A broad-deep fusion network-based fuzzy emotional intention inference model for teaching validity evaluation

Abstract

A broad-deep fusion network-based fuzzy emotional inference model with personal information (BDFEI) is proposed for emotional intention understanding in human-robot interaction. It aims to understand students' intentions and evaluate the teaching validity through quantitative analysis in the university teaching scene. Specifically, broad-deep fusion network is carried out for facial emotion and behavior recognition. Then, fuzzy inference with personal information is used to understand the intention. The V-A emotion space is used to quantify students' studying status with originality, by mapping the output of Softmax. Finally, teaching validity is obtained by fuzzy inference. According to the recognition results, the accuracy on bimodal face and body gesture database (FABO) of our proposal is 12.21%, 1.89% and 0.78% higher than those of the sparse autoencoder-based fuzzy deep neural network (FDNNSA), geodesic flow kernel integrated with residual network (ResNet-101+GFK), the hierarchical classification fusion strategy (HCFS), respectively. Through the experiment on the self-built database, the accuracy of intention understanding is 90.48%, and the effectiveness of teaching validity evaluation method is confirmed.