Prediction and Assistance of Navigation Demand Based on Eye Tracking in Virtual Reality Environment

In order to solve the problems of insufficient user support and low user immersion in traditional navigation methods in complex virtual reality scenes, this paper proposes a binary classification model based on the gradient boost decision tree,which uses eye movement data before and after the user's need for auxiliary navigation in the VR environment to analyze and predict whether the user needs navigation during the task.The model is evaluated according to the user's gaze sequence,and the average precision and accuracy of the user demand judgment method are 77.6% and 77.2%,respectively.In addition,we implemente a navigation aid prototype system.By classifying the user's navigation requirement based on eye movement data,the user interface of the prototype system can automatically present the map for navigation.Experimental results showed that,compared with the traditional permanent assisted navigation method,the adaptive assisted navigation proposed in this paper can provide better user experience.

Abstract licence: CC BY-NC-ND

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