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.

中文翻译：

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基于自适应BP神经网络的眼动预测

本文使用自适应BP神经网络对阅读过程中的眼球运动进行深入检查，并预测阅读效果。实施视觉跟踪系统的一个重要组成部分是使用实际数据或真实世界数据集正确检测眼球运动。我们建议使用基于自适应 BP 神经网络的眼动识别算法来识别三种典型的眼动类型，即凝视、跳跃和平滑导航。本研究使用眼动跟踪传感器评估 BP 神经网络算法。对于实验环境，从 10 位被试中采集了四种类型的眼动信号，对采集到的信号进行了初步处理。