Eye Movement Prediction Based on Adaptive BP Neural Network

Jianhuan Su,Yushou Tang,Mian Ahmad Jan

doi:10.1155/2021/4977620

Abstract

This paper uses adaptive BP neural networks to conduct an in-depth examination of eye movements during reading and to predict reading effects. An important component for the implementation of visual tracking systems is the correct detection of eye movement using the actual data or real-world datasets. We propose the identification of three typical types of eye movements, namely, gaze, leap, and smooth navigation, using an adaptive BP neural network-based recognition algorithm for eye movement. This study assesses the BP neural network algorithm using the eye movement tracking sensors. For the experimental environment, four types of eye movement signals were acquired from 10 subjects to perform preliminary processing of the acquired signals. The experimental results demonstrate that the recognition rate of the algorithm provided in this paper can reach up to 97%, which is superior to the commonly used CNN algorithm.

Highlights

With the rapid advancement of artificial intelligence, individuals began to utilize machines to detect users’ emotional states, and the machines were required to provide feedback based on human emotions, a mechanism known as the human-computer interaction
Eye movement signals have the benefits of large amplitude, easy waveform identification, and easy processing when compared to other bioelectric signals, providing more reliable and convenient circumstances for collecting eye movement information [3]
This paper proposes an adaptive BP algorithm, which mainly solves the problem of different lengths of eye movement information and substantially improves the recognition rate of eye movement signals, laying a good foundation for future human-computer interaction systems [5]

Summary

Introduction

With the rapid advancement of artificial intelligence, individuals began to utilize machines to detect users’ emotional states, and the machines were required to provide feedback based on human emotions, a mechanism known as the human-computer interaction. This paper proposes an adaptive BP algorithm, which mainly solves the problem of different lengths of eye movement information and substantially improves the recognition rate of eye movement signals, laying a good foundation for future human-computer interaction systems [5]. Is study focuses on a combination of single and multiframe human eye tracking enhanced algorithms with radial blurring. E shading component, optimization, and characteristics of the radial rendering methodology are explored, the comparison with classic blurring effects is made, and the combination of single and multiframe human eye tracking improved algorithm with radial blurring is studied. E shading component, optimization, and characteristics of the radial rendering technique are explored, as well as the comparison with standard blurring effects and the integration of single and multiframe human eye tracking enhanced algorithms with radial blurring.

Literature Review

Adaptive BP Neural Network for Reading Eye Movement Prediction Analysis

Analysis of Results