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At least 19 recordsLinked to original sources
PubMed2003

A multisensory posture control model of human upright stance.

We present a multisensory postural control model based on experiments where the balance in normal subjects and vestibular loss patients was perturbed by application of external torque produced by force-controlled pull stimuli. The stimuli were applied while subjects stood on a stationary or body-sway-referenced motion platform with eyes closed and auditory cues masked. Excursions of the center of mass (COM) and the center of pressure (COP) were analyzed using a systems analysis approach. The results were compared to an 'inverted pendulum' model of posture control. The model receives input from four sensors: ankle proprioceptors, semicircular canals, otoliths, and plantar pressure sensors (somatosensory graviceptors). Sensor fusion mechanisms are used to yield separate internal representations of foot support motion, gravity, and external torque (pull). These representations are fed as global set point signals into a local control loop based on ankle proprioceptive negative feedback. This set point control upgrades the proprioceptive body-on-foot (support) stabilization into a body-in-space control which compensates for support tilt, gravity, and contact forces. This compensation occurs even when the stimuli are combined or a voluntary lean is superimposed. Model simulations paralleled our experimental findings.

Adult↗
PubMed2002

Sensory contributions to the control of stance: a posture control model.

We present the outline of a dual kinetic-kinematic postural control model. It is based on concepts of inter-sensory interaction (sensor fusion) which we consider instrumental for sensorimotor integration. Separation into kinetic and kinematic control signals begins at the level of the sensors (e.g., vestibular system--otoliths: force field meters, canals: head angular speedometers). Sensor fusion mechanisms are used to yield separate internal representations for foot support kinematics, force fields such as gravity, and contact forces such as pull or push having impact on the body. These representations are fed as global set point signals into local proprioceptive control loops of the joints. Fed into an ankle joint proprioceptive loop for body-on-support stabilization, they yield compensation of support tilt, gravity and contact forces, even when these stimuli are combined and, furthermore, voluntary lean is superimposed. Model simulations parallel our experimental findings so far obtained.

Animals↗
PubMed2001

Modeling postural control in the lamprey.

A phenomenological model of the mechanism of stabilization of the body orientation during locomotion (dorsal side up) in the lamprey is presented. The mathematical modeling is based on experimental results obtained during investigations of postural control in lampreys using a combined in vivo and robotics approach. The dynamics of the model agree qualitatively with the experimental data. It is shown by computer simulations that postural correction commands from reticulospinal neurons provide information sufficient to stabilize body orientation in the lamprey. The model is based on differences between the effects exerted by the vestibular apparatus on the left and the right side.

Animals↗
PubMed1998

Two-Dimensional Automatic Control Modeling of a Posture Control System.

A posture control model has been developed on the basis of the 2-dimensional feedback control theory. Human postural characteristics were investigated in 5 healthy participants. Tests were performed with eyes open and eyes closed. After 5 s of quiet standing, each participant was unexpectedly pulled forward by 30 mm at his pelvis height and then released. Postural sway was measured over 20 s at a rate of 100 per second. Transfer functions to represent the posture control characteristic were identified by the least squares' method. These showed good results of the model's fitness, predictability, and stability. The response of the eyes-closed condition to perturbation is more oscillatory than that of the eyes-open condition. It seems that the model identified could be applicable to ergonomics, sports, or clinical situations.

model fitness↗
PubMed2000

Postural control model interpretation of stabilogram diffusion analysis.

Collins and De Luca [Collins JJ. De Luca CJ (1993) Exp Brain Res 95: 308-318] introduced a new method known as stabilogram diffusion analysis that provides a quantitative statistical measure of the apparently random variations of center-of-pressure (COP) trajectories recorded during quiet upright stance in humans. This analysis generates a stabilogram diffusion function (SDF) that summarizes the mean square COP displacement as a function of the time interval between COP comparisons. SDFs have a characteristic two-part form that suggests the presence of two different control regimes: a short-term open-loop control behavior and a longer-term closed-loop behavior. This paper demonstrates that a very simple closed-loop control model of upright stance can generate realistic SDFs. The model consists of an inverted pendulum body with torque applied at the ankle joint. This torque includes a random disturbance torque and a control torque. The control torque is a function of the deviation (error signal) between the desired upright body position and the actual body position, and is generated in proportion to the error signal, the derivative of the error signal, and the integral of the error signal [i.e. a proportional, integral and derivative (PID) neural controller]. The control torque is applied with a time delay representing conduction, processing, and muscle activation delays. Variations in the PID parameters and the time delay generate variations in SDFs that mimic real experimental SDFs. This model analysis allows one to interpret experimentally observed changes in SDFs in terms of variations in neural controller and time delay parameters rather than in terms of open-loop versus closed-loop behavior.

