It is generally accepted that human bipedal upright stance is achieved by feedback mechanisms that generate an appropriate corrective torque based on body-sway motion detected primarily by visual, vestibular, and proprioceptive sensory systems. Because orientation information from the various senses is not always available (eyes closed) or accurate (compliant support surface), the postural control system must somehow adjust to maintain stance in a wide variety of environmental conditions. This is the sensorimotor integration problem that we investigated by evoking anterior-posterior (AP) body sway using pseudorandom rotation of the visual surround and/or support surface (amplitudes 0.5-8 degrees ) in both normal subjects and subjects with severe bilateral vestibular loss (VL). AP rotation of body center-of-mass (COM) was measured in response to six conditions offering different combinations of available sensory information. Stimulus-response data were analyzed using spectral analysis to compute transfer functions and coherence functions over a frequency range from 0.017 to 2.23 Hz. Stimulus-response data were quite linear for any given condition and amplitude. However, overall behavior in normal subjects was nonlinear because gain decreased and phase functions sometimes changed with increasing stimulus amplitude. "Sensory channel reweighting" could account for this nonlinear behavior with subjects showing increasing reliance on vestibular cues as stimulus amplitudes increased. VL subjects could not perform this reweighting, and their stimulus-response behavior remained quite linear. Transfer function curve fits based on a simple feedback control model provided estimates of postural stiffness, damping, and feedback time delay. There were only small changes in these parameters with increasing visual stimulus amplitude. However, stiffness increased as much as 60% with increasing support surface amplitude. To maintain postural stability and avoid resonant behavior, an increase in stiffness should be accompanied by a corresponding increase in damping. Increased damping was achieved primarily by decreasing the apparent time delay of feedback control rather than by changing the damping coefficient (i.e., corrective torque related to body-sway velocity). In normal subjects, stiffness and damping were highly correlated with body mass and moment of inertia, with stiffness always about 1/3 larger than necessary to resist the destabilizing torque due to gravity. The stiffness parameter in some VL subjects was larger compared with normal subjects, suggesting that they may use increased stiffness to help compensate for their loss. Overall results show that the simple act of standing quietly depends on a remarkably complex sensorimotor control system.

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.

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.

Because sensory systems often provide ambiguous information, neural processes must exist to resolve these ambiguities. It is likely that similar neural processes are used by different sensory systems. For example, many tasks require neural processing to distinguish linear acceleration from gravity, but Einstein's equivalence principle states that all linear accelerometers must measure both linear acceleration and gravity. Here we investigate whether the brain uses internal models, defined as neural systems that mimic physical principles, to help estimate linear acceleration and gravity. Internal models may be used in motor contro, sensorimotor integration and sensory processing, but direct experimental evidence for such models is limited. To determine how humans process ambiguous gravity and linear acceleration cues, subjects were tilted after being rotated at a constant velocity about an Earth-vertical axis. We show that the eye movements evoked by this post-rotational tilt include a response component that compensates for the estimated linear acceleration even when no actual linear acceleration occurs. These measured responses are consistent with our internal model predictions that the nervous system can develop a non-zero estimate of linear acceleration even when no true linear acceleration is present.

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.