Application of Knowledge Model in Dance Teaching Based on Wearable Device Based on Deep Learning

Abstract

With the great changes in the social, economic, and cultural background, the traditional education model can no longer meet the current goal of cultivating dance talents. The application of wearable devices based on deep learning helps to improve students’ understanding and application of dance movements. Through extensive data analysis, experimental research, and kinesthetic theory, it is found that trainers based on deep neural networks can effectively improve students’ overall learning performance. At the same time, the application of emotion-intelligence teaching mode theory in dance experiments and the conclusions drawn from the experimental research fully demonstrate the teaching advantages of applying deep learning wearable devices to dance emotion-intelligence teaching mode. In order to explore the application of wearable devices based on deep learning in dance teaching, this paper discusses the improvement of deep learning through the elaboration of parameter smoothing initialization, convolutional pooling layer, optimal smoothing filter, and dynamic pruning method, and then a wearable device is designed. A device can recognize dance movements to verify the application of deep learning-based wearables in dance teaching in emotional mode.