Computer Simulation↗
PubMed2000

Renormalization group analysis of a quivering string model of posture control.

Scaling concepts and renormalization group methods are applied to a simple linear model of human posture control consisting of a trembling or quivering string subject to damping and restoring forces. The string is driven by uncorrelated white Gaussian noise, intended to model the corrections of the physiological control system. We find that adding a weak quadratic nonlinearity to the posture control model opens up a rich and complicated phase space (representing the dynamics) with various nontrivial fixed points and basins of attraction. The transition from diffusive to saturated regimes of the linear model is understood as a crossover phenomenon, and the robustness of the linear model with respect to weak nonlinearities is confirmed. Correlations in posture fluctuations are obtained in both time and space domains. There is an attractive fixed point identified with falling. The scaling of the correlations in the front-back displacement, which can be measured in the laboratory, is predicted for both large-separation (along the string) and long-time regimes of posture control.

Humans↗
PubMed2003

Evaluation of the lambda model for human postural control during ankle strategy.

An accurate modeling of human stance might be helpful in assessing postural deficit. The objective of this article is to validate a mathematical postural control model for quiet standing posture. The postural dynamics is modeled in the sagittal plane as an inverted pendulum with torque applied at the ankle joint. The torque control system is represented by the physiological lambda model. Two neurophysiological command variables of the central nervous system, designated lambda and micro, establish the dynamic threshold muscle at which motoneuron recruitment begins. Kinematic data and electromyographic signals were collected on four young males in order to measure small voluntary sway and quiet standing posture. Validation of the mathematical model was achieved through comparison of the experimental and simulated results. The mathematical model allows computation of the unmeasurable neurophysiological commands lambda and micro that control the equilibrium position and stability. Furthermore, with the model it is possible to conclude that low-amplitude body sway during quiet stance is commanded by the central nervous system.

Adult↗
PubMed2001

Increased risk for falling associated with obesity: mathematical modeling of postural control.

Recent epidemiological studies report that obesity is positively related to fracture incidence. In the present experiment, a model of postural control was used to examine the impact of an abnormal distribution of body fat in the abdominal area upon postural stability. Obese and lightweight humanoids were destabilized by imposing a small initial angular speed from a neutral standing position. To avoid a loss of stability yielding a stepping reaction or a fall, an ankle torque is necessary to counteract the perturbation. Three torque parameters--ankle torque onset, time to peak torque, and muscular ankle torque--were entered in a program to simulate the intrinsic variability of the human postural control system. A loss of stability was detected when the center of pressure exceeded stability margins. The most striking observation is the nonlinear increase of torque needed to stabilize the humanoid when the motor response was characterized by delayed temporal parameters. The effect was more pronounced when an anterior position of the center of mass was included in the simulations. This suggests that, when submitted to daily postural stresses and perturbations, obese persons (particularly those with an abnormal distribution of body fat in the abdominal area) may be at higher risk of falling than lightweight individuals.

Abdomen↗
PubMed1990

Posturography following rotation: a model of posture control during vestibular dysfunction.

Postural stability is influenced by multiple sensory inputs, including visual, somatosensory, and vestibular cues. Adaptive changes in how these sensory inputs influence posture may occur in the presence of vestibular dysfunction. In this study, we simulated vestibular dysfunction in normal subjects in order to study immediate and adaptive modifications in postural control under reproducible and reversible experimental conditions. Twenty subjects (ages 20 to 35 years) were tested on a force-detecting platform; the support surface or visual surround was modulated (anterior-posterior) proportionally to body sway. Vestibular dysfunction was simulated by rotating subjects with their eyes closed at 180 degrees/second for 30 seconds, then rapidly halting rotation. Posturographic measures were obtained during the subsequent 20 seconds. Ten identical rotation trials for each subject were performed, alternating the direction of rotation, and with either the support surface or the visual surround modulated with sway in different subjects. Thus, only one sensory modality could provide Earth-stable sensory cues. Control groups were tested to assess effects of repeated alternating rotations alone, and effects of repeated posturographic trials alone. Sway was quantified by measuring peak-to-peak excursions of the center of mass. Results showed that subjects were more stable after rotation when they were provided with a stable support surface than with a stable visual surround. No subject could maintain stance without at least one Earth-stable sensory input. Improvement of mean sway scores in either group was not significant, although isolated cases did exhibit adaptive changes with improved stability over repeated trials.(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗
PubMed1980

A spring model and equivalent neural network for arm posture control.

