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Non-linear stimulus-response behavior of the human stance control system is predicted by optimization of a system with sensory and motor noise.

van der Kooij H, Peterka RJ - J Comput Neurosci (2010)

Bottom Line: Different combinations of internal sensory and/or motor noise sources were added to the model to identify the properties of noise sources that were able to account for the experimental remnant sway characteristics.Robust findings were that remnant sway characteristics were best predicted by models that included both sensory and motor noise, the graviceptive noise magnitude was about ten times larger than the proprioceptive noise, and noise sources with signal-dependent properties provided better explanations of remnant sway.Overall results indicate that humans dynamically weight sensory system contributions to stance control and tune their corrective responses to minimize the energetic effects of sensory noise and external stimuli.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomechanical Engineering, University of Twente, 7500 AE, Enschede, The Netherlands. H.vanderKooij@utwente.nl

ABSTRACT
We developed a theory of human stance control that predicted (1) how subjects re-weight their utilization of proprioceptive and graviceptive orientation information in experiments where eyes closed stance was perturbed by surface-tilt stimuli with different amplitudes, (2) the experimentally observed increase in body sway variability (i.e. the "remnant" body sway that could not be attributed to the stimulus) with increasing surface-tilt amplitude, (3) neural controller feedback gains that determine the amount of corrective torque generated in relation to sensory cues signaling body orientation, and (4) the magnitude and structure of spontaneous body sway. Responses to surface-tilt perturbations with different amplitudes were interpreted using a feedback control model to determine control parameters and changes in these parameters with stimulus amplitude. Different combinations of internal sensory and/or motor noise sources were added to the model to identify the properties of noise sources that were able to account for the experimental remnant sway characteristics. Various behavioral criteria were investigated to determine if optimization of these criteria could predict the identified model parameters and amplitude-dependent parameter changes. Robust findings were that remnant sway characteristics were best predicted by models that included both sensory and motor noise, the graviceptive noise magnitude was about ten times larger than the proprioceptive noise, and noise sources with signal-dependent properties provided better explanations of remnant sway. Overall results indicate that humans dynamically weight sensory system contributions to stance control and tune their corrective responses to minimize the energetic effects of sensory noise and external stimuli.

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Results of the Stage 4 analysis. The shaded areas indicate the derivative (combination of neural controller kd and intrinsic damping bi) and proportional (combination of neural controller kp and intrinsic stiffness ki) gains for which the stance control model was stable. Both the derivative and proportional gains are normalized by the gravitational stiffness mgh. The area of stable operation decreases when the time delay increases. The square symbol denotes the gains derived from the Stage 1 analysis of experimental data. The other symbols indicate the neural controller plus intrinsic visco-elastic gains predicted by minimizing different behavioral criteria using the S1M3 remnant noise model. The experimentally derived gains are in between the predictions made by minimization of torque and velocity criteria
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Fig7: Results of the Stage 4 analysis. The shaded areas indicate the derivative (combination of neural controller kd and intrinsic damping bi) and proportional (combination of neural controller kp and intrinsic stiffness ki) gains for which the stance control model was stable. Both the derivative and proportional gains are normalized by the gravitational stiffness mgh. The area of stable operation decreases when the time delay increases. The square symbol denotes the gains derived from the Stage 1 analysis of experimental data. The other symbols indicate the neural controller plus intrinsic visco-elastic gains predicted by minimizing different behavioral criteria using the S1M3 remnant noise model. The experimentally derived gains are in between the predictions made by minimization of torque and velocity criteria

Mentions: For the data used in the current study, all tests were performed with subjects using a backboard assembly that constrained their body mechanics to be that of a single-link inverted pendulum. Results from the previous study demonstrated that use of the backboard did not alter postural dynamics in this particular experiment (Fig. 7 in (Peterka 2002)). The mass, moment of inertia, and CoM height of the backboard assembly were taken into consideration in all of our modeling efforts.


