on body orientation in space at 0.05 and 0.1 Hz. However, at the higher frequencies Ps qualitatively changed their behavior; they then tended to actively align their bodies with respect to the motion platform. The findings confirm predictions of a novel postural control model.

Cues↗
PubMed2004

A new interpretation of spontaneous sway measures based on a simple model of human postural control.

This study shows that center-of-pressure (COP) traces that closely resemble physiologically measured COP functions can be produced by an appropriate selection of model parameters in a simple feedback model of the human postural control system. Variations in the values of stiffness, damping, time delay, and noise level determine the values of 15 sway measures commonly used to characterize spontaneous sway. Results from model simulations indicate that there is a high degree of correlation among these sway measures, and the measures cluster into three different groups. Only two principal components accounted for about 92% of the variation among the different sway measures analyzed. This model can be used to formulate hypotheses regarding the cause of postural control deficits reported in the literature. This is accomplished using a multidimensional optimization procedure to estimate model parameters from a diverse set of spontaneous sway measures. These model parameters describe physiologically meaningful features of the postural control system as opposed to conventional sway measures that provide only a parametric description of sway. To show the application of this method, we applied it to published data of spontaneous sway from elderly subjects and contrasted it to the data of young healthy subjects. We found that modest increases in stiffness and damping and a fairly large increase in noise level with aging could account for the variety of sway measures reported in the literature for elderly subjects.

Age Factors↗
PubMed2004

Meta level concept versus classic reflex concept for the control of posture and movement.

Postural reflexes are replaced soon after birth by automatic reactions that allow for volition and cognition. It is still an enigma how this change in postural control is achieved. We suggest that the change involves the formation of a sensory processing level (meta level) that becomes interleaved in between the tight sensor-actuator coupling of the classic reflexes. We assume that the brain applies at this level intersensory interactions to reconstruct the physical stimuli which are causing the physiological stimuli and sensory signals. The thus derived estimates of the physical stimuli are then used as feedback signals in the posture control system. We present this concept on the background of the classic reflex concept and earlier attempts in the literature to overcome it. The earlier attempts were often motivated by the question how the brain prevents voluntary movements from being hampered by reflexive stabilisation of posture (so-called posture-movement problem). We compare our new concept with the classic reflex concept in a theoretical approach, by implementing both concepts into simple postural control models. In simulations of the two models we superimpose external perturbations (the physical stimuli) and a voluntary body lean movement. We show that it is possible to achieve successful stimulus compensation and unperturbed lean movement with both, the model derived from the new concept and the one of the classic reflex concept. With both approaches, the posture-movement problem does not arise. Based on preliminary considerations that include experimental findings from the literature, however, we conclude that the new concept provides more explanatory power than the classic reflex concept.

Animals↗
PubMed2005

Cross-correlation between EMG and center of gravity during quiet stance: theory and simulations.

Several signal processing tools have been employed in the experimental study of the postural control system in humans. Among them, the cross-correlation function has been used to analyze the time relationship between signals such as the electromyogram and the horizontal projection of the center of gravity. The common finding is that the electromyogram precedes the biomechanical signal, a result that has been interpreted in different ways, for example, the existence of feedforward control or the preponderance of a velocity feedback. It is shown here, analytically and by simulation, that the cross-correlation function is dependent in a complicated way on system parameters and on noise spectra. Results similar to those found experimentally, e.g., electromyogram preceding the biomechanical signal may be obtained in a postural control model without any feedforward control and without any velocity feedback. Therefore, correct interpretations of experimentally obtained cross-correlation functions may require additional information about the system. The results extend to other biomedical applications where two signals from a closed loop system are cross-correlated.

Computer Simulation↗
PubMed1993

Geometrical approach to neural net control of movements and posture.

In one approach to modeling brain function, sensorimotor integration is described as geometrical mapping among coordinates of non-orthogonal frames that are intrinsic to the system; in such a case sensors represent (covariant) afferents and motor effectors represent (contravariant) motor efferents. The neuronal networks that perform such a function are viewed as general tensor transformations among different expressions and metric tensors determining the geometry of neural functional spaces. Although the non-orthogonality of a coordinate system does not impose a specific geometry on the space, this "Tensor Network Theory of brain function" allows for the possibility that the geometry is non-Euclidean. It is suggested that investigation of the non-Euclidean nature of the geometry is the key to understanding brain function and to interpreting neuronal network function. This paper outlines three contemporary applications of such a theoretical modeling approach. The first is the analysis and interpretation of multi-electrode recordings. The internal geometries of neural networks controlling external behavior of the skeletomuscle system is experimentally determinable using such multi-unit recordings. The second application of this geometrical approach to brain theory is modeling the control of posture and movement. A preliminary simulation study has been conducted with the aim of understanding the control of balance in a standing human. The model appears to unify postural control strategies that have previously been considered to be independent of each other. Third, this paper emphasizes the importance of the geometrical approach for the design and fabrication of neurocomputers that could be used in functional neuromuscular stimulation (FNS) for replacing lost motor control.

