Design and Implementation of Virtual Reality Interactive Product Software Based on Artificial Intelligence Deep Learning Algorithm

The aim of this study is to improve the interactive needs of artificial intelligence in the virtual reality environment. Based on the in-depth study of the interactive needs of virtual reality, a virtual reality interactive glove based on nine-axis inertial sensor and realized by artificial intelligence deep learning algorithm is designed. The AI deep learning algorithms employed include the KNN, SVM, Fuzzy, PNN, and DTW algorithms. Static gesture recognition is relatively simple, dynamic gesture recognition needs to a dynamic real-time gesture sequence data starting point and end point planning, by building the directed graph structure, quickly retrieving the global optimal solution, and determining gesture starting point, with dynamic planning to solve the minimum distance between two points, avoid the graph, and improve efficiency. The results showed that by 50 gestures such as select object, attract object, zoom object, rotate object, shoot small box, exhale menu, and close the menu, the recognition rate is 100%, 94%, 96%, 100%, 92%, 100%, and 100%. The motion data of finger and palm are captured by nine axis sensor, and the gesture recognition is carried out by using artificial intelligence deep learning algorithm. It is proved that the artificial intelligence deep learning algorithm can effectively realize the design of virtual reality interactive product software.

Automated summary

The aim of this study is to enhance the interactive capabilities of artificial intelligence in virtual reality. A virtual reality interactive glove based on a nine-axis inertial sensor and powered by AI deep learning algorithms has been developed. The study employs KNN, SVM, Fuzzy, PNN, and DTW algorithms. The results demonstrate high recognition rates for various gestures, with some achieving 100% accuracy.