Non-linear stimulus-response behavior of the human stance control system is predicted by optimization of a system with sensory and motor noise.

van der Kooij H, Peterka RJ - J Comput Neurosci (2010)

Results of the Stage 4 analysis. The shaded areas indicate the derivative (combination of neural controller kd and intrinsic damping bi) and proportional (combination of neural controller kp and intrinsic stiffness ki) gains for which the stance control model was stable. Both the derivative and proportional gains are normalized by the gravitational stiffness mgh. The area of stable operation decreases when the time delay increases. The square symbol denotes the gains derived from the Stage 1 analysis of experimental data. The other symbols indicate the neural controller plus intrinsic visco-elastic gains predicted by minimizing different behavioral criteria using the S1M3 remnant noise model. The experimentally derived gains are in between the predictions made by minimization of torque and velocity criteria
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3108015&req=5

Fig7: Results of the Stage 4 analysis. The shaded areas indicate the derivative (combination of neural controller kd and intrinsic damping bi) and proportional (combination of neural controller kp and intrinsic stiffness ki) gains for which the stance control model was stable. Both the derivative and proportional gains are normalized by the gravitational stiffness mgh. The area of stable operation decreases when the time delay increases. The square symbol denotes the gains derived from the Stage 1 analysis of experimental data. The other symbols indicate the neural controller plus intrinsic visco-elastic gains predicted by minimizing different behavioral criteria using the S1M3 remnant noise model. The experimentally derived gains are in between the predictions made by minimization of torque and velocity criteria
Mentions: For the data used in the current study, all tests were performed with subjects using a backboard assembly that constrained their body mechanics to be that of a single-link inverted pendulum. Results from the previous study demonstrated that use of the backboard did not alter postural dynamics in this particular experiment (Fig. 7 in (Peterka 2002)). The mass, moment of inertia, and CoM height of the backboard assembly were taken into consideration in all of our modeling efforts.

Bottom Line: Different combinations of internal sensory and/or motor noise sources were added to the model to identify the properties of noise sources that were able to account for the experimental remnant sway characteristics.Robust findings were that remnant sway characteristics were best predicted by models that included both sensory and motor noise, the graviceptive noise magnitude was about ten times larger than the proprioceptive noise, and noise sources with signal-dependent properties provided better explanations of remnant sway.Overall results indicate that humans dynamically weight sensory system contributions to stance control and tune their corrective responses to minimize the energetic effects of sensory noise and external stimuli.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomechanical Engineering, University of Twente, 7500 AE, Enschede, The Netherlands. H.vanderKooij@utwente.nl

ABSTRACT
We developed a theory of human stance control that predicted (1) how subjects re-weight their utilization of proprioceptive and graviceptive orientation information in experiments where eyes closed stance was perturbed by surface-tilt stimuli with different amplitudes, (2) the experimentally observed increase in body sway variability (i.e. the "remnant" body sway that could not be attributed to the stimulus) with increasing surface-tilt amplitude, (3) neural controller feedback gains that determine the amount of corrective torque generated in relation to sensory cues signaling body orientation, and (4) the magnitude and structure of spontaneous body sway. Responses to surface-tilt perturbations with different amplitudes were interpreted using a feedback control model to determine control parameters and changes in these parameters with stimulus amplitude. Different combinations of internal sensory and/or motor noise sources were added to the model to identify the properties of noise sources that were able to account for the experimental remnant sway characteristics. Various behavioral criteria were investigated to determine if optimization of these criteria could predict the identified model parameters and amplitude-dependent parameter changes. Robust findings were that remnant sway characteristics were best predicted by models that included both sensory and motor noise, the graviceptive noise magnitude was about ten times larger than the proprioceptive noise, and noise sources with signal-dependent properties provided better explanations of remnant sway. Overall results indicate that humans dynamically weight sensory system contributions to stance control and tune their corrective responses to minimize the energetic effects of sensory noise and external stimuli.

Show MeSH
Related in: MedlinePlus