A model is introduced for the motor program which controls final position. The first part of the model relates the biomechanical properties of the muscles to the EMG activities of the extensor and flexor muscles and thereby generates quantitative predictions for the relationships between the EMGs, final position, external forces, muscle stiffness, and muscle tension. To the extent that comparable data exist, the model is shown to give correct quantitative predictions. When only qualitative comparisons can be made, the model is consistent with the data in the literature. The model is complete and can be tested quantitatively in detail in the future. An equivalent circuit for the neural network that innervates the muscles is given. It is shown to have the advantages of making the programming of final position simple to either compute or look up in a table. In addition, new situations, such as adapting to a force, or an unusual viewing angle, lead to very simple changes in the basic program in terms of the equivalent circuit.

Arm↗
PubMed2002

Multisensory fusion and the stochastic structure of postural sway.

We analyze the stochastic structure of postural sway and demonstrate that this structure imposes important constraints on models of postural control. Linear stochastic models of various orders were fit to the center-of-mass trajectories of subjects during quiet stance in four sensory conditions: (i) light touch and vision, (ii) light touch, (iii) vision, and (iv) neither touch nor vision. For each subject and condition, the model of appropriate order was determined, and this model was characterized by the eigenvalues and coefficients of its autocovariance function. In most cases, postural-sway trajectories were similar to those produced by a third-order model with eigenvalues corresponding to a slow first-order decay plus a faster-decaying damped oscillation. The slow-decay fraction, which we define as the slow-decay autocovariance coefficient divided by the total variance, was usually near 1. We compare the stochastic structure of our data to two linear control-theory models: (i) a proportional-integral-derivative control model in which the postural system's state is assumed to be known, and (ii) an optimal-control model in which the system's state is estimated based on noisy multisensory information using a Kalman filter. Under certain assumptions, both models have eigenvalues consistent with our results. However, the slow-decay fraction predicted by both models is less than we observe. We show that our results are more consistent with a modification of the optimal-control model in which noise is added to the computations performed by the state estimator. This modified model has a slow-decay fraction near 1 in a parameter regime in which sensory information related to the body's velocity is more accurate than sensory information related to position and acceleration. These findings suggest that: (i) computation noise is responsible for much of the variance observed in postural sway, and (ii) the postural control system under the conditions tested resides in the regime of accurate velocity information.

Adult↗
PubMed1989

[Model studies of muscle postural control in children with spastic hemiparesis: pathophysiologic and clinical relevance].

By means of a biocybernetic method the transfer behaviour of postural motor control system was characterized in children with spastic hemiparesis and matched healthy subjects. There were significant differences between the initial reactions of the postural motor systems of both examination groups to pseudorandom torque sequences (fig. 3, tab. 1). Considering these results we created a postural motor control model for both examinations groups (model see fig. 2). From this model it is possible to estimate separately the biomechanical effect of augmented segmental reflex activity and muscle-inherent dysfunctions in patients with spastic syndrome (fig. 4). The determination of these disturbed functions enables us to give instructions on a focused treatment of spastic syndrome according to the dominant dysfunction quantified.

Brain Damage, Chronic↗
PubMed2005

Body sway during quiet standing: is it the residual chattering of an intermittent stabilization process?

This paper reviews different approaches for explaining body sway while quiet standing that directly address the instability of the human inverted pendulum. We argue that both stiffness control [Winter, D. A., Patla, A. E., Riedtyk, S., & Ishac, M. (2001). Ankle muscle stiffness in the control of balance during quiet standing. Journal of Neurophysiology, 85, 2630-2633] and continuous feedback control by means of a PID (Proportional, Integral, Derivative) mechanism [Peterka, R. J. (2000). Postural control model interpretation of stabilogram diffusion analysis. Biological Cybernetics, 83, 335-343.] can guarantee asymptotic stability of controlled posture at the expense of unrealistic assumptions: the level of intrinsic muscle stiffness in the former case, and the level of background noise in the latter, which also determines an unrealistic level of jerkiness in the sway. We show that the decomposition of the control action into a slow and a fast component (rambling and trembling, respectively, as proposed by [Zatsiorsky, V. M., & Duarte, M. (1999). Instant equilibrium point and its migration in standing tasks: Rambling and trembling components of the stabilogram. Motor Control, 4, 185-200; Zatsiorsky, V. M., & Duarte, M. (2000). Rambling and trembling in quiet standing. Motor Control, 4, 185-200.]) is useful but must be modified in order to take into account that rambling is not a stable equilibrium trajectory. We address the possibility of a form of stability weaker than asymptotic stability in light of the intermittent stabilization mechanism outlined by [Loram, I. D., & Lakie, M. (2002a). Human balancing of an inverted pendulum: position control by small, ballistic-like, throw and catch movements. Journal of Physiology, 540, 1111-1124.], and propose an indicator of intermittent stabilization that is related to the phase portrait of the human inverted pendulum. This indicator provides a further argument against the plausibility of PID-like control mechanisms. Finally, we draw attention to the sliding mode control theory that provides a useful theoretical framework for formulating realistic intermittent, stabilization models.