Animals↗
PubMed1999

Internal models in the control of posture.

The aim of the paper is to investigate the application of control schemes based on "internal models" to the stabilization of the standing posture. The computational complexities of the control problems are analyzed, showing that muscle stiffness alone is insufficient to carry out the task. The paper also re-visits the concept of the cerebellum as a Smith's predictor.

Journal Article↗
PubMed2003

Biomechanical modeling and optimal control of human posture.

The present work describes the biomechanical modeling of human postural mechanics in the saggital plane and the use of optimal control to generate open-loop raising-up movements from a squatting position. The biomechanical model comprises 10 equivalent musculotendon actuators, based on a 40 muscles model, and three links (shank, thigh and HAT-Head, Arms and Trunk). Optimal control solutions are achieved through algorithms based on the Consistent Approximations Theory (Schwartz and Polak, 1996), where the continuous non-linear dynamics is represented in a discrete space by means of a Runge-Kutta integration and the control signals in a spline-coefficient functional space. This leads to non-linear programming problems solved by a sequential quadratic programming (SQP) method. Due to the highly non-linear and unstable nature of the posture dynamics, numerical convergence is difficult, and specific strategies must be implemented in order to allow convergence. Results for control (muscular excitations) and angular trajectories are shown using two final simulation times, as well as specific control strategies are discussed.

Algorithms↗
PubMed2001

Optimal posture control of a musculo-skeletal arm model.

In this paper maximal performance posture control of the human arm is investigated by means of model simulations. Recent experiments (F.C.T. van der Helm, submitted, 2000) have shown that the reflexive feedback during postural control varies with the bandwidth of the applied force disturbances. This paper focusses on the influence of the frequency content of force disturbances on the reflexive feedback gains by means of optimization. The arm is modelled by a nonlinear musculo-skeletal model with two degrees of freedom and six muscles. To facilitate the optimization of the model parameters, the arm model is linearized. A performance criterion is minimized for stochastic force disturbances in a two-step procedure: (1) optimization of static muscle activations using an additional energy criterion to obtain a unique and energy-efficient solution; antid (2) optimization of reflex gains using an additional control effort criterion to obtain a unique solution. The optimization reveals that for the given task and posture, the shoulder muscles have the largest contribution, whereas the bi-articular muscles have a relatively small contribution to the behaviour. The dynamics at the endpoint level are estimated so that a comparison can be made with the experiments. Compared to the experiments, the intrinsic damping of the model is relatively large (about 150%), whereas the intrinsic stiffness is relatively small (about 60%). These differences can be attributed to unmodelled mechanical effects of crossbridges in Hill-type muscle models. The optimized reflex gains show remarkable similarities with the values found in the experiments, implying that humans can adjust their reflexive feedback gains in an optimal way, weighting the performance and energy. The approach in this paper could be useful in the study of various posture tasks, for example in the prediction of the relation between the control parameters of various musculo-skeletal models and different experimental variables.

Arm↗
PubMed1989

Evaluation of a generalized model of human postural dynamics and control in the sagittal plane.

A two-step identification method is used to evaluate a generalized model of human postural control in the sagittal plane. Postural dynamics are represented as a planar open-chain linkage system supported by a triangular foot. The control mechanism is modeled as a state feedback element in which the torque acting at a given link is an arbitrary function of the state variables--angles and angular velocities. To validate the approach, six normal subjects underwent two series of experiments. The first series were used to determine an appropriate model of the system dynamics. The second series were used to estimate the parameters of the feedback model. A computer simulation of the complete system shows that the model predictions closely match the observed responses. These results suggest that the proposed model provides a useful framework for analysis of postural control mechanisms.

Computer Simulation↗
PubMed1995

An optimal control model for analyzing human postural balance.

