Stance width changes how sensory feedback is used for multisegmental balance control

Abstract

A multilink sensorimotor integration model of frontal plane balance control was developed to determine how stance width influences the use of sensory feedback in healthy adults. Data used to estimate model parameters came from seven human participants who stood on a continuously rotating surface with three different stimulus amplitudes, with eyes open and closed, and at four different stance widths. Dependent variables included lower body (LB) and upper body (UB) sway quantified by frequency-response functions. Results showed that stance width had a major influence on how parameters varied across stimulus amplitude and between visual conditions. Active mechanisms dominated LB control. At narrower stances, with increasing stimulus amplitude, subjects used sensory reweighting to shift reliance from proprioceptive cues to vestibular and/or visual cues that oriented the LB more toward upright. When vision was available, subjects reduced reliance on proprioception and increased reliance on vision. At wider stances, LB control did not exhibit sensory reweighting. In the UB system, both active and passive mechanisms contributed and were dependent on stance width. UB control changed across stimulus amplitude most in wide stance (opposite of the pattern found in LB control). The strong influence of stance width on sensory integration and neural feedback control implies that rehabilitative therapies for balance disorders can target different aspects of balance control by using different stance widths. Rehabilitative strategies designed to assess or modify sensory reweighting will be most effective with the use of narrower stances, whereas wider stances present greater challenges to UB control.

Keywords: balance; frontal plane; sensorimotor model; sensory feedback; stance width.

Copyright © 2014 the American Physiological Society.

Figures

Fig. 1.

Model predictions of frequency-response functions (FRFs) in eyes closed (EC) conditions. Symbols represent experimental data, and lines are the model-predicted FRFs. Model FRFs are from the fits to across-subject mean experimental FRFs.

Fig. 2.

Model predictions of FRFs in eyes open (EO) conditions. Symbols represent experimental data, and lines are the model-predicted FRFs. Model FRFs are from the fits to across-subject mean experimental FRFs.

Fig. 3.

Block diagrams representing lower body (LB; A) and upper body (UB; B) feedback control mechanisms that generate corrective torques as a function of sensory signals encoding joint motion, segment orientation, or muscle torque (active torque, TLact and TUact) or as a function of passive mechanics associated with muscle/tendon stretch (passive torque, TLpas and TUpas). In the block diagrams, physical segment kinematic variables are represented with thick solid black lines, sensory signals encoding segment kinematics or torques are represented as dashed lines, and joint torques are thin solid black lines. θU is the UB angle, θL is the LB angle, θP is the pelvis angle, θS is the surface angle, TU is UB torque, and TL is LB torque. The active mechanisms include a time delay and a set of neural control parameters that specify the transformation from sensory signals to corrective torque. Across all stimulus amplitudes and stance widths, the control parameters, identified from fits to the across-subject mean FRFs, are shown for EC LB control (C), EC UB control (D), EO LB control (E), and EO UB control (F). Plot symbols used in C and E are associated with sensory and mechanical signals defined in the block diagram of the LB control mechanism shown in A, and symbols used in D and F are defined in the block diagram of the UB control mechanism shown in B. Stiffness parameters generate torque in proportion to angular deviations, and damping parameters generate torque in proportion to angular velocity.

Fig. 4.

Model feedback (FB) parameters as a function of frontal plane stance width. At each stance width, results shown are the average of parameter values across visual conditions and across stimulus amplitudes. All parameters values are from fits to across-subject mean FRFs. Nar, narrow stance; Par, parallel stance; Med, medium stance; Wide, wide stance. See text and Table 1 for parameter definitions.

Fig. 5.

Comparison of EO and EC sensory feedback parameters. EO and EC parameters were averaged across the 3 stimulus amplitudes for each of the 4 stance widths. All results are based on parameters from fits to across-subject mean FRFs. Plots show EO minus EC parameter values for the LB (left) and UB parameters (right). Values greater than zero indicate that parameters were greater in EO compared with EC conditions.

Fig. 6.

Sensitivity analysis characterizing how variations in model parameters influence LB and UB FRFs in the EC condition. A: summary bar plots of sensitivities of FRFs to control parameter variations were obtained by calculating an average mean square error (MSE) value at each stance width when parameters were varied ±5% from their nominal values (see methods). B: the frequency dependence of sensitivities to parameter variation is illustrated for 2 LB-to-surface proprioception feedback parameters, KLsl and BLsl. MSE vs. frequency plots are based on ±5% parameter variations relative to the nominal parameter value followed by averaging across MSE values from all stimulus amplitudes.