Gesture recognition and information recommendation based on machine learning and virtual reality in distance education

Abstract

The dynamic and static gesture recognition in the distance education application scenario is not mature enough in theory at present, and still has a large space for development, and the application of gesture recognition in education is relatively insufficient. The purpose of this article is to combine gesture recognition with teacher classroom education and introduce a dynamic gesture recognition method. Moreover, this study introduces the data collection and preprocessing in detail and converts the data of the gesture action area into gray value images, and then uses the improved algorithm to perform classification. In addition, this study designs a control experiment to analyze the performance of the algorithm in this study and compares the accuracy of algorithm recognition from the perspective of simple background and complex background. The research results show that teaching gesture recognition in distance education can effectively improve education efficiency, with high accuracy, and can be directly applied to the system.