Mathematical modeling and grey-box identification of the human smooth pursuit mechanism

A mathematical model of the human eye smooth pursuit mechanism was constructed by combining a fourth order nonlinear biomechanical model of the eye plant with a dynamic gain controller model. The biomechanical model was derived based on knowledge of the anatomical properties and characteristics of the extraocular motor system. The controller model structure was chosen empirically to agree with experimental data. With the parameters of the eye plant obtained from the literature, the controller parameters were estimated through grey-box identification. Randomly generated and smoothly moving visual stimuli projected on a computer monitor were used as input data while the output data were the resulting eye movements of test subjects tracking the stimuli. The model was evaluated in terms of accuracy in reproducing eye movements registered over time periods longer than 10 seconds, frequency characteristics and angular velocity step responses. It was found to perform better than earlier models for the extended time data sets used in this study.