Interaction and psychological characteristics of art teaching based on Openpose and Long Short-Term Memory network

Abstract

As living standards improve, people’s demand for appreciation and learning of art is growing gradually. Unlike the traditional learning model, art teaching requires a specific understanding of learners’ psychology and controlling what they have learned so that they can create new ideas. This article combines the current deep learning technology with heart rate to complete the action recognition of art dance teaching. The video data processing and recognition are conducted through the Openpose network and graph convolution network. The heart rate data recognition is completed through the Long Short-Term Memory (LSTM) network. The optimal recognition model is established through the data fusion of the two decision levels through the adaptive weight analysis method. The experimental results show that the accuracy of the classification fusion model is better than that of the single-mode recognition method, which is improved from 85.0% to 97.5%. The proposed method can evaluate the heart rate while ensuring high accuracy recognition. The proposed research can help analyze dance teaching and provide a new idea for future combined research on teaching interaction.