Emulating Balance Control Observed in Human Test Subjects with a Neural Network

• Main Author: Hilts, Wade William
• Format: Others
• Published: PDXScholar 2018
• Subjects: Neural networks (Computer science); Equilibrium (Physiology); Human locomotion; Mechanical Engineering
• Online Access: https://pdxscholar.library.pdx.edu/open_access_etds/4499; https://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi?article=5570&context=open_access_etds

Human balance control is a complex feedback system that must be adaptable and robust in an infinitely varying external environment. It is probable that there are many concurrent control loops occurring in the central nervous system that achieve stability for a variety of postural perturbations. Though many engineering models of human balance control have been tested, no models of how these controllers might operate within the nervous system have yet been developed. We have focused on building a model of a proprioceptive feedback loop with simulated neurons. The proprioceptive referenced portion of human balance control has been successfully modeled by a PD controller with a time delay and output torque positive feedback. For this model, angular position is measured at the ankle and corrective torque is applied about the joint to maintain a vertical orientation. In this paper, we construct a neural network that performs addition, subtraction, multiplication, differentiation and signal filtering to demonstrate that a simulated biological neural system based off of the engineering control model is capable of matching human test subject dynamics.