The Feasibility of a Virtual Reality System for Attention Analysis
Bin Ji, Zhiyong Wang, Kai Jun Xu, Honghai Liu · 2019
Attention is considered one of the most specific cognitive processes in human consciousness activities. The detection of attention depends on the well-designed experimental scenes in laboratories with single visual information which greatly limits the application in practice. Taking advantage of the virtual scenarios where features are able to be acquired, this study proposes a novel and effective virtual reality (VR) system to analyze the attention with multiple features. Ten participants were invited to the experiment and the real-time gaze data and operational information were recorded during the process. The Gaussian Mixture Model (GMM) and Dynamic Time Warping (DTW) were employed to analyze the gaze data and real-time operating information in the game to describe the attention preference and continuous attention. The experiment result presents that behaviors of distracted participants in these two experiments have a strong correlation. Besides, the proposed method demonstrates the feasibility of the VR system to analyze attention with multiple types of features.