Nonparametric identification of wrestlers' eye movements using a differential neural network

Abstract

Nowadays there are different multiple modern technologies that allow studying cor-relates of development of sportsmen’s professional qualities. Eye tracking is one of such technologies, widely used in sport practice. The article demonstrates application of eye tracking to the task of testing wrestling athletes using a virtual environment demonstra-tion in a HTC Vive Pro Eye headset. Built-in eye-tracking systems of VR headsets often suffer from a low sampling rates and possible pupil loss during recording. Filtering can be applied in order to combat these effects, for example Kalman and similar filters. In this case, an adequate mathematical model in the state space is required. Usually a parametric or nonparametric model of the system in question is created. If dynamics of the model is known, the problem is usually reduced to identification of system parameters. It is not always possible to give an adequate mathematical description of the processes occur-ring in the system, or the system itself can be represented as a "black box". The oculomo-tor system may also be described as one such system. In such cases, nonparametric identification is applicable, that is, identification of the dynamics of the system. This pa-per proposes to identify the dynamics of the system using differential neural networks. They have good approximating capabilities for non-linear systems. The standard sig-moidal activation function was replaced by the Izhikevich activation function described by differential equations. The result of the work of the neural network identifier was an approximate system describing the dynamics of the ocular movement. Computational modeling was carried out. The workability of the model is studied on several data sets obtained by recording the oculomotor reaction of athletes-wrestlers to visual stimuli in a virtual environment, showing the effectiveness of the neural network learning laws.