Adaptive navigation assistance based on eye movement features in virtual reality - Song Zhao; Shiwei Cheng (Virtual Reality & Intelligent Hardware, 2023)
Background: Navigation assistance is very important for users when roaming in virtual reality scenes, however, the traditional navigation method requires users to manually request a map for viewing, which leads to low immersion and poor user experience. Methods: To address this issue, first, we collected data when users need navigation assistance in a virtual reality environment, including various eye movement features such as gaze fixation, pupil size, and gaze angle, etc. After that, we used the Boostingbased XGBoost algorithm to train a prediction model, and finally used it to predict whether users need navigation assistance in a roaming task. Results: After evaluating the performance of the model, the accuracy, precision, recall, and F1-score of our model reached about 95%. In addition, by applying the model to a virtual reality scene, an adaptive navigation assistance system based on the user’s real-time eye movement data was implemented. Conclusions: Compared with traditional navigation assistance methods, our new adaptive navigation assistance could enable the user to be more immersive and effective during roaming in VR environment.