Design of virtual reality augmented reality mobile platform and game user behavior monitoring using deep learning

Abstract

To explore the design of the virtual reality (VR) augmented reality (AR) mobile platform and game decision model based on deep learning (DL), the gesture-based interaction of VR games based on Leap Motion is researched. Based on the interactive features of gestures, a set of general gesture interaction rules in VR games is established. In the meantime, according to the theoretical basis and the characteristics of VR, a set of general models of VR gesture interaction is designed, the factors affecting the efficiency of VR gesture interaction are studied, and reasonable interaction feedback is designed. By using the computer vision and image processing technology, gesture-based interaction can collect natural gestures, extract gesture features, recognize gesture indications, and respond to the user demands. Also, it can extract the basic gestures from gesture-based interaction in VR, analyze the basic features of gestures and gesture-based interaction in VR games, and describes the gesture features by mathematical vectors and sets. The research results show that the application of gesture feature design method in the game can analyze the factors affecting the interaction efficiency. Also, the usability of the gesture-based interaction designed by the gesture design method is verified by tests. Therefore, the AR&DL platform of “AR+DL” establishes a learning platform supported by DL and AR technology. The game decision model is used to describe the process of gesture-based interaction in the game, and the factors affecting the interaction efficiency are reduced, which has certain reference and guidance for VR applications using gesture-based interaction.