Adult↗
PubMed2005

Human postural responses to motion of real and virtual visual environments under different support base conditions.

The role of visual orientation cues for human control of upright stance is still not well understood. We, therefore, investigated stance control during motion of a visual scene as stimulus, varying the stimulus parameters and the contribution from other senses (vestibular and leg proprioceptive cues present or absent). Eight normal subjects and three patients with chronic bilateral loss of vestibular function participated. They stood on a motion platform inside a cabin with an optokinetic pattern on its interior walls. The cabin was sinusoidally rotated in anterior-posterior (a-p) direction with the horizontal rotation axis through the ankle joints (f=0.05-0.4 Hz; A (max)=0.25 degrees -4 degrees ; v (max)=0.08-10 degrees /s). The subjects' centre of mass (COM) angular position was calculated from opto-electronically measured body sway parameters. The platform was either kept stationary or moved by coupling its position 1:1 to a-p hip position ('body sway referenced', BSR, platform condition), by which proprioceptive feedback of ankle joint angle became inactivated. The visual stimulus evoked in-phase COM excursions (visual responses) in all subjects. (1) In normal subjects on a stationary platform, the visual responses showed saturation with both increasing velocity and displacement of the visual stimulus. The saturation showed up abruptly when visually evoked COM velocity and displacement reached approximately 0.1 degrees /s and 0.1 degrees , respectively. (2) In normal subjects on a BSR platform (proprioceptive feedback disabled), the visual responses showed similar saturation characteristics, but at clearly higher COM velocity and displacement values ( approximately 1 degrees /s and 1 degrees , respectively). (3) In patients on a stationary platform (no vestibular cues), the visual responses were basically similar to those of the normal subjects, apart from somewhat higher gain values and less-pronounced saturation effects. (4) In patients on a BSR platform (no vestibular and proprioceptive cues, presumably only somatosensory graviceptive and visual cues), the visual responses showed an abnormal increase in gain with increasing stimulus frequency in addition to a displacement saturation. On the normal subjects we performed additional experiments in which we varied the gain of the visual response by using a 'virtual reality' visual stimulus or by applying small lateral platform tilts. This did not affect the saturation characteristics of the visual response to a considerable degree. We compared the present results to previous psychophysical findings on motion perception, noting similarities of the saturation characteristics in (1) with leg proprioceptive detection thresholds of approximately 0.1 degrees /s and 0.1 degrees and those in (2) with vestibular detection thresholds of 1 degrees /s and 1 degrees , respectively. From the psychophysical data one might hypothesise that a proprioceptive postural mechanism limits the visually evoked body excursions if these excursions exceed 0.1 degrees /s and 0.1 degrees in condition (1) and that a vestibular mechanism is doing so at 1 degrees /s and 1 degrees in (2). To better understand this, we performed computer simulations using a posture control model with multiple sensory feedbacks. We had recently designed the model to describe postural responses to body pull and platform tilt stimuli. Here, we added a visual input and adjusted its gain to fit the simulated data to the experimental data. The saturation characteristics of the visual responses of the normals were well mimicked by the simulations. They were caused by central thresholds of proprioceptive, vestibular and somatosensory signals in the model, which, however, differed from the psychophysical thresholds. Yet, we demonstrate in a theoretical approach that for condition (1) the model can be made monomodal proprioceptive with the psychophysical 0.1 degrees /s and 0.1 degrees thresholds, and for (2) monomodal vestibular with the psychophysical 1 degrees /s and 1 degrees thresholds, and still shows the corresponding saturation characteristics (whereas our original model covers both conditions without adjustments). The model simulations also predicted the almost normal visual responses of patients on a stationary platform and their clearly abnormal responses on a BSR platform.

Adult↗