A Computational Visual-Vestibular Balance Control Model with Peripheral Visual Input

Abstract

 

Purpose

 

Demonstrate that longer visual processing delays in glaucoma patients can explain greater postural instability.

 

Methods

 

Glaucoma and healthy patients were exposed to rotating peripheral visual stimuli through an Oculus Rift while standing on a force platform that recorded their postural sway. Postural response was recorded for three conditions: 1) a dark visual field which no visual information can be used to stabilize posture, 2) a static field that can be used to stabilize posture, 3) a peripheral stimulus rotating around the patient’s feet that perturbs the patient visually in the mediolateral direction and elicits vection. A computational model (Figure 1) capable of controlling posture under these test conditions was developed; it includes: 1) mechanism that perturbs the participant’s sense of visual vertical, 2) mechanism that generates a vection signal, 3) visual delay that impacts the perception of angular and angular-rate errors between perceived visual vertical and angular body movements, 4) neuromuscular limitations, 5) inverted pendulum model of the human body, 6) vestibular feedback, and 7) a controller that takes perceived errors in body angle, angle rate, and angular acceleration to generate a muscle control command.

 

Results

 

The study included 42 glaucoma and 38 healthy subjects. Glaucoma patients had significantly higher torque moment standard deviations for rotational stimulus (2.55 Nm vs. 1.69 Nm). The model was used to determine if a difference in only visual processing delay could explain this result. All other mechanisms were kept equal across the patient groups. The control gains of the model were optimized per visual processing delay to minimize the standard deviation of sway to account for the fact that people with different delays adopt different control gains but assumes that all people try to minimize body sway. The model is indeed capable of reproducing the 50% greater body sway by assuming a 50% longer visual delay.

 

Conclusions

 

The study demonstrates that control-theoretic techniques to model postural balance control offer useful insight into the effect that changes in visual processing delay have on people’s ability to control body sway under static and dynamic visual conditions. These insights may help provide a better understanding of the impact that degraded visual performance can have on daily tasks such as postural control.  

 

Figure 1. Postural balance control model.