Dance Emotion Recognition Based on Laban Motion Analysis Using Convolutional Neural Network and Long Short-Term Memory

Abstract

Dance emotion recognition technology is of great significance for the digitalization, virtual performance, inheritance and protection of folk dance. Based on the mechanism that emotion expression in dance performance can be fully expressed through the strength and rhythm of dance movements, a novel dance emotion expression method is proposed to train hybrid deep learning neural network, to effectively identify the seven basic dance emotions of fear, anger, boredom, excitement, joy, relaxation and sadness. First, in order to fully express the emotions contained in the dance movements, this paper defines a dance emotion expression method through Laban Movement Analysis (LMA) method, which includes the characteristic parameters of the three aspects of body structure, spatial orientation and force effect, and converts the original dance movement data into three characteristic expression parameters to obtain dance emotion data. Then, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) hybrid neural network models are used to test and train dance emotion data. Finally, in order to verify the applicability of the CNN-LSTM model, decision tree, random forest, CNN and LSTM are established and compared for accuracy. The results show that it is feasible to identify dance emotion from the perspective of dance movement, and the CNN-LSTM model is of high accuracy.