The question posed in this study is whether optimal control and state estimation can explain selection of control strategies used by humans, in response to small perturbations to stable upright balance. To answer this question, a human sensorimotor control model, compatible with previous work by others, was assembled. This model incorporates linearized equations and full-state feedback with provision for state estimation. A form of gain-scheduling is employed to account for nonlinearities caused by control and biomechanical constraints. By decoupling the mechanics and transforming the controls into the space of experimentally observed strategies, the model is made amenable to the study of a number of possible control objectives. The objectives studied include cost functions on the state deviations, so as to control the center of mass, provide a stable platform for the head, or maintain upright stance, along with a cost function on control effort. Also studied was the effect of time delay on the stability of controls produced using various control strategies. An objective function weighting excursion of the center of mass and deviations from the upright stable position, while taking advantage of fast modes of the system, as dictated by inertial parameters and musculoskeletal geometry, produces a control that reasonably matches experimental data. Given estimates of sensor performance, the model is also suited for prediction of uncertainty in the response.

Biomechanical Phenomena↗
PubMed2004

Nonlinear postural control in response to visual translation.

Recent models of human postural control have focused on the nonlinear properties inherent to fusing sensory information from multiple modalities. In general, these models are underconstrained, requiring additional experimental data to clarify the properties of such nonlinearities. Here we report an experiment suggesting that new or multiple mechanisms may be needed to capture the integration of vision into the postural control scheme. Subjects were presented with visual displays whose motion consisted of two components: a constant-amplitude, 0.2 Hz oscillation, and constant-velocity translation from left to right at velocities between 0 cm/s and 4 cm/s. Postural sway variability increased systematically with translation velocity, but remained below that observed in the eyes-closed condition, indicating that the postural control system is able to use visual information to stabilize sway even at translation velocities as high as 4 cm/s. Gain initially increased as translation velocity increased from 0 cm/s to 1 cm/s and then decreased. The changes in gain and variability provided a clear indication of nonlinearity in the postural response across conditions, which were interpreted in terms of sensory reweighting. The fact that gain did not decrease at low translation velocities suggests that the postural control system is able to decompose relative visual motion into environmental motion and self-motion. The eventual decrease in gain suggests that nonlinearities in sensory noise levels (state-dependent noise) may also contribute to the sensory reweighting involved in postural control. These results provide important constraints and suggest that multiple mechanisms may be required to model the nonlinearities involved in sensory fusion for upright stance control.

Adult↗
PubMed1984

Mathematical modeling and simulation of the postural control loop. Part III.

The engineering analysis and modeling of the neuromuscular system is an exciting and challenging area of research where we may learn something about the principles of controlling large, complex systems. This review is limited to the most peripheral subsystem: the postural control loop. In the first part, we reviewed the input-output properties of the motoneuron pool, modeling of the motor unit action potential, and analysis of the interference electromyographic activity. Aslo included were the engineering techniques to quantitatively study electromyographic activity and its relationship to muscle force. The second part of this review focuses on the biomechanical models of the muscle based on Hill's work as well as the modern sliding filament theory. This section also includes models of the proprioceptive receptors: the muscle spindle organ and the Golgi tendon organ. In this final paper we discuss various models and simulations of the peripheral loop and their applications to study physiological system behavior such as tremor.

Afferent Pathways↗
PubMed1984

Mathematical modeling and simulation of the postural control loop--Part II.

The engineering analysis and modeling of the neuromuscular system is an exciting and challenging area of research where we may learn something about the principles of controlling large, complex systems. This review is limited to the most peripheral subsystem: the postural control loop. In the first part, we reviewed the input-output properties of the motoneuron pool, modeling of the motor unit action potential and analysis of the interference electromyographic activity. Also included were the engineering techniques to quantitatively study electromyographic activity and its relationship to muscle force. The second part of this review focuses on the biomechanical models of the muscle based on Hill's work as well as the modern sliding filament theory. This section will also include models of the proprioceptive receptors: the muscle spindle organ and the Golgi tendon organ. In the final paper we will discuss various models and simulations of the peripheral loop and their applications to study physiological system behavior such as tremor.

Action Potentials↗
PubMed1999

Impedance characteristics of a neuromusculoskeletal model of the human arm I. Posture control.

The mechanical impedance of neuromusculoskeletal models of the human arm is studied in this paper. The model analysis provides a better understanding of the contributions of possible intrinsic and reflexive components of arm impedance, makes clear the limitations of second-order mass-viscosity-stiffness models and reveals possible task effects on the impedance. The musculoskeletal model describes planar movements of the upper arm and forearm, which are moved by six lumped muscles with nonlinear dynamics. The motor control system is represented by a neural network which combines feedforward and feedback control. It is optimized for the control of movements or for posture control in the presence of external forces. The achieved impedance characteristics depend on the conditions during the learning process. In particular, the impedance is adapted in a suitable way to the frequency content and direction of external forces acting on the hand during an isometric task. The impedance characteristics of a model, which is optimized for movement control, are similar to experimental data in the literature. The achieved stiffness is, to a large extent, reflexively determined whereas the approximated viscosity is primarily due to intrinsic attributes. It is argued that usually applied Hill-type muscle models do not properly represent intrinsic muscle stiffness.

