Biomedical subjects

Kelvin S Oie

Publications and source records attributed to Kelvin S Oie.

4 recordsLinked to original sources

PubMed2005

Slow dynamics of postural sway are in the feedback loop.

Postural sway is considered to have two fundamental stochastic components, a slow nonoscillatory component and a faster damped-oscillatory component. The slow component has been shown to account for the majority of sway variance during quiet stance. Postural control is generally viewed as a feedback loop in which sway is detected by sensory systems and appropriate motor commands are generated to stabilize the body's orientation. Whereas the mechanistic source for the damped-oscillatory sway component is most likely feedback control of an inverted pendulum, the underlying basis for the slow component is less clear. We investigated whether the slow process was inside or outside the feedback loop by providing standing subjects with sum-of-sines visual motion. Linear stochastic models were fit to the experimental sway trajectories to determine the stochastic structure of sway as well as the transfer function from visual motion to sway. The results supported a fifth-order stochastic model, consisting of a slow process and two damped-oscillatory components. Importantly, the slow process was determined to be inside the feedback loop. This supports the hypothesis that the slow component is due to errors in state estimation because state estimation is inside the feedback loop rather than a moving reference point or an exploratory process outside the feedback loop.

Adult↗

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↗

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↗

PubMed2002

Multisensory fusion: simultaneous re-weighting of vision and touch for the control of human posture.

We examined the generally held belief that the postural control system is able to re-weight its available sensory inputs in order to optimize stance control in altered sensory environments. Our view is that previous accounts of sensory re-weighting provide only indirect evidence, which is subject to alternative explanations. The present results provide strong evidence for sensory re-weighting as the primary mechanism for changes observed in postural sway between conditions. Subjects were presented with small-amplitude, oscillatory visual and somatosensory stimuli at 0.20 and 0.28 Hz, respectively, in five conditions that manipulated the amplitudes of stimulus motion. Gain calculated in each trial with respect to each of the two stimuli was found to change systematically as stimulus motion amplitudes changed across condition. The observed pattern of gain rules out a constant-weight, linear account of posture and is consistent with the re-weighting hypothesis. Parameter fits of a third-order, linear stochastic model to postural sway trajectories in each condition showed that changes in gain across condition were primarily due to changes in coupling coefficients rather than changes in parameters that characterize the stability of the postural system. Visual gain was found to depend upon visual motion amplitude and touch gain was found to depend upon touch motion amplitude, indicating intra-modality dependencies. Visual gain also depended upon touch motion amplitude, indicating an inter-modality dependence. To our knowledge, simultaneous re-weighting of more than one sensory input has never been rigorously demonstrated. These techniques may be able to resolve the source of balance control deficits across populations with far more certainty than currently possible.

Adult↗