Adaptation, Physiological↗
PubMed2003

Dynamic regulation of sensorimotor integration in human postural control.

Upright stance in humans is inherently unstable, requiring corrective action based on spatial-orientation information from sensory systems. One might logically predict that environments providing access to accurate orientation information from multiple sensory systems would facilitate postural stability. However, we show that, after a period in which access to accurate sensory information was reduced, the restoration of accurate information disrupted postural stability. In eyes-closed trials, proprioceptive information was altered by rotating the support surface in proportion to body sway (support surface "sway-referencing"). When the support surface returned to a level orientation, most subjects developed a transient 1-Hz body sway oscillation that differed significantly from the low-amplitude body sway typically observed during quiet stance. Additional experiments showed further enhancement of the 1-Hz oscillation when the surface transitioned from a sway-referenced to a reverse sway-referenced motion. Oscillatory behavior declined with repetition of trials, suggesting a learning effect. A simple negative feedback-control model of the postural control system predicted the occurrence of this 1-Hz oscillation in conditions where too much corrective torque is generated in proportion to body sway. Model simulations were used to distinguish between two alternative explanations for the excessive corrective torque generation. Simulation results favor an explanation based on the dynamic reweighting of sensory contributions to postural control rather than a load-compensation mechanism that scales torque in proportion to a fixed combination of sensory-orientation information.

Adaptation, Physiological↗
PubMed2005

Multisensory control of human upright stance.

The interaction of different orientation senses contributing to posture control is not well understood. We therefore performed experiments in which we measured the postural responses of normal subjects and vestibular loss patients during perturbation of their stance. Subjects stood on a motion platform with their eyes closed and auditory cues masked. The perturbing stimuli consisted of either platform tilts or external torque produced by force-controlled pull of the subjects' body on a stationary platform. Furthermore, we presented trials in which these two stimuli were applied when the platform was body-sway referenced (i.e., coupled 1:1 to body position, by which ankle joint proprioceptive feedback is essentially removed). We analyzed subjects' postural responses, i.e., the excursions of their center of mass (COM) and center of pressure (COP), using a systems analysis approach. We found gain and phase of the responses to vary as a function of stimulus frequency and in relation to the absence versus presence of vestibular and proprioceptive cues. In addition, gain depended on stimulus amplitude, reflecting a non-linearity in the control. The experimental results were compared to simulation results obtained from an 'inverted pendulum' model of posture control. In the model, sensor fusion mechanisms yield internal estimates of the external stimuli, i.e., of the external torque (pull), the platform tilt and gravity. These estimates are derived from three sensor systems: ankle proprioceptors, vestibular sensors and plantar pressure sensors (somatosensory graviceptors). They are fed as global set point signals into a local control loop of the ankle joints, which is based on proprioceptive negative feedback. This local loop stabilizes the body-on-foot support, while the set point signals upgrade the loop into a body-in-space control. Amplitude non-linearity was implemented in the model in the form of central threshold mechanisms. In model simulations that combined sensor fusion and thresholds, an automatic context-specific sensory re-weighting across stimulus conditions occurred. Model parameters were identified using an optimization procedure. Results suggested that in the sway-referenced condition normal subjects altered their postural strategy by strongly weighting feedback from plantar somatosensory force sensors. Taking this strategy change into account, the model's simulation results well paralleled all experimental results across all conditions tested.

Adult↗
PubMed1997

The ankle strategy for postural control--a comparison between a model-based and a marker-based method.

When analysing postural control statistical characteristics of the centre of pressure or the ground reaction force are often used. A complement would be to analyse movement strategies as well. The purpose of this study was to determine to what extent the ankle strategy is used to maintain the balance in standing. One important question is if it is sufficient to use only force plate data in this determination, or if markers have to be used. To answer this question we studied two different methods--a marker-based method and a model-based method. The latter needs force plate data only as input to an inverted pendulum model. Different measures were calculated and used to draw conclusions of to what extent the ankle strategy was used. Experiments demonstrated that the data from the two methods provided the same general conclusions, although there were some differences in the estimated measures. By using the model-based method instead of tracking markers we can increase the clinical effectiveness considerably, get a lesser amount of data, and still get results comparable to the marker-based method.

